Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.

Faster-than-Nyquist (FTN) signaling is a classic signaling scheme for improved spectral efficiency compared to the conventional Nyquist signaling at the cost of increased complexity. In this paper, we investigate the FTN transmission with Nyquist receiver sampling (a.k.a. FTN-NR transmission), in order to simplify the receiver processing. Particularly, we derive a closed-form expression on the achievable rates of the FTN-NR transmission with arbitrary shaping pulses and Gaussian constellations. Our analysis reveals that this achievable rate relates closely to both the folded spectrum and folded squared spectrum of the pulse, which incorporate the folding effect of the signal spectrum due to the receiver sampling. Furthermore, we prove that the achievable rate of FTN-NR transmission is no better than that of Nyquist signaling despite the pulse shapes, when Gaussian constellation is applied. However, we then provide a numerical study on the rate with finite-alphabet constellations, and verify that FTN-NR transmission can outperform Nyquist signaling in terms of the achievable rate, especially when the modulation order is low. Our numerical results confirm our conclusions and report a noticeable achievable rate improvement of the FTN-NR transmission compared to Nyquist signaling under QPSK signaling.

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

The multiaccess coded caching (MACC) system, as formulated by Hachem {\it et al.}, consists of a central server with a library of $N$ files, connected to $K$ cache-less users via an error-free shared link, and $K$ cache nodes, each equipped with cache memory of size $M$ files. Each user can access $L$ neighboring cache nodes under a cyclic wrap-around topology. Most existing studies operate under the strong assumption that users can retrieve content from their connected cache nodes at no communication cost. In practice, each user retrieves content from its $L$ different connected cache nodes at varying costs. Additionally, the server also incurs certain costs to transmit the content to the users. In this paper, we focus on a cost-aware MACC system and aim to minimize the total system cost, which includes cache-access costs and broadcast costs. Firstly, we propose a novel coded caching framework based on superposition coding, where the MACC schemes of Cheng \textit{et al.} are layered. Then, a cost-aware optimization problem is derived that optimizes cache placement and minimizes system cost. By identifying a sparsity property of the optimal solution, we propose a structure-aware algorithm with reduced complexity. Simulation results demonstrate that our proposed scheme consistently outperforms the scheme of Cheng {\it et al.} in scenarios with heterogeneous retrieval costs.

We consider a $(G,L,K,M,N)$ cache-aided multiple-input multiple-output (MIMO) network, where a server equipped with $L$ antennas and a library of $N$ equal-size files communicates with $K$ users, each equipped with $G$ antennas and a cache of size $M$ files, over a wireless interference channel. Each user requests an arbitrary file from the library. The goal is to design coded caching schemes that simultaneously achieve the maximum sum degrees of freedom (sum-DoF) and low subpacketization. In this paper, we first introduce a unified combinatorial structure, termed the MIMO placement delivery array (MIMO-PDA), which characterizes uncoded placement and one-shot zero-forcing delivery. By analyzing the combinatorial properties of MIMO-PDAs, we derive a sum-DoF upper bound of $\min\{KG, Gt+G\lceil L/G \rceil\}$, where $t=KM/N$, which coincides with the optimal DoF characterization in prior work by Tehrani \emph{et al.}. Based on this upper bound, we present two novel constructions of MIMO-PDAs that achieve the maximum sum-DoF. The first construction achieves linear subpacketization under stringent parameter constraints, while the second achieves ordered exponential subpacketization under substantially milder constraints. Theoretical analysis and numerical comparisons demonstrate that the second construction exponentially reduces subpacketization compared to existing schemes while preserving the maximum sum-DoF.

We consider the $(K,L,M,N)$ multi-access coded caching system introduced by Hachem et al., which consists of a central server with $N$ files and $K$ cache nodes, each of memory size $M$, where each user can access $L$ cache nodes in a cyclic wrap-around fashion. At present, several existing schemes achieve competitive transmission performance, but their subpacketization levels grow exponentially with the number of users. In contrast, schemes with linear or polynomial subpacketization always incur higher transmission loads. We aim to design a multi-access coded caching scheme with linear subpacketization $F$ while maintaining low transmission load. Recently, Cheng et al. proposed a construction framework for coded caching schemes with linear subpacketization (i.e., $F=K$) called non-half-sum disjoint packing (NHSDP). Inspired by this structure, we introduce a novel combinatorial structure named cyclic multi-access non-half-sum disjoint packing (CMA-NHSDP) by extending NHSDP to MACC system. By constructing CMA-NHSDP, we obtain a new class of multi-access coded caching schemes. Theoretical and numerical analyses show that our scheme achieves lower transmission loads than some existing schemes with linear subpacketization. Moreover, the proposed schemes achieves lower transmission load compared to existing schemes with exponential subpacketization in some case.

This paper proposes a coding framework for capacity-region-achieving sparse regression (SR) codes over MIMO multiple-access channels (MIMO-MAC), where a single SR code is used for each user at the transmitter. With random semi-unitary dictionary matrices applied for encoding, multiple-access OAMP (MA-OAMP) enables reliable parallel interference cancellation (PIC) at the receiver. Theoretically, an optimal coding principle with the MA-OAMP receiver, which achieves the sum capacity and, in combination with time sharing, achieves the entire capacity region, is established as the guiding principle for designing capacity-region-achieving codes. Accordingly, a coding scheme for capacity-region-achieving SR codes is proposed via proper power allocation over the position-modulated signals.

Coded caching is recognized as an effective method for alleviating network congestion during peak periods by leveraging local caching and coded multicasting gains. The key challenge in designing coded caching schemes lies in simultaneously achieving low subpacketization and low transmission load. Most existing schemes require exponential or polynomial subpacketization levels, while some linear subpacketization schemes often result in excessive transmission load. Recently, Cheng et al. proposed a construction framework for linear coded caching schemes called Non-Half-Sum Disjoint Packing (NHSDP), where the subpacketization equals the number of users $K$. This paper introduces a novel combinatorial structure, termed the Non-Half-Sum Latin Rectangle (NHSLR), which extends the framework of linear coded caching schemes from $F=K$ (i.e., the construction via NHSDP) to a broader scenario with $F=\mathcal{O}(K)$. By constructing NHSLR, we have obtained a new class of coded caching schemes that achieves linearly scalable subpacketization, while further reducing the transmission load compared with the NHSDP scheme. Theoretical and numerical analyses demonstrate that the proposed schemes not only achieves lower transmission load than existing linear subpacketization schemes but also approaches the performance of certain exponential subpacketization schemes.

This paper investigates the fundamental limits and optimal signal distribution design for Integrated Sensing and Communication (ISAC) systems operating under strictly noncoherent conditions. Unlike conventional coherent frameworks that rely on perfect channel state information, we consider a block-fading MIMO channel where the channel realizations are unknown to both the transmitter and the receiver. We adopt a realization-wise perspective to characterize the noncoherent performance tradeoff across different signal-to-noise ratio (SNR) regimes. In the high-SNR regime, we derive a lower bound for the noncoherent mutual information and define a metric, termed sensing-induced rate loss, to quantify the communication penalty incurred by sensing-oriented beamforming. We then employ a projected gradient algorithm to optimize the spatial power allocation, balancing the conflict between the unitary space-time modulation-based structure for communication and the task-oriented spatial power allocation for sensing. Conversely, in the low-SNR regime, we perform a first-order asymptotic analysis of the ergodic minimum mean squared error (EMMSE). Our theoretical derivation reveals a fundamental synergy: the sensing-optimal strategy collapses to a rank-one transmission along the dominant eigenvector of the target response, which incurs no first-order communication loss in the low-SNR regime. This result demonstrates that the conflicting tradeoff observed at high SNR vanishes asymptotically at low SNR, enabling perfect alignment between sensing and communication objectives.

This paper studies the multi-access coded caching (MACC) problem with arbitrary user-cache access topology, which extends existing MACC models that rely on highly structured and combinatorially designed topologies. We consider a MACC system consisting of a single server, $Λ$ cache-nodes, and $K$ user-nodes. The server stores $N$ equal-size files, each cache-node has a storage capacity of $M$ files, and each user-node $k\in[K]$ can access an arbitrary subset of cache-nodes $\mathcal{A}_k\subseteq[Λ]$ and retrieve the cached content stored in cache-nodes $\mathcal{A}_k$. The objective is to design a universal framework for the MACC delivery problem. Decoding conflicts among the requested packets are captured by a conflict graph, and the design of the delivery is reduced to a graph coloring problem, where achieving a lower transmission load corresponds to coloring the graph using fewer colors. Under this formulation, the classical DSatur algorithm achieves a transmission load close to the index-coding (IC) converse bound, thereby providing a practical benchmark. However, its computational complexity becomes prohibitive for large-scale graphs. To overcome this limitation, we develop a learning-driven approach using graph neural networks (GNNs) that efficiently constructs coded multicast transmissions with performance close to the theoretical bounds and generalizes across different user-cache access topologies and numbers of users. In addition, we extend the IC converse bound to MACC systems with arbitrary access topology and propose a low-complexity greedy approximation that closely matches the IC converse bound. Numerical results demonstrate that the proposed approach achieves performance close to the DSatur algorithm and the IC converse bound, while significantly reducing computational complexity, making it well-suited for large-scale MACC systems.

We study joint communication and sensing in an OFDM system, involving a transmitter sending a message to a receiver while enabling radar sensing by generating back-scattered signals. The sensing task is ranging, modeled by on-grid recovery of target delays that remain fixed over the transmission block, and formulated as a multiple hypothesis testing problem. We establish the exact tradeoff between the achievable communication rate and the ranging error exponent, and show that the tradeoff relies only on the power allocation across subcarriers. We further identify scenarios where uniform power allocation is optimal and sub-optimal for the ranging task.

This paper considers a new secure gradient coding problem with uncoded groupwise keys, formalized as a (K, N, N_r, M, S) secure gradient coding model, where a user aims to compute the sum of the gradients from K datasets with the assistance of N distributed servers. We consider arbitrary heterogeneous data assignment, where each dataset is assigned to at least M servers. The user should recover the sum of gradients from the transmissions of any N_r servers. The security constraint guarantees that even if the user receives the transmitted messages from all servers, it cannot obtain any other information about the datasets except the sum of gradients. Compared to existing secure gradient coding works, we introduce a practical constraint on secret keys, namely uncoded groupwise keys, where the keys are mutually independent and each key is shared by precisely S servers. An achievable secure gradient coding scheme with uncoded groupwise keys is proposed, which is then proven to be optimal if S > M and to be order optimal within a factor of 2 otherwise.

Large-scale decentralized learning frameworks such as federated learning (FL), require both communication efficiency and strong data security, motivating the study of secure aggregation (SA). While information-theoretic SA is well understood in centralized and fully connected networks, its extension to decentralized networks with limited local connectivity remains largely unexplored. This paper introduces \emph{topological secure aggregation} (TSA), which studies one-shot, information-theoretically secure aggregation of neighboring users' inputs over arbitrary network topologies. We develop a unified linear design framework that characterizes TSA achievability through the spectral properties of the communication graph, specifically the kernel of a diagonally modulated adjacency matrix. For several representative classes of $d$-regular graphs including ring, prism and complete topologies, we establish the optimal communication and secret key rate region. In particular, to securely compute one symbol of the neighborhood sum, each user must (i) store at least one key symbol, (ii) broadcast at least one message symbol, and (iii) collectively, all users must hold at least $d$ i.i.d. key symbols. Notably, this total key requirement depends only on the \emph{neighborhood size} $d$, independent of the network size, revealing a fundamental limit of SA in decentralized networks with limited local connectivity.

Distributed linearly separable computation is a fundamental problem in large-scale distributed systems, requiring the computation of linearly separable functions over different datasets across distributed workers. This paper studies a heterogeneous distributed linearly separable computation problem, including one master and N distributed workers. The linearly separable task function involves Kc linear combinations of K messages, where each message is a function of one dataset. Distinguished from the existing homogeneous settings that assume each worker holds the same number of datasets, where the data assignment is carefully designed and controlled by the data center (e.g., the cyclic assignment), we consider a more general setting with arbitrary heterogeneous data assignment across workers, where `arbitrary' means that the data assignment is given in advance and `heterogeneous' means that the workers may hold different numbers of datasets. Our objective is to characterize the fundamental tradeoff between the computable dimension of the task function and the communication cost under arbitrary heterogeneous data assignment. Under the constraint of integer communication costs, for arbitrary heterogeneous data assignment, we propose a universal computing scheme and a universal converse bound by characterizing the structure of data assignment, where they coincide under some parameter regimes. We then extend the proposed computing scheme and converse bound to the case of fractional communication costs.

In the $(L,K,M,N)$ cache-aided multiple-input single-output (MISO) broadcast channel (BC) system, the server is equipped with $L$ antennas and communicates with $K$ single-antenna users through a wireless broadcast channel where the server has a library containing $N$ files, and each user is equipped with a cache of size $M$ files. Under the constraints of uncoded placement and one-shot linear delivery strategies, many schemes achieve the maximum sum Degree-of-Freedom (sum-DoF). However, for general parameters $L$, $M$, and $N$, their subpacketizations increase exponentially with the number of users. We aim to design a MISO coded caching scheme that achieves a large sum-DoF with low subpacketization $F$. An interesting combinatorial structure, called the multiple-antenna placement delivery array (MAPDA), can be used to generate MISO coded caching schemes under these two strategies; moreover, all existing schemes with these strategies can be represented by the corresponding MAPDAs. In this paper, we study the case with $F=K$ (i.e., $F$ grows linearly with $K$) by investigating MAPDAs. Specifically, based on the framework of Latin squares, we transform the design of MAPDA with $F=K$ into the construction of a combinatorial structure called the $L$-half-sum disjoint packing (HSDP). It is worth noting that a $1$-HSDP is exactly the concept of NHSDP, which is used to generate the shared-link coded caching scheme with $F=K$. By constructing $L$-HSDPs, we obtain a class of new schemes with $F=K$. Finally, theoretical and numerical analyses show that our $L$-HSDP schemes significantly reduce subpacketization compared to existing schemes with exponential subpacketization, while only slightly sacrificing sum-DoF, and achieve both a higher sum-DoF and lower subpacketization than the existing schemes with linear subpacketization.

The Fluid Antenna System (FAS) overcomes the spatial degree-of-freedom limitations of conventional static antenna arrays in wireless communications.This capability critically depends on acquiring full Channel State Information across all accessible ports. Existing studies focus exclusively on narrowband FAS, performing channel estimation solely in the spatial domain. This work proposes a channel estimation and spatial equalization framework for wideband FAS, revealing for the first time an inherent group-sparse structure in aperture-limited FAS channels. First, we establish a group-sparse recovery framework for space-frequency characteristics in FAS, formally characterizing leakage-induced sparsity degradation from limited aperture and bandwidth as a structured group-sparsity problem. By deriving dictionary-adapted group restricted isometry property, we prove tight recovery bounds for a convex ℓ1/ℓ2-mixed norm optimization formulation that preserves leakage-aware sparsity patterns. Second, we develop a descending correlation group orthogonal matching pursuit algorithm that systematically relaxes leakage constraints to reduce subcoherence. This approach enables FSC recovery with accelerated convergence and superior performance compared to conventional compressive sensing methods like OMP or GOMP. Third, we formulate spatial equalization as a mixed-integer linear programming problem, complement this with a greedy algorithm maintaining near-optimal performance. Simulation results demonstrate the proposed channel estimation algorithm effectively resolves energy misallocation and enables recovery of weak details, achieving superior recovery accuracy and convergence rate. The SE framework suppresses deep fading phenomena and largely reduces time consumption overhead while maintaining equivalent link reliability.

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. This paper focuses on the bistatic ISAC systems with separated multi-receiver and one sensor. Compared to a monostatic ISAC system, the main challenge in the bistatic setting is that the information messages are unknown to the sensor and therefore they are seen as interference, while the channel between the transmitters and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce two strategies, namely, blind interference alignment and topological interference management. Although well-known in the context of Gaussian interference channels, these strategies are novel in the context of bistatic ISAC. For the bistatic ISAC models with heterogeneous coherence time or with heterogeneous connectivity, the achieved ISAC tradeoff points in terms of communication and sensing degrees of freedom are characterized. In particular, we show that the new tradeoff outperforms the time-sharing between the sensing-only and the communication-only schemes. Simulation results demonstrate that the proposed schemes significantly improve the channel estimation error for the sensing task, compared to treating interference as noise at the sensor and successive interference cancellation.

This paper considers a joint scattering environment sensing and data recovery problem in an uplink integrated sensing and communication (ISAC) system. To facilitate joint scatterers localization and multi-user (MU) channel estimation, we introduce a three-dimensional (3D) location-domain sparse channel model to capture the joint sparsity of the MU channel (i.e., different user channels share partially overlapped scatterers). Then the joint problem is formulated as a bilinear structured sparse recovery problem with a dynamic position grid and imperfect parameters (such as time offset and user position errors). We propose an expectation maximization based turbo bilinear subspace variational Bayesian inference (EM-Turbo-BiSVBI) algorithm to solve the problem effectively, where the E-step performs Bayesian estimation of the the location-domain sparse MU channel by exploiting the joint sparsity, and the M-step refines the dynamic position grid and learns the imperfect factors via gradient update. Two methods are introduced to greatly reduce the complexity with almost no sacrifice on the performance and convergence speed: 1) a subspace constrained bilinear variational Bayesian inference (VBI) method is proposed to avoid any high-dimensional matrix inverse; 2) the multiple signal classification (MUSIC) and subspace constrained VBI methods are combined to obtain a coarse estimation result to reduce the search range. Simulations verify the advantages of the proposed scheme over baseline schemes.

This paper focuses on the design and performance analysis of faster-than-Nyquist (FTN) signaling employing enhanced 5G low-density parity-check (LDPC) codes, oriented toward the requirements of future 6G systems. We propose the extrinsic information transfer (EXIT) chart analysis for the LDPC-coded FTN system based on the Ungerboeck observation model, where the input-output mutual information function of the detector is approximated using least squares fitting. With the proposed EXIT chart analysis, we explore the thresholds and decoding performance of different LDPC codes (regular codes, irregular codes and protograph codes) in both Nyquist and FTN systems, revealing two important observational findings for FTN signaling: 1) Unlike Nyquist systems, where certain 5G New Radio (NR)-like information puncturing can enhance the decoding threshold and performance, we observe that in the FTN setting considered in this paper such puncturing leads to performance degradation; 2) Unlike Nyquist systems, the paritycheck matrix of LDPC codes optimized for FTN signaling tends to be relatively sparser within comparable ensembles, due to the intentionally introduced inter-symbol interference (ISI). Based on these findings, we develop tailored LDPC codes for FTN signaling by applying the masking operation to the base matrix of the standard 5G LDPC codes, aiming to achieve a lower decoding threshold and thereby better decoding performance. Moreover, the raptor-like structure and rate compatibility are preserved in the proposed LDPC codes, and the encoder and decoder are reused with only minor modifications. Numerical results show that: 1) All simulation results align with the decoding thresholds obtained by the proposed EXIT chart analysis, confirming the effectiveness of the analysis; 2) For the FTN system, the tailored LDPC codes outperform standard 5G LDPC codes, achieving over 0.4 dB coding gain and approaching (slightly exceeding) the constrained Nyquist capacity; 3) Under the same spectral efficiency, FTN with tailored LDPC codes performs better than standard 5G LDPC codes with Nyquist signaling, demonstrating a coding gain of up to 0.6 dB; 4) The proposed LDPC codes with the FTN signaling achieve better performance compared to existing high-performance codes specifically designed for FTN signaling.

In sixth-generation and beyond, space-air-ground integrated networks (SAGINs) extend network connectivity to space, thereby enabling broader service coverage. This paper proposes a topology-aware SAGIN framework to address the integrated sensing, communication, and wireless power transfer (ISCPT) problem, leveraging the distinctive visibility of satellite-terrestrial and satellite-satellite users as well as their constructing in-between channel strengths. By modeling the topology of the SAGIN as a bipartite graph, we formulate the ISCPT problem as a multi-objective joint optimization problem with specified topological structures to reflect connection relationships of satellite-terrestrial and satellite-satellite users. The ISCPT problem is then reformulated and carefully decomposed as several mixed-integer linear programs (MILPs) by leveraging the network topology to individually optimize sensing, communication, and power transfer. To reduce the computational complexity of the proposed method, a greedy algorithm deal with generalized multi-assignment problem (GMAP) is developed. Simulation results demonstrate superior performance in communication and sensing, with a tolerable trade-off in wireless power transfer.

In decentralized federated learning (FL), multiple clients collaboratively learn a shared machine learning (ML) model by leveraging their privately held datasets distributed across the network, through interactive exchange of intermediate model updates. To ensure data security, cryptographic techniques are commonly employed to protect model updates during aggregation. Despite growing interest in secure aggregation, existing works predominantly focus on protocol design and computational guarantees, with limited understanding of the fundamental information-theoretic limits of such systems. Moreover, optimal bounds on communication and key usage remain unknown in decentralized settings, where no central aggregator is available. Motivated by these gaps, we study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective. Specifically, we consider a network ofKfully-connected users, each holding a private input—an abstraction of local training data—who aim to securely compute the sum of all inputs. The security constraint requires that no user learns anything beyond the input sum, even when colluding with up toTother users. We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA. In particular, we show that to securely compute one symbol of the desired input sum, each user must (i) transmit at least one symbol to others, (ii) hold at least one symbol of secret key, and (iii) all users must collectively hold no fewer thanK−1independent key symbols. Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols in decentralized learning.

This paper studies the master-worker distributed linearly separable computation problem, where the considered computation task, referred to as linearly separable function, is a generic linear map. This model includes cooperative distributed gradient coding, real-time rendering, linear transforms, etc. as special cases. The computation task on K datasets can be expressed as Kclinear combinations of K messages, where each message is the output of an individual function on one dataset. In this distributed computing model, the K datasets are assigned to N workers for computation. Due to the possible presence of stragglers, it is required that the master can obtain the desired computation task from the answers of any Nrout of N workers. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of codewords that should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. A common way to assign the datasets to the workers is “cyclic assignment”. This has been considered in several theoretical works, as well as gradient coding, etc. Motivated by its theoretical and practical relevance, in this paper we focus on the cyclic assignment and solve the problem by determining the optimal computation/communication cost tradeoff when N = K and order optimal within a factor of 2 otherwise. In particular, this paper proposes a new computing scheme with the cyclic assignment based on the concept of interference alignment, by treating each message which cannot be computed by a worker as an interference from this worker. The decodability of our scheme is proved for the cases Kc[K/N (Nr− m + 1) : K] and N = Nrwith m+u−1 dividing N (where u = [ KcN K ]), and is further numerically verified for N ≤ 60. Beyond the appealing order-optimality result, we also show that the proposed scheme achieves significant gains over the current state of the art in practice. Experimental results over Tencent Cloud show the reduction of whole distributed computing process time of our scheme is up to 72.8% compared to the benchmark scheme which treats the computation on Kclinear combinations as Kcindividual computations.

Coded caching is a promising technique for effectively reducing peak traffic by using local caches and the multicast gains generated by these local caches. Coded caching schemes have been widely investigated, following the seminal work of Maddah-Ali and Niesen. Explicit coding constructions have been proposed for a variety of network topologies with information-theoretically optimal or near-optimal transmission loadR. An important parameter in these constructions is the subpacketizationF, i.e., the number of subpackets that each content file needs to be divided into. In particular, the original scheme of Maddah-Ali and Niesen as well as several other variants requireFto grow exponentially with the number of usersK. In practice, files have finite size and too largeFyields impractically small subpackets. Therefore, it is important to design coded caching schemes withFandRas small as possible. At present, the few known schemes with subpacketization linear inKachieve large load. In this paper, we consider the linear scaling regimeF = O(K)and design schemes with a lower transmission loadR. Specifically, we first introduce a new combinatorial structure called non-half-sum disjoint packing (NHSDP) which can be used to generate a coded caching scheme withK = O(F). A class of new schemes is then obtained by constructing NHSDP. Theoretical analysis and numerical results demonstrate that (i) in comparison to existing schemes with linear subpacketization, the proposed scheme achieves a lower load; (ii) the proposed scheme also attains a lower load than some existing schemes with polynomial subpacketization in some cases; and (iii) the proposed scheme achieves load values comparable to those of existing schemes with exponential subpacketization in some cases. Furthermore, the newly introduced concept of NHSDP is closely related to classical combinatorial structures, including cyclic difference packings (CDP), non-three-term arithmetic progressions (NTAP), and perfect hash families (PHF). These relationships underscore the significance of NHSDP as a combinatorial structure of independent interest in the field of combinatorial design, even beyond its application to coded caching.

wide range of problems in communications and information theory can be cast as sparse recovery tasks, where the objective is to identify the active codewords in a linear superposition with random channel coefficients and additive noise. In multi-antenna systems, it is commonly assumed that the channel coefficients and noise are identically distributed across receive antennas, which implies that the activity pattern is shared among all antennas. By forming outer products of the received signals, such problems can be transformed into structured covariance estimation problems in which the unknown parameters are the variances of the channel coefficients, commonly referred to as large-scale fading coefficients. Characterizing the interplay among the number of receive antennas, pilot symbols, active users, total users, and the resulting error probability is therefore of fundamental importance for the design of efficient random access protocols. In this work, a general class of covariance estimators, defined by a real-valued functiongand a prescribed set of admissible covariance matrices, is studied. Given a possibly perturbed observation of an underlying covariance matrix, the estimator is defined as the minimizer of a sum ofgapplied to the eigenvalues of a suitably normalized matrix, subject to the constraint that the estimate lies in the admissible set. Under mild regularity conditions on the functiongand the constraint set, robustness of this class of estimators is established, in the sense that the estimation error can be made arbitrarily small as the perturbation vanishes. These general results are applied to activity detection in random access systems with multiple receive antennas. Recovery via nonnegative least squares and via a relaxed maximum-likelihood estimator is considered, and it is shown that, under suitable assumptions on the channel and noise distributions, the relaxed maximum-likelihood estimator belongs to the proposed class of covariance estimators. Finally, pilot codebooks satisfying a signed kernel condition are introduced and it is shown that, with such codebooks, reliable recovery of the large-scale fading coefficients is possible when the number of receive antennas is sufficiently large and the number of active users satisfiesS≤ [1/2M2] − 1, whereMdenotes the number of pilot symbols per user. For the finite-antenna regime, refined recovery conditions that explicitly capture the dependence on the number of receive antennas are derived.

Secure aggregation is concerned with the task of securely computing the sum of the inputs from multiple users by an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed machine learning paradigms such as federated learning (FL) where numerous clients, each holding a proprietary dataset, periodically upload their locally trained models (abstracted as inputs) to a parameter server. The server then generates an aggregate model, typically through averaging, which is shared back with clients as the starting point for a new round of local training. To protect data security, secure aggregation protocols leverage cryptographic techniques to ensure the server gains no additional information beyond the input sum, even if it colludes with a subset of users. While the simple star client-server architecture provides insights into the fundamental utility-security trade-off in secure aggregation, it falls short of capturing the impact of network topology in practical systems. Motivated by hierarchical federated learning, we investigate the secure aggregation problem in a three-layer hierarchical network, where clustered users communicate with an aggregation server via an intermediate layer of relays. In addition to conventional server security which ensures the server learns only the input sum, we also impose relay security, requiring that the relays remain oblivious to users’ inputs. For such a hierarchical secure aggregation (HSA) problem, we characterize the optimal multifaceted trade-off between communication efficiency (measured by user-to-relay and relay-to-server communication rates) and key generation efficiency (including individual and source key rates). A core contribution of this work is the derivation of the optimal source key rate as a function of the number of relays, cluster size, and collusion level. We propose an optimal communication scheme alongside a key generation scheme utilizing a novel matrix structure called extended Vandermonde matrix that guarantees both input sum recovery and security. Moreover, we derive a tight information-theoretic converse proof to establish the optimal rate region for the HSA problem.

This paper presents discrete codebook synthesis methods for self-interference (SI) suppression in a mmWave device, designed to support full-duplex (FD) integrated sensing and communication (ISAC). We formulate a signal-to-interference-and-noise ratio (SINR) maximization problem that optimizes the receiver (RX) and transmitter (TX) codewords, aimed at suppressing the near-field SI signal while maintaining the beamforming gain in the far-field sensing directions. The formulation considers the practical constraints of discrete RX and TX codebooks with quantized phase settings, as well as a TX beamforming gain requirement in the specified communication direction. Under an alternating optimization framework, the RX and TX codewords are iteratively optimized, with one fixed while the other is optimized. When the TX codeword is fixed, the RX codeword optimization problem is formulated as an integer quadratic fractional programming (IQFP) problem. Using Dinkelbach's algorithm, we transform it into a sequence of subproblems in which the numerator and denominator are decoupled, and solve these subproblems efficiently by the spherical search (SS) method. This approach is referred to as FP-SS. When the RX codeword is fixed, the TX codeword optimization is similarly an IQFP problem, but an additional TX beamforming constraint for communication must be considered; it is solved through Dinkelbach's transformation followed by the constrained spherical search (CSS), which we refer to as FP-CSS. We prove that both methods find the optimal solutions to their respective codebook optimization problems. Simulations show that FP-SS and FP-CSS achieve the same SI suppression performance as their corresponding exhaustive search (ES) methods, confirming their optimality, but at much lower complexity. Integrating them into the alternating optimization framework yields even better SI suppression performance.

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (TBS), multiple receive base-stations (RBSs) connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the echo signals at all the RBSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase framework. In Phase I, each RBS recovers an individual image of all the targets from its own view, which is obtained via utilizing its received signals’ sample covariance matrix to detect the grids with non-zero effective scattering intensity in the region of interest. Moreover, the shape of each grid is adjusted to conform to target geometries. In Phase II, the CPU fuses the individual images of all the RBSs to construct a higher-quality image of all the targets. To this end, we first design an edge-preserving natural neighbor interpolation (EP-NNI) method and then formulate an optimization problem to fuse the interpolated results. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.

In this paper, we investigate delay-Doppler (DD) sensing in a communication-centric integrated sensing and communication (ISAC) framework based on Zak transform-based orthogonal time frequency space (Zak-OTFS) modulation. Specifically, we consider target sensing with communication waveforms and propose a novel backpropagation (BP) algorithm for multi-target DD parameter estimation. We formulate the radar sensing task as a maximum likelihood parameter estimation problem, which is highly non-convex. By exploiting the structural analogy between parameter estimation and neural network training, the BP algorithm treats the DD parameters as tunable network weights and efficiently computes their gradients via the chain rule, enabling accurate and parallelized estimation. To facilitate the algorithm implementation, a successive interference cancellation method based on DD domain twisted convolution is developed to obtain coarse DD estimates. Furthermore, a constant false alarm rate based dynamic merging strategy is introduced to adaptively estimate the number of targets during the BP process. Comprehensive theoretical analyses are conducted, including the derivation of the Cramér–Rao bound (CRB) for Zak-OTFS systems and performance evaluation under various challenging sensing scenarios. Simulation results demonstrate that the proposed algorithm achieves high estimation accuracy and validates the theoretical analysis.

Several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measurement campaigns, inaccurate statistical models, or computationally expensive ray-tracing simulations with machine learning models that deliver quick and accurate predictions. We focus on predicting path loss radio maps using convolutional neural networks, leveraging aerial images alone or in combination with supplementary height information. Notably, our approach does not rely on explicit classification of environmental objects, which is often unavailable for most locations worldwide. While the prediction of radio maps using complete 3D environmental data is well-studied, the use of only aerial images remains under-explored. We address this gap by showing that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task. Additionally, we introduce a new model dubbed UNetDCN that achieves on par or better performance compared to the state-of-the-art with reduced complexity. The trained models are differentiable, and therefore they can be incorporated in various network optimization algorithms. While an extensive discussion is beyond this paper’s scope, we demonstrate this through an example optimizing the directivity of base stations in cellular networks via backpropagation to enhance coverage.

In this paper we study fully asynchronous random access (RA) multiple antenna receiver in a Rayleigh block-fading AWGN channel with pure path delays. Although our approach can be used to detect users in a grant-free random access system, where each user sporadically and without waiting for a permission from a base station (BS) transmits short messages, we in particular consider the pilot-based unsourced random access (U-RA), where those messages are from a common codebook. The first and arguably the most important task of the pilotbased U-RA receiver is to detect the list of transmitted pilots. Due to the propagation through the considered channel, those pilots are received as a superposition at the BS with delays, due to the lack of perfect timing synchronization. We show that the output of the chip matched filter at the BS receiver can be seen as the superposition of two zero-padded versions of each transmitted pilot sequence. We include this observation in the compressed sensing (CS) formulation of the activity detection (AD) problem, and solve it using the multiple measurement vectors approximate message passing (MMV-AMP) algorithm and a dedicated parametrized 2-level hierarchical sparsity (P2-LHS) denoiser. Our numerical experiments show that the proposed scheme can accurately detect U-RA messages and shows excellent robustness to timing asynchronism. The proposed denoiser vastly outperforms the standard Bernoulli-Gaussian (BG) denoiser for a large range of signal-to-noise ratio (SNR) values. Furthermore, using outage rate analysis, we investigate the performance of the asynchronous U-RA receiver using a Gaussian codebook and minimum distance decoding in the data phase. Our results show that a significant gain in throughput can be achieved when users adopt rate control (RC).

Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensional channel state information entails large overhead, which drastically increases with the number of network devices. In this paper, we propose an accelerated method to achieve network-wide beam alignment in an efficient way. To this aim, we consider compressed sensing (CS) estimation enabled by a novel design of pilot sequences. Our designed pilots have constant envelope to alleviate hardware requirements at the transmitters, while they exhibit a “comb-like” spectrum that flexibly allocates energy only on certain frequencies. This design enables multiple devices to transmit their pilots concurrently while remaining orthogonal in frequency, achieving simultaneous alignment of multiple devices. Furthermore, we present a sequential partitioning strategy into transmitters and receivers that results in logarithmic scaling of the overhead with the number of devices, as opposed to the conventional linear scaling. Finally, we show via accurate modeling of the indoor propagation environment and ray tracing simulations that the resulting sub-THz channels after successful beamforming are approximately frequency flat, therefore suitable for efficient single carrier transmission without equalization. We compare our results against an ”802.11ad inspired” baseline and show that our method is capable to greatly reduce the number of pilots required to achieve network-wide alignment.

Smaller cells have been the most important contributor to throughput improvement since the birth of cellular networks. They are likely to evolve further in the shift to cell-free massive MIMO (CF mMIMO), where multiple closely placed access points (APs) collaborate to serve users. This scheme is particularly suitable for millimeter wave (mmWave) communication, which enables very high data rates with its large bandwidth, but encounters severe challenges of high path loss and blockage. The CF mMIMO network is a good countermeasure to these two challenges by utilizing overlapping signals from different APs and macro-diversity. In this work, we demonstrate that mmWave CF mMIMO network optimization is largely an AP-user assignment problem. To solve this large-scale, nondifferentiable problem, we propose an unsupervised machine learning (ML) approach, which looks for the optimal solution autonomously without labels. A customized graph neural network architecture tailored to the problem properties is proposed, which enables distributed optimization without a central unit, allows for a varying number of users, and hierarchical permutation-equivariance of APs and users. A teacher-student model is applied to prune the graph, where the teacher model uses a fully connected graph for maximum performance, and the student model uses a pruned graph to reproduce the teacher's behavior with less communication in fronthaul. Moreover, a special training method is designed, which relaxes the combinatorial problem to a continuous one. In this way, we can apply gradient-based neural network training. An entropy-inspired penalty is introduced to make the relaxed problem equivalent to the original one. The analytical augmented Lagrangian method is combined with ML for the constrained optimization. Simulation results show that the proposed approach outperforms baselines in both performance and computation time. In addition, with a properly pruned graph, the proposed approach performs inference in a distributed manner with sparse message passing between APs, realizing a low signaling overhead in fronthaul, and a performance close to the fully connected graph.

This paper investigates the use of the weighted centroid localization (WCL) method for user localization in cell-free massive MIMO (CF-mMIMO) networks. This low-complexity algorithm operates solely on received power measurements from pilot transmissions, requiring no prior channel information or estimation. It enables coarse localization, which is valuable for various network management tasks while incurring minimal cost and overhead. Using a stochastic geometry-based analytical framework, we derive approximations for the localization mean-square error, providing insights into the performance and limitations of WCL. We also present an exact expression for the localization error cumulative distribution function, along with alternative approximations based on moment matching. The predictive capability of the proposed analytical framework is validated through extensive simulations that incorporate key practical impairments, such as multipath propagation, spatially correlated shadowing, and pilot contamination, that are analytically intractable. These results confirm the practical utility of our analysis in supporting the design of CF-mMIMO networks to meet specific localization performance targets.

This work considers a spatially non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationarity, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and develop a novel dynamic alternating maximum a posteriori (DA-MAP) method to solve the problem. DA-MAP contains four core modules: channel estimation module, VR detection module, grid update module, and temporal processing module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computationally intensive matrix inverse in each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulation results demonstrate that the proposed method achieves significant improvements in channel tracking performance with low computational overhead.

Metamaterial antennas are appealing for next-generation wireless networks due to their simplified hardware and much-reduced size, power, and cost. This paper investigates the holographic multiple-input multiple-output (HMIMO)-aided multi-cell systems with practical per-radio frequency (RF) chain power constraints. With multiple antennas at both base stations (BSs) and users, we design the baseband digital precoder and the tuning response of HMIMO metamaterial elements to maximize the weighted sum user rate. Specifically, under the framework of block coordinate descent (BCD) and weighted minimum mean square error (WMMSE) techniques, we derive the low-complexity closed-form solution for baseband precoder without requiring bisection search and matrix inversion. Then, for the design of HMIMO metamaterial elements under binary tuning constraints, we first propose a low-complexity suboptimal algorithm with closed-form solutions by exploiting the hidden convexity (HC) in the quadratic problem and then further propose an accelerated sphere decoding (SD)-based algorithm which yields global optimal solution in the iteration. For HMIMO metamaterial element design under the Lorentzian-constrained phase model, we propose a maximization-minorization (MM) algorithm with closed-form solutions at each iteration step. Furthermore, in a simplified multiple-input single-output (MISO) scenario, we derive the scaling law of downlink single-to-noise (SNR) for HMIMO with binary and Lorentzian tuning constraints and theoretically compare it with conventional fully digital/hybrid arrays. Simulation results demonstrate the effectiveness of our algorithms compared to benchmarks and the benefits of HMIMO compared to conventional arrays.

This paper investigates an integrated sensing and communication (ISAC) network operating over millimeter-wave and sub-Terahertz bands, where a base station serves downlink communication users (CUs), while simultaneously sensing targets. First, we propose a novel hybrid beamforming structure that reduces power consumption in high-frequency bands by using a low number of phase shifters for analog beamforming and enhances spatial diversity in baseband beamforming through improper Gaussian signaling (IGS), addressing the limitations of having only a few radio frequency chains by boosting the number of supported data streams. Together, these techniques establish a new phased-MIMO radar structure and an energy-efficient signaling strategy designed for joint sensing and communication. Second, we formulate a new beampattern-optimization objective that enables computationally efficient algorithms, which iteratively update the hybrid beamformers through closed-form expressions. This design ensures tight mainlobe concentration for sensing while simultaneously serving multiple CUs. A new soft-min function, paired with a closed-form algorithm, secures both strong worst-rate and sum-rate performance. By unifying sensing and communication objectives, the proposed framework offers a well-balanced trade-off between high CU rates and high-quality sensing beampatterns, while maintaining computational complexity scalable. Simulation results validate the practicality of the proposed approach.

This paper investigates an integrated sensing and communication (ISAC) system employing delay-Doppler (DD) signaling. The sensing performance of both random and deterministic signaling schemes is evaluated based on the expected squared ambiguity function (AF), for which closed-form expressions are derived by leveraging the Zak transform-based orthogonal time-frequency space (Zak-OTFS) modulation framework. Our analysis highlights a key difference between the two signaling types: DD domain ISAC (DD-ISAC) with deterministic signaling yields a roughly periodic AF with prominent peaks and low sidelobes between adjacent peaks, whereas DD-ISAC with random signaling using a Quadrature Phase-Shift Keying (QPSK) constellation exhibits low sidelobe values periodically without prominent peaks. Furthermore, we demonstrate that DD-ISAC enables a flexible trade-off between delay and Doppler sidelobe levels by adjusting the number of delay and Doppler bins. The analytical findings are explicitly validated through numerical simulations.

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity ℓ1-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) to WiFi to support concurrent transmissions to a larger number of users. As bandwidth continues to grow, WiFi channels exhibit increased frequency selectivity, which poses new challenges for MU-MIMO user selection: the optimal user set varies across frequency and is interleaved over subbands (called resource units, or RUs). This frequency selectivity, coupled with the complex subband allocation pattern, renders conventional narrowband user selection algorithms inefficient for 802.11ax. In this paper, we propose ProxySelect, a scalable and frequency selectivity-aware user scheduling algorithm for joint OFDMA and MU-MIMO usage in 802.11ax under zero-forcing beamforming (ZFBF). The scheduling task is formulated as an integer linear program (ILP) with binary variables indicating user (group)-RU associations, and linear constraints ensuring standard compatibility. To reduce complexity, we introduce a novel proxy rate–a function of individual channel strengths and their correlations–that approximates the ZFBF rate without requiring cubic-complexity matrix inversion. Additionally, we develop a sampling-based candidate group generation scheme that selects up to T near-orthogonal user groups for each RU, thereby bounding the ILP size and ensuring scalability. Simulations using realistic ray-tracing-based channel models show that ProxySelect achieves near-optimal rate performance with significantly lower complexity.

In this paper, we perform a comparative study of common wireless communication waveforms, namely the single carrier (SC), orthogonal frequency-division multiplexing (OFDM), and orthogonal time-frequency-space (OTFS) modulation in a millimeter wave (mmWave) downlink multi-connectivity scenario, where multiple access points (APs) jointly serve a given user under imperfect time and frequency synchronization errors. For a fair comparison, all the three waveforms are evaluated using variants of common frequency domain equalization (FDE). To this end, a novel cross domain iterative detection for OTFS is proposed. The performance of the different waveforms is evaluated numerically in terms of pragmatic capacity. The numerical results show that OTFS significantly outperforms SC and OFDM at cost of reasonably increased complexity, because of the low cyclic-prefix (CP) overhead and the effectiveness of the proposed detection.

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. The performance limits of a system are crucial for guiding its design; however, the performance limits of ISAC systems remain an open question. This paper focuses on the bistatic ISAC systems with dispersed multi-receivers and one sensor. Compared to the monostatic ISAC systems, the main challenge is that that the communication messages are unknown to the sensor and thus become its interference, while the channel information between the transmitters and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce the blind interference alignment strategy for various bistatic ISAC settings, including interference channels, MU-MISO channels, and MU-MIMO channels. Under each of such system, the achieved ISAC tradeoff points by the proposed schemes in terms of communication and sensing degrees of freedom are characterized, which outperforms the time-sharing between the two extreme sensing-optimal and communication-optimal points. Simulation results also demonstrate that the proposed schemes significantly improve on the ISAC performance compared to treating interference as noise at the sensor.

This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of -1, and with the TX/RX densities to the power of$-\gamma / 2$, where$\gamma$is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.

In this work, we study the transmit sequence design for integrated sensing and communications (ISAC) using random single-carrier communication signals. Particularly, we focus on the sensing-centric ISAC, where a family of communication codewords is optimized to yield a good sensing performance. To this end, we formulate the problem of finding the optimal communication codewords by minimizing the integrated sidelobe of the ambiguity function under the transmit power constraint. Specifically, two optimization methods are developed to solve such a problem, whose suitability with different communication shaping pulses is also highlighted. We unveil that the considered problem has non-unique optimum that can be exploited to obtain a family of communication codewords with optimized sensing performance. Furthermore, the communication performance of the derived codewords is evaluated based on both the Euclidean distance and the pairwise error probability (PEP) over multipath fading channels. Our numerical results confirm the superiority of the optimized codewords and the effectiveness of the proposed optimization methods.

Coded caching leverages the differences in user cache memories to achieve gains that scale with the total cache size, alleviating network congestion due to high-quality content requests. Additionally, distributing transmitters over a wide area can mitigate the adverse effects of path loss. In this work, we consider a partially connected network where the channel between distributed transmitters (helpers) and users is modeled as a distributed multiple-input-multiple-output (MIMO) Gaussian broadcast channel. We propose a novel delivery scheme consisting of two phases: partitioning and transmission. In the partitioning phase, users with identical cache profiles are partitioned into the minimum number of sets, such that users within each set can successfully decode their desired message from a joint transmission enabled by MIMO precoding. To optimally partition the users, we employ the branch and bound method. In the transmission phase, each partition is treated as a single entity, and codewords are multicast to partitions with distinct cache profiles. The proposed delivery scheme is applicable to any partially connected network, and while the partitioning is optimal, the overall delivery scheme, including transmission, is heuristic. Interestingly, simulation results show that its performance closely approximates that of the fully connected optimal solution.

Motivated by the recent interest in approximate message passing (AMP) for matrix-valued linear observations with superposition of multiple statistically asymmetric signal sources, we introduce a multi-source AMP framework in which the dictionary matrices associated with each signal source are drawn from a random semi-unitary ensemble (rather than the standard Gaussian matrix ensemble.) While a similar model has been explored by Vehkaperä, Kabashima, and Chatterjee (2016) using the replica method, here we present an AMP algorithm and provide a high-dimensional yet finite-sample analysis of its dynamics. As a proof of concept, we show the effectiveness of the proposed approach on the problem of message detection in an unsourced random access scenario in wireless communication.

Coded caching is a promising technique to effectively reduce peak traffic by using local caches and the multicast gains generated by these local caches. We aim to design a coded caching scheme to minimize subpacketization$F$and transmission load$R$, since these two metrics are key measures of scheme implementation complexity and transmission efficiency, respectively. In this paper, we focus on studying linear subpacketization coded caching schemes with low transmission load. We first introduce a new combinatorial structure, called non-half-sum disjoint packing (NHSDP), which can be used to construct coded caching schemes where the number of users is equal to the subpacketization, i.e.$K=F$. Then by constructing NHSDPs, we obtain a new class of coded caching schemes which achieve lower load compared to the existing schemes with linear subpacketization and even some of the existing schemes with polynomial subpacketization. Moreover, the novel concept of NHSDPs is closely related to the classical combinatorial structures, including cyclic difference packing, non-three-term arithmetic progressions, and perfect hash family.

In this paper, we study gradient coding in a hierarchical setting, where there are intermediate nodes between the server and the workers. This structure reduces the bandwidth requirements at the server, which is a significant bottleneck in conventional gradient coding systems. In this paper, the intermediate nodes, referred to as relays, process the data received from workers and send the results to the server for the final gradient computation. Our main contribution is deriving the optimal communication-computation trade-off by designing a linear coding scheme inspired by coded computing techniques, considering straggling and adversarial nodes among both relays and workers. The processing of the data in the relays makes it possible to achieve both the relay-to-server and the worker-to-relay communication loads simultaneously optimal with regard to the computation load.

In the problem of conditional disclosure of secrets (CDS), two parties, Alice and Bob, each has an input and shares a common secret. Their goal is to reveal the secret to a third party, Carol, as efficiently as possible, only if the inputs of Alice and Bob satisfy a certain functional relation$f$. To prevent leakage of the secret to Carol when the input combination is unqualified, both Alice and Bob introduce noise. This work aims to determine the noise capacity, defined as the maximum number of secret bits that can be securely revealed to Carol, normalized by the total number of independent noise bits held jointly by Alice and Bob. Our contributions are twofold. First, we establish the necessary and sufficient conditions under which the CDS noise capacity attains its maximum value of 1. Second, in addition to the above best-case scenarios, we derive an upper bound on the linear noise capacity for any CDS instance. In particular, this upper bound is equal to$(\rho-1)(d-1) /(\rho d-1)$, where$\rho$is the covering parameter of the graph representation of$f$, and$d$is the number of unqualified edges in residing unqualified path.

Spectrum sharing in Device-to-Device (D2D) communications with power control and link scheduling is a challenging non-convex combinatorial optimization problem. The state-of-the-art model-based iterative algorithms such as FPLinQ produce optimum-achieving solutions, whilst deep learning-based approaches have been proposed recently to approximate FPLinQ with reduced computational complexity. However, due to the highly non-convex nature of the optimization problem, FPLinQ exhibits certain deficiencies in the highly interference-limited networks, as it may be trapped within certain local sub-optimal solutions that may be far from the global optimum. To address these issues, we propose to unfold FPLinQ, with certain parameters inside the iterative procedure adjusted by a graph reinforcement learning (GRL) method, and end up with a novel hybrid model/data-driven approach, termed UFPLinQ. Not only does UFPLinQ inherit the advantages of FPLinQ and GRL with respect to local optimality, explainability, scalability, and generalizability, but it also provides excellent solutions in interference-limited networks where FPLinQ fails. By numerical evaluations, UFPLinQ outperforms existing learning-based power control mechanisms, with substantially reduced training samples and iterations, and more interestingly remedies the potential deficiencies of FPLinQ in highly interference-limited networks.

This paper studies the ergodic achievable rates of a primitive Gaussian diamond channel with Rayleigh fading. The system is modeled as a two-hop relay channel where a single user communicates with a central processor (CP) through two relays. These relays are agnostic to the user's codebooks and are considered “primitive” because the fronthaul links are error-free but have limited capacity. In this setup, the channel state information (CSI) is assumed to be available only at the relays and not at the CP. Despite the simplicity of this configuration, deriving an accurate characterization of the ergodic capacity is surprisingly challenging. To address this, we first establish an analytical rate upper bound, assuming that the relays can cooperate and that the CP has access to the CSI as well. In order to obtain lower bounds, we resort to specific analytically/numerically tractable achievability strategies. When designing such strategies, we need to take into account that the CP has no CSI and that each relay has only statistical knowledge of the CSI other relay. Under these constraints, we propose two achievable schemes employing different estimation and compression methods at relays. Simulation results show that these schemes achieve performance close to the derived upper bound over a wide range of system parameters.

This paper considers a multi-message secure aggregation with demand privacy problem, in which a server aims to compute$K_{c} \geq 1$linear combinations of local inputs from$K$distributed and non-colluding users. The problem addresses two tasks: (1) security, ensuring that the server can only obtain the desired linear combinations without any else information about the users' inputs, and (2) privacy, preventing users from learning about the server's computation task. In addition, the effect of user dropouts is considered, where at most$K-U$users can drop out and the identity of these users cannot be predicted in advance. We propose two schemes for$\mathrm{K}_{\mathrm{c}}=1$and$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$, respectively. For$\mathrm{K}_{\mathrm{c}}=1$, we introduce multiplicative encryption of the server's demand using a random variable, where users share coded keys offline and transmit masked models in the first round, followed by aggregated coded keys in the second round for task recovery. For$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$, we use robust symmetric private computation to recover linear combinations of keys in the second round. The objective is to minimize the number of symbols sent by each user during the two rounds. Our proposed schemes have achieved the optimal rate region when$\mathrm{K}_{\mathrm{c}}=1$and the order optimal rate (within 2) when$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$.

This paper focuses on the analysis and design of improved 5G low-density parity-check (LDPC) codes for faster-than-Nyquist (FTN) signaling. We first propose the protograph-based extrinsic information transfer (PEXIT) chart analysis for the LDPC-coded FTN system using the sum-product algorithm (SPA) based on the Ungerboeck observation model, where the distribution of the output mutual information from the detector is approximately derived using least squares fitting. With the proposed PEXIT chart analysis, we then design the improved LDPC codes for the coded FTN signaling aiming to achieve a lower decoding threshold and thereby better error performance. The proposed codes are optimized based on the raptor-like structure of the 5G LDPC codes and also support rate compatibility. The proposed codes reveals two distinct LDPC code design criteria for FTN signaling, i.e., 1) no information bits should be punctured; 2) columns with high column weights should be removed in the base graph. The advantages of the proposed codes are explicitly verified by our numerical results, where noticeable coding gains compared to existing codes and coded Nyquist systems can be observed.

Motivated by federated learning (FL), secure aggregation (SA) aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation (HSA) investigated the communication and secret key generation efficiency in a 3-layer relay network, where clusters of users are connected to the aggregation server through an intermediate layer of relays. Due to the pre-aggregation of the messages at the relays, HSA reduces the communication burden on the relay-to-server links and is able to support a large number of users. However, as the number of users increases, a practical challenge arises from heterogeneous security requirements–for example, users in different clusters may require varying levels of input protection. Motivated by this, we study weakly-secure HSA (WS-HSA) with collusion resilience, where instead of protecting all the inputs from any set of colluding users, only the inputs belonging to a predefined collection of user groups (referred to as security input sets) need to be protected against another predefined collection of user groups (referred to as collusion sets). Since the security input sets and collusion sets can be arbitrarily defined, our formulation offers a flexible framework for addressing heterogeneous security requirements in HSA. We characterize the optimal total key rate, i.e., the total number of independent key symbols required to ensure both server and relay security, for a broad range of parameter configurations. For the remaining cases, we establish lower and upper bounds on the optimal key rate, providing constant-factor gap optimality guarantees.

We investigate a joint scattering environment sensing, channel estimation, and data recovery problem in an uplink integrated sensing and communication (ISAC) system. Based on a three-dimensional (3D) location-domain sparse channel model, the joint problem is formulated as a bilinear sparse recovery problem with a dynamic position grid and imperfect parameters. We propose an expectation maximization based bilinear subspace variational Bayesian inference (EM-BiSVBI) algorithm to solve the problem effectively, where the E-step performs Bayesian estimation of the the location-domain sparse channel and transmitted data, and the M-step refines the dynamic position grid and learns the imperfect factors via gradient update. In particular, the BiSVBI algorithm in the E-step avoids the high-dimensional matrix inverse by a subspace constrained approach while ensuring convergence to a stationary solution of the Kullback-Leibler divergence minimization problem. Simulations verify the advantages of the proposed method over baselines.

Joint localization and environmental mapping in multipath environments aims to jointly estimate the states of user equipment (UE) and the positions of landmarks using radio signals in multipath environments. Most existing approaches for joint localization and environmental mapping in multipath environments handle the data association (DA) problem using random finite sets (RFS) or factor graphs (FG). This paper introduces a novel clustering-based landmark optimization approach (CLOM) for multipath joint localization and environmental mapping, designed for global optimization in multipath environments. CLOM handles DA by iteratively clustering the landmark positions observed at different time steps and optimizing the corresponding UE positions. Unlike traditional methods based on RFS or FG, CLOM offers an alternative approach focused on optimizing UE positions while maintaining the consistency of landmarks across different time steps. It is demonstrated that the proposed method achieves comparable performance to the Rao-Blackwellized particle filter (RBPF)-based method, with relatively low complexity.

Flat-top beam designs are essential for uniform power distribution over a wide angular sector for applications such as 5G/6G networks, beaconing, satellite communications, radar systems, etc. Low sidelobe levels with steep transitions allow negligible cross sector illumination. Active array designs requiring amplitude taper suffer from poor power amplifier utilization. Phase only designs, e.g., Zadoff-Chu or generalized step chirp polyphase sequence methods, often require large active antenna arrays which in turns increases the hardware complexity and reduces the energy efficiency. In our recently proposed novel array-fed reflective intelligent surface (RIS) architecture, the small (2×2) active array has uniform (principal eigenmode) amplitude weighting. We now present a pragmatic flat-top pattern design method for practical array (RIS) sizes, which outperforms current state-of-the-art in terms of design superiority, energy efficiency, and deployment feasibility. This novel design holds promise for advancing sustainable wireless technologies in next-generation communication systems, including applications such as beaconing, broadcast signaling, and hierarchical beamforming, while mitigating the environmental impact of high-energy antenna arrays.

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

Orthogonal time frequency space (OTFS) modulation has received significant attention recently due to its superior performance compared to conventional multicarrier waveforms. However, symbol detection with OTFS is significantly more involved and typically operates on large signal blocks with intersymbol interference (ISI) in the delay-Doppler (DD) domain. In this paper, we investigate the performance of OTFS within the cross-domain iterative detection (CDID) framework. Specifically, three distinct CDID algorithms are presented and investigated, which estimate/detect the information symbols iteratively across the frequency and DD domains via passing either thea posteriorior extrinsic information using a full-sized or single-tap linear minimum mean square error (LMMSE) estimator. Building upon this framework, we study the average mean square error (MSE) for the considered CDID algorithms, where both the bias evolution and the state (variance) evolution are investigated. Particularly, we show that the proposed CDIDs can provide unbiased estimation under certain channel conditions. Furthermore, a fixed point exists in the state evolution when the estimation is unbiased, indicating that the algorithm’s convergence is guaranteed. More importantly, we reveal that passing thea posterioriinformation is more beneficial when the underlying channel has negligible Doppler spread while passing the extrinsic information is more suitable for non-negligible Doppler spread cases, where the frequency domain channel matrix lacks diagonal dominance. Our numerical results confirm our analytical findings and unveil the near-optimal error performance achieved by the proposed design.

In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of$ K$files from N servers without revealing the demanded file index to any server. This paper formulates an extended model of PIR, referred to as multi-message private computation (MM-PC), where instead of retrieving a single file, the user wishes to retrieve$P\gt 1$linear combinations of files while preserving the privacy of the demand information. The MM-PC problem is a generalization of the private computation (PC) problem (where the user requests one linear combination of the files), and the multi-message private information retrieval (MM-PIR) problem (where the user requests$P\gt 1$files). A baseline achievable scheme repeats the optimal PC scheme by Sun and Jafar P times, or treats each possible demanded linear combination as an independent file and then uses the near optimal MM-PIR scheme by Banawan and Ulukus. In this paper, we propose a new MM-PC scheme that significantly improves upon the baseline schemes. In doing so, we design the queries inspired by the structure in the cache-aided scalar linear function retrieval scheme by Wan et al., which leverages the dependency between linear functions to reduce the amount of communications. To ensure the decodability of our scheme, we propose a new method to benefit from the existing dependency, referred to as the sign assignment step. In the end, we use Maximum Distance Separable matrices to code the queries, which allows the reduction of download from the servers, while preserving privacy. By the proposed schemes, we characterize the capacity within a multiplicative factor of 2.

Coded Caching is a technology that promises to reduce cacheable traffic by turning stored content at the users to multicast opportunities. In wireless channels, though, users experience different rates causing each message to be communicated with the group’s worst-user’s rate, which in turn impacts significantly the achieved performance. In this work we propose an adaptive quality transmission framework specifically designed for coded caching multicast communications, which uses superposition coding to overcome channel degradation. Our scheme combines coded caching, superposition coding, and scalable source coding, while keeping the caching oblivious to future channel rates and delivered file quality. The proposed framework covers all possible channel rate and quality configurations, while we further propose algorithms that can optimise the served quality. An interesting outcome of our work is that a modest quality reduction at the degraded users can counter the effects of significant channel degradation. For example, in a 100-user system with normalized cache size 1/10 at each user, if 10 users experience channel degradation of 60% compared to the rate of the non-degraded users, we show that our transmission strategy leads to a$\thicksim 85\%$quality at the degraded users and perfect quality at the non-degraded users.

In this paper, a point-to-point (P2P) orthogonal time frequency space (OTFS)-based multiple-input multiple-output (MIMO-OTFS) transmission scheme is devised for millimeter wave (mmWave) channels. The proposed transmission scheme relies on a low-complexity hybrid digital-analog beamforming (HBF) scheme that exploits the delay-Doppler (DD) domain channel properties, where detailed design criteria for different channel conditions are presented, including the case where paths are indistinguishable by angles. Thanks to the proposed HBF scheme, approximate path-wise interference-free transmission of multiple data streams is achieved, and consequently, only little pre-equalization is required for combating the residual channel impairments. The achievable rate of the proposed scheme is studied and compared with the orthogonal frequency-division multiplexing (OFDM) counterpart. In particular, we unveil that the condition number of the effective angular domain matrix for OTFS is smaller than that for OFDM, due to the enhanced path separability in the DD domain. As a result, the proposed MIMO-OTFS transmission scheme demonstrates superior performance over the MIMO-OFDM transmission scheme. Our numerical results corroborate our theoretical analysis and show a near-optimal rate performance with significantly reduced complexity compared to the optimal singular value decomposition (SVD) precoding method.

Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates potentially leak sensitive user information, and the lack of central control over local model updates leaves the global model susceptible to malicious attacks. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) design for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update); and 2) have partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion ofaggregated privacythat characterizes the minimum amount of user information, in the form of aggregated statistics of users’ updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines.

We consider unsourced random access (uRA) in a cell-free (CF) user-centric wireless network, where a large number of potential users compete for a random access slot, while only a finite subset is active. The random access users transmit codewords of lengthLsymbols from a shared codebook, which are received byBgeographically distributed radio units (RUs), each equipped withMantennas. Our goal is to devise and analyze acentralizeddecoder to detect the transmitted messages (without prior knowledge of the active users) and estimate the corresponding channel state information. A specific challenge lies in the fact that, due to the geographically distributed nature of the CF network, there is no fixed correspondence between codewords and large-scale fading coefficients (LSFCs). This makes current activity detection approaches which make use of this fixed LSFC-codeword association not directly applicable. To overcome this problem, we propose a scheme where the access codebook is partitioned in location-based subcodes, such that users in a particular location make use of the corresponding subcode. The joint message detection and channel estimation is obtained via a novelApproximated Message Passing(AMP) algorithm for a linear superposition of matrix-valued sources corrupted by noise. The statistical asymmetry in the fading profile and message activity leads todifferent statisticsfor the matrix sources, which distinguishes the AMP formulation from previous cases. In the regime where the codebook size scales linearly withL, whileBandMare fixed, we present a rigorous high-dimensional (but finite-sample) analysis of the proposed AMP algorithm. Exploiting this, we then present a precise (and rigorous) large-system analysis of the message missed-detection and false-alarm rates, as well as the channel estimation mean-square error. The resulting system allows the seamless formation of user-centric clusters and very low latency beamformed uplink-downlink communication without explicit user-RU association, pilot allocation, and power control. This makes the proposed scheme highly appealing for low-latency random access communications in CF networks.

This paper studies a multiaccess coded caching (MACC) problem where the connectivity topology between the users and the caches can be described by a class of combinatorial designs. Our model includes several MACC topologies considered in previous works as special cases. The considered MACC network includes a server containingNfiles, Γ cache nodes andKcacheless users, where each user can accessLcache nodes. The server is connected to the users via an error-free shared link, while the users can directly access the content in their connected cache nodes. Our goal is to minimize the worst-case transmission load on the shared link in the delivery phase. The main limitation of the existing MACC works is that only some specific access topologies are considered, including the only cases where the number of usersKscales either linearly or exponentially in Γ. We overcome this limitation by formulating a new access topology derived from two classical combinatorial structures, referred to as thet-design and thet-group divisible design. By leveraging the properties of these combinatorial structures, we propose two classes of coded caching schemes for a flexible number of users, where the number of users can scale linearly, polynomially or exponentially with the number of cache nodes. As a by-product, by extending the proposed scheme to the original dedicated coded caching scenario (i.e., each user has its own cache), the resulting scheme can unify several existing coded caching schemes.

In this paper, we propose ByzSecAgg, an efficient secure aggregation scheme for federated learning that is resistant to Byzantine attacks and privacy leakages. Processing individual updates to manage adversarial behavior, while preserving privacy of data against colluding nodes, requires some sort of secure secret sharing. However, the communication load for secret sharing of long vectors of updates can be very high. In federated settings, where users are often edge devices with potential bandwidth constraints, excessive communication overhead is undesirable. ByzSecAgg solves this problem by partitioning local updates into smaller sub-vectors and sharing them using ramp secret sharing. However, this sharing method does not admit bi-linear computations, such as pairwise distance calculations, which are needed for distance-based outlier-detection algorithms, and effective methods for mitigating Byzantine attacks. To overcome this issue, each user runs another round of ramp sharing, with a different embedding of data in the sharing polynomial. This technique, motivated by ideas from coded computing, enables secure computation of pairwise distance. In addition, to maintain the integrity and privacy of the local update, ByzSecAgg also uses a vector commitment method, in which the commitment size remains constant (i.e., does not increase with the length of the local update), while simultaneously allowing verification of the secret sharing process. In terms of communication load, ByzSecAgg significantly outperforms the related baseline scheme, known as BREA.

Bistatic sensing refers to scenarios where the transmitter (illuminating the target) and the sensing receiver (estimating the target state) are physically separated, in contrast to monostatic sensing, where both functions are co-located. In practical settings, bistatic sensing may be required either due to inherent system constraints or as a means to mitigate the strong self-interference encountered in monostatic configurations. A key practical challenge in bistatic radio-frequency radar systems is the synchronization and calibration of the separate transmitter and sensing receiver. In this paper, we are not concerned with these signal processing aspects and take a complementary information-theoretic perspective on bistatic integrated sensing and communication (ISAC). Namely, we aim to characterize the capacity-distortion function—the fundamental tradeoff between communication capacity and sensing accuracy. We consider a general discrete channel model for a bistatic ISAC system and derive a multi-letter representation of its capacity-distortion function. Then, we establish single-letter upper and lower bounds and provide exact single-letter characterizations for degraded bistatic ISAC channels. Numerical examples illustrate the theoretical results, highlighting the benefits of ISAC over separate communication and sensing, as well as the role of leveraging communication to assist sensing in bistatic systems.

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC):What is the optimal communication-centric ISAC waveform for ranging?Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multiband splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications. All three algorithms are integrated into an experimental OFDM-based IEEE 802.11ac system. Our analysis centers on evaluating the performance of these algorithms under limited number of narrow-band measurements, demonstrating that accurate CIR estimation is achievable even using only 50% of the full wideband spectrum. Additionally, we analyze and compare the computational complexity of these algorithms to assess their practical feasibility

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks.

This study investigates simpler alternatives to coherent joint transmission for supporting robust connectivity against signal blockage in mmWave/sub-THz access networks. By taking an information-theoretic viewpoint, we demonstrate analytically that with a careful design, full macrodiversity gains and significant SNR gains can be achieved through canonical receivers and minimal coordination and synchronization requirements at the infrastructure side. Our proposed scheme extends non-coherent joint transmission by employing a special form of diversity to counteract artificially induced deep fades that would otherwise make this technique often compare unfavorably against standard transmitter selection schemes. Additionally, the inclusion of an Alamouti-like space-time coding layer is shown to recover a significant fraction of the optimal performance. Our conclusions are based on a statistical single-user multi-point intermittent block fading channel model that, although simplified, enables rigorous ergodic and outage rate analysis, while also considering timing offsets due to imperfect delay compensation. In addition, we validate our theoretical approach by means of deterministic ray-tracing simulations that capture the essential features of next generation mmWave/sub-THz communications.

In this paper, we study the sensing-assisted beam tracking (BT) problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications with different constraints on the antenna feeding networks. In particular, we consider a monostatic setup where a multi-antenna base station (BS) transmits information bearing orthogonal frequency division multiplexing (OFDM) signals via narrow beams, and a sensing receiver, co-located with the BS, listens to the echoes and estimates the adaptive steering needed in the BS transmit beams. Motivated by cost and space limitations of wide bandwidth mixed signal components, we propose and evaluate estimation and tracking algorithms for analog, digital, and hybrid digital analog (HDA) antenna architectures. The analog tracker is based on a novel learned variation of the hidden Markov model (HMM) filter, whereas HDA and digital trackers are based on much simpler methods, given their much richer corresponding measurements. Our results show that analog architectures attain performance close to that of fully digital setups while keeping system complexity much lower. All trackers are also shown to be capable of operating in multi user scenarios. The effect of transmit power and interval between measurements for the different architectures is thoroughly discussed in our numerical results.

The acquisition of Downlink (DL) channel state information at the transmitter (CSIT) is known to be a challenging task in multiuser massive MIMO systems when uplink/downlink channel reciprocity does not hold (e.g., in frequency division duplexing systems). From a coding viewpoint, the DL channel state acquired at the users via DL training can be seen as an information source that must be conveyed to the base station via the UL communication channels. The transmission of a source through a channel can be accomplished either by separate or joint source-channel coding (SSCC or JSCC). In this work, using classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation meansquare-error (MSE) of both JSCC and SSCC-based feedback schemes, which however requires encoding of large blocks of successive channel states and thus cannot be used in practice since it would incur in an extremely large feedback delay. We then focus on the relevant case of minimal (one slot) feedback delay and propose a practical JSCC-based feedback scheme that fully exploits the channel second-order statistics to optimize the dimension projection in the eigenspace. We analyze the large SNR behavior of the proposed JSCC-based scheme in terms of the quality scaling exponent (QSE). Given the second-order statistics of channel estimation of any feedback scheme, we further derive the closed-form of the lower bound to the ergodic sum-rate for DL data transmission under maximum ratio transmission and zero-forcing precoding. Via extensive numerical results, we show that our proposed JSCC-based scheme outperforms known JSCC, SSCC baseline and deep learning-based schemes and is able to approach the performance of the optimal DR scheme in the range of practical SNR.

This work presents an efficient codebook synthesis method with self-interference (SI) suppression for mmWave integrated sensing and communication (ISAC) devices employing RF beamforming. We formulate a signal-to-interference-and-noise ratio (SINR) maximization problem which aims to suppress the near-field SI signal while maintaining the beamforming gain in the far-field sensing directions. In particular, we consider the practical constraints of discrete codebooks with quantized phase settings. To solve such a discrete optimization problem, we propose to use a fractional programming (FP) technique to decouple the numerator and the denominator of the optimization target, which is followed by a spherical search (SS). Simulations show that the proposed method significantly outperforms the existing methods, which are based on continuous optimization algorithms. The results also demonstrate that the proposed method can reach the same performance as the exhaustive search method, but with a lower complexity.

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address this combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to an upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the semantic content of an image, while ensuring a good trade-off between coding rate and image quality. The proposed Semantic-Preserving Image Coding based on Conditional Diffusion Models (SPIC) transmitter encodes a Semantic Segmentation Map (SSM) and a low-resolution version of the image to be transmitted. The receiver then reconstructs a high-resolution image using a Denoising Diffusion Probabilistic Models (DDPM) doubly conditioned to the SSM and the low-resolution image. As shown by the numerical examples, compared to state-of-the-art (SOTA) approaches, the proposed SPIC exhibits a better balance between the conventional rate-distortion trade-off and the preservation of semantically-relevant features. Code available at https://github.com/frapez1/SPIC

This study investigates scalable uplink cell-free massive multiple-input multiple-output networks, comprising user equipments (UEs), radio units (RUs), data routers, and decentralized processing units (DUs). In our model, UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors, implementing the physical layer for each user, are hosted as software-defined virtual network functions by the DUs. In our paradigm, RUs, data routers, and DUs are not fully connected, necessitating a holistic approach to address the joint challenges of cluster processor placement (at one of the DUs) and the allocation of fronthaul data links among RUs, routers, and DUs. We simultaneously consider the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. Specifically, we formulate the joint optimization of fronthaul load balancing and cluster processor placement as a mixed-integer linear problem. Furthermore, we present numerical results that shed light on the interplay between these elements under finite resolution of the A/D quantization at the RUs.

In this work, we study and compare two types of CSI feedback schemes in multi-user massive MIMO systems, respectively based on joint and separate source-channel coding (JSCC and SSCC). Using the classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation mean-square-error (MSE) of any feedback scheme. The DR bound is achieved by using vector quantization applied to long sequences of channel state estimates and requires capacity-achieving channel coding in the uplink, resulting in a large delay in the CSI feedback loop that makes the scheme impractical. Thus we propose a practical JSCC-based feedback scheme that sends the CSI with minimal delay. Unlike previous works that simply apply linear mapping and equal power allocation to generate the feedback signal, our method applies the dimension projection in the eigenspace and optimizes power allocation by fully exploiting the channel second-order statistics. The extensive numerical results show that our proposed JSCC-based scheme not only outperforms the previous JSCC scheme with linear processing but also produces lower channel estimate MSE compared to a standard SSCC-based scheme at practical SNR.

This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.

Integrated sensing and communications (ISAC) is envisioned to be a key to advanced applications in future wireless networks. In this paper, we study the coded modulation designs for ISAC transmissions with short block lengths over correlated Rayleigh fading channels. In line with the short block length transmission, we consider the non-coherent communication detection and coherent radar sensing, where a neural network (NN)-assisted frame-wise constellation design is proposed. Specifically, we first derive the optimal communication and radar receivers. Then, we present some heuristic understandings of the code designs by considering special cases, based on which a conjecture on the optimal codes for the considered ISAC transmissions is developed. The constellation obtained from the proposed NN agrees with our conjecture and shows an important conclusion that the optimal codes of the considered problem may be a combination of the “on-off keying” and phase-shifted keying signalings. Our numerical results show that the proposed code exhibits promising communication and sensing performance simultaneously and outperforms the transmissions with a standard channel code and symbol-wise modulation.

This paper explores the fundamental limits of unsourced random access (URA) with a random and unknown number$\mathrm{K}_{a}$of active users in MIMO quasi-static Rayleigh fading channels. First, we derive an upper bound on the probability of incorrectly estimating the number of active users. We prove that it exponentially decays with the number of receive antennas and eventually vanishes, whereas reaches a plateau as the power and blocklength increase. Then, we derive non-asymptotic achievability and converse bounds on the minimum energy-per-bit required by each active user to reliably transmit$J$bits with blocklength$n$. Numerical results verify the tightness of our bounds, suggesting that they provide benchmarks to evaluate existing schemes. The extra required energy-per-bit due to the uncertainty of the number of active users decreases as$\mathbb{E}[\mathrm{K}_{a}]$increases. Compared to random access with individual codebooks, the URA paradigm achieves higher spectral and energy efficiency. Moreover, using codewords distributed on a sphere is shown to outperform the Gaussian random coding scheme in the non-asymptotic regime.

In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of$K$files from$N$servers without revealing the demanded file index to any server. This paper formulates an extended model of PIR, referred to as multi-message private computation (MMPC), where instead of retrieving a single file, the user wishes to retrieve$P > 1$linear combinations of files while preserving the privacy of the demand information. The MM-PC problem is a generalization of the private computation (PC) problem (where the user requests one linear combination of the files), and the multi-message private information retrieval (MM-PIR) problem (where the user requests$P > 1$files). A baseline achievable scheme repeats the optimal PC scheme by Sun and Jafar$P$times, or treats each possible demanded linear combination as an independent file and then uses the near optimal MM-PIR scheme by Banawan and Ulukus. In this paper, we propose an achievable MM-PC scheme that significantly improves upon the baseline scheme. Doing so, we design the queries inspired from the structure in the cache-aided scalar linear function retrieval scheme, where they leverage the dependency between messages to reduce the amount of communication. To ensure the decodability of our scheme, we propose a new method to benefit from the existing dependency, referred to as the sign assignment step. In the end, we use Maximum Distance Separable matrices to code the queries, which allows the reduction of download from the servers, while preserving privacy.

This work focuses on the efficient delivery of content over the single antenna degraded broadcast channel with user caching through the adaptation of the content quality at the users. We design a delivery scheme which combines superposition coding, multicasting, and scalable video coding, while keeping the caching scheme oblivious to channel qualities. By lowering the quality at users who experience channel degradation we are able to satisfy user demands in a time efficient manner. In addition, superposition coding allows us to treat users with higher channel rates without subjecting them to a delay penalty due to their degraded counterparts' channels. An interesting outcome of this work is that a modest reduction in the quality of the degraded users can counter the effects of a significant channel degradation. For example, in a 100-user channel with normalised cache size 1/10 at each user, if 10 users experience channel degradation of 60% compared to the rate of the non-degraded users, we show that our transmission strategy leads to a ~ 85% quality at the degraded users and perfect quality at the non-degraded users.

In this paper, we propose a novel cross domain iterative detection for unitary modulated symbols transmissions, e.g., OFDM. Particularly, signal spaces before and after the unitary modulation are conceptualized as two domains and the proposed scheme performs iterations across these two domains for signal detection. More specifically, a tunable-sized linear minimum mean square error (LMMSE) estimator is adopted in one domain, complemented by a reduced-complexity sum-product algorithm (SPA) in the other. Heuristic state evolution of the proposed scheme is derived, which reveals that a reduced-sized LMMSE estimator will introduce performance degradation that cannot be compensated by the cross domain iteration. However, it is advantageous for complexity reduction. Our numerical results verify our conclusions and show that the proposed scheme can achieve error performance comparable to that of the standard SPA while requiring lower complexity.

Motivated by wireless communications at high carrier frequencies in 5G and 6G systems (mmWaves, sub-THz), we consider a state-dependent channel model with in-block memory referred to as the Gaussian beam-pointing (GBP) channel. A transmitter equipped with a large antenna array wishes to communicate with a receiver located at an unknown angle of departure (AoD). The AoD defines discrete channel states, taking values in a discrete set of$M$possible values (quantized beam “directions”), constant within a coherence block and changing independently across blocks. Each block spans$Q$time slots of length$q$channel uses (also referred to as signal dimension). At the end of each slot, the transmitter receives a (strictly causal) feedback signal which may represent either the detection result of some radar sensor, or an explicit feedback signal from the receiver. The GBP model, a realistic extension of a binary beam-pointing channel studied in the authors' previous paper, offers a sufficiently simple yet insightful model for understanding channel capacity in beamforming-based communication systems. We establish both an upper bound and an approximate inner bound on capacity that can be calculated by solving carefully designed optimization problems. Numerical examples demonstrate that our proposed transmission strategy achieves a near-optimal achievable rate when the signal dimension$q$is large enough.

In this paper, we consider a remote source coding problem with binary phase shift keying (BPSK) modulation sources, where observations are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs further compression to find the optimal trade-off between complexity and relevance. This problem can be formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations, for which no closed-form solution is known. To address this challenge, we propose a unified achievable scheme that employs three different compression strategies for intermediate node processing, i.e., two-level quantization, multi-level deterministic quantization, and soft quantization with tanh function. Comparative analyses with existing methods, such as the Blahut-Arimoto (BA) algorithm and the Information Dropout approach, are performed through numerical evaluations. The proposed analytic scheme is observed to consistently approach the (numerically) optimal performance over a range of signal-to-noise ratios (SNRs), confirming its effectiveness in the considered setting.

This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a server aggregates the trained models without retrieving other information about users' local data. Secure aggregation generally contains two phases, namely key sharing phase and model aggregation phase. Due to the common effect of user dropouts in federated learning, the model aggregation phase should contain two rounds, where in the first round the users transmit masked models and according to the identity of surviving users, the surviving users then transmit some further messages to help the server decrypt the sum of users' trained models. The objective of the considered information theoretic formulation is to characterize the capacity region of the communication rates from the users to the server in the two rounds of the model aggregation phase, by assuming that the key sharing have already been done offline in prior. If the keys shared by the users could be any random variables, the capacity was fully characterized in the literature. Recently, an additional constraint on the keys (referred to as uncoded groupwise keys) was added into the problem, where there are several independent keys in the system and each key is shared by exactly S users, where S is a system parameter. In this paper, we fully characterize the capacity region for this problem by matching new converse and achievable bounds.

This paper studies information theoretic secure aggregation in federated learning, involving K distributed users and a central server. “Secure” means that the server can only get aggregated locally trained model updates, with no other information about the local users' data being leaked to the server. In addition, the effect of user dropouts is considered, where at most$\mathsf{K}-\mathsf{U}$users can drop and the identity of these users cannot be predicted in advance. Users share keys in an offline way independently of the models, and send the encrypted models to the server in the model aggregation phase. The objective of this problem is to minimize the number of transmissions in the model aggregation phase. A secure aggregation scheme with uncoded groupwise keys, where any$\mathsf{S}$users share an independent key, was recently proposed to achieve the same optimal communication cost as the best scheme with coded keys when$\mathsf{S} > \mathsf{K}-\mathsf{U}$. In this paper, we additionally consider the potential impact of user collusion, where up to$\mathsf{T}$users may collude with the server. For this setting, we propose a secure aggregation scheme with uncoded groupwise keys that guarantees secure aggregation with$\mathsf{U}$non-dropped users and$\mathsf{T}$colluding users provided that$\mathsf{K}-\mathsf{U}+1\leq \mathsf{S}\leq$K - T, and is proven to achieve the optimality without any constraint on the keys.

In this paper, we investigate the performance of the cross-domain iterative detection (CDID) framework with orthogonal time frequency space (OTFS) modulation, where two distinct CDID algorithms are presented. The proposed schemes estimate/detect the information symbols iteratively across the frequency domain and the delay-Doppler (DD) domain via passing either the a posteriori or extrinsic information. Building upon this framework, we investigate the error performance by considering the bias evolution and state evolution. Furthermore, we discuss their error performance in convergence and the DD domain error state lower bounds in each iteration. Specifically, we demonstrate that in convergence, the ultimate error performance of the CDID passing the a posteriori information can be characterized by two potential convergence points. In contrast, the ultimate error performance of the CDID passing the extrinsic information has only one convergence point, which, interestingly, aligns with the matched filter bound. Our numerical results confirm our analytical findings and unveil the promising error performance achieved by the proposed designs.

Bistatic Integrated Sensing and Communication (ISAC) systems circumvent the issue of strong self-interference present in monostatic ISAC systems by employing a pair of physically separated sensing transceivers. They maintain the advantage of co-designing radar sensing and communications on shared spectrum and hardware. Motivated by the favorable attributes of bistatic radar, this paper investigates bistatic ISAC. In this setup, a transmitter sends messages to a communication receiver, while a sensing receiver at another location conducts a “decoding-and-estimation” (DnE) operation to obtain the state of the communication receiver. We propose three achievable DnE strategies based on the degree of information decoding at the sensing receiver: blind estimation, partial decoding-based estimation, and full decoding-based estimation. We explore the corresponding rate-distortion regions associated with each strategy. Furthermore, we provide a specific example to illustrate the comparison of the rate-distortion regions among the three DnE strategies and demonstrate the advantage of ISAC over independent communication and sensing.

Reconfigurable intelligent surface (RIS) has been treated as a core technique in improving wireless propagation environments for the next generation wireless communication systems. This paper proposes a new coded caching problem, referred to as Reconfigurable Intelligent Surface (RIS)-assisted multiple-antenna coded caching, which is composed of a server with multiple antennas and some single-antenna cache-aided users. Different from the existing multi-antenna coded caching problems, we introduce a passive RIS (with limited number of units) into the systems to further increase the multicast gain (i.e., degrees of freedom$(\mathbf{DoF}))$in the transmission, which is done by using RIS-assisted interference nulling. That is, by using RIS, we can ‘erase’ any path between one transmission antenna and one receive antenna. We first propose a new RIS-assisted interference nulling approach to search for the phase-shift coefficients of RIS for the sake of interference nulling, which converges faster than the state-of-the-art algorithm. After erasing some paths in each time slot, the delivery can be divided into several non-overlapping groups including transmission antennas and users, where in each group the transmission antennas serve the contained users without suffering interference from the transmissions by other groups. The division of groups for the sake of maximizing the DoF could be formulated into a combinatorial optimization problem. We propose a grouping algorithm which can find the optimal solution with low complexity, and the corresponding coded caching scheme achieving this DoF.

Secure aggregation is concerned with the task of securely uploading the inputs associated with multiple users to an aggregation server without revealing the user inputs to the server besides the summation of all inputs. It finds broad applications in distributed machine learning paradigms such as federated learning (FL). Motivated by practical hierarchical FL systems which utilize the client-edge-cloud network architecture to improve delay performance, we study the hierarchical secure aggregation (HSA) problem in a 3-layer hierarchical network where a total of$UV$users are connected to an aggregation server through$U$relay nodes each being associated with a disjoint subset of$V$users. Security requires that the server learn nothing beyond the desired sum of the inputs (server security), and each relay learn nothing about the user inputs (relay security) even if they collude with up to$T$users. We characterize the optimal communication and key rate region by proposing a novel secure aggregation scheme and deriving an information-theoretic converse that matches the achievable scheme. In particular, we show that when$T\geq(U-1)V$, the proposed HSA problem is infeasible. Otherwise when$T < (U-1)V$, to securely compute 1 bit of the desired sum, each user needs to upload at least 1 bit to its associating relay, each relay needs to upload at least 1 bit to the server, each user needs to hold at least 1 key bit, and all users need to collectively hold at least$\max\{V+T, \min\{U+T-1,UV- 1\}\}$(source) key bits. The characterization of the source key rate is a major contribution of this work.

This work proposes a maximum likelihood-based parameter estimation framework for a multistatic millimeter wave integrated sensing and communication system using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays used in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. To address this, we propose a two-step estimation process. Initially, we consider far-field (FF) propagation assumptions, followed by refined estimation based on NF assumptions, enhancing accuracy when the target is within the NF of the arrays. In particular, when operating in the NF of the transmitter (Tx), we select beamfocusing array weights designed to achieve constant gain over an extended spatial region. Subsequently, we re-estimate target parameters at the receivers (Rxs). The effectiveness of the proposed framework is evaluated over various scenarios through numerical simulations. In particular, the impact of customdesigned flat-gain beamfocusing codewords in improving both communication and sensing performance when the target is in the NF of the Tx is demonstrated. Additionally, the benefit of considering a correct NF channel model when the target is located near an Rx is shown.

We present numerical results with a focus on power transfer between two standard linear antenna arrays placed in the near field, where a much smaller active multi-antenna feeder (AMAF) space feeds a far larger passive array referred to as a reflective intelligent surface (RIS). The interest is in the regime of focal length to diameter ratio (F/D) less than unity. We address the question of center feed vs. end feed for array fed array antenna architectures and present the following novel findings and contributions: 1. In the regime of$F$/$D$ratio less than one, the AMAF-RIS power transfer deviates from the classical inverse square law. Furthermore, the behavior of the power transmission coefficient is more sensitive to the$F$/$D$ratio than to a particular RIS size. 2. For an end feed, non-eigenmodes provide better beam shapes than the eigenmodes, which are still inferior to beam shapes from the center feed eigenmodes. 3. The center feed provides more power gain than an end feed. This clearly illustrates that the center feed configuration should be used for array fed arrays, in contrast to the classical parabolic geometry.

Ultradense cell-free massive multiple-input multiple-output (CF-MMIMO) has emerged as a promising technology expected to meet the future ubiquitous connectivity requirements and ever-growing data traffic demands in sixth generation (6G). This article provides a contemporary overview of ultradense CF-MMIMO networks and addresses important unresolved questions on their future deployment. We first present a comprehensive survey of state-of-the-art research on CF-MMIMO and ultradense networks. Then, we discuss the key challenges of CF-MMIMO under ultradense scenarios such as low-complexity architecture and processing, low-complexity/scalable resource allocation, fronthaul limitation, massive access, synchronization, and channel acquisition. Finally, we answer key open questions, considering different design comparisons and discussing suitable methods dealing with the key challenges of ultradense CF-MMIMO. The discussion aims to provide a valuable roadmap for interesting future research directions in this area, facilitating the development of CF-MMIMO for 6G.

Multiple access is the cornerstone technology for each generation of wireless cellular networks, which fundamentally determines the method of radio resource sharing and significantly influences both the system performance and transceiver complexity. Meanwhile, resource allocation (RA) design plays a crucial role in multiple access, as it can manage both encompassing radio resources and interference, and it is critical for providing high-speed and reliable communication services to multiple users. Given that the RA design is intrinsically scenario-specific and the optimization tools for RA design are typically varied, in this article, we present a comprehensive tutorial overview for junior researchers in this field, aiming to offer a foundational guide for RA design in the context of next-generation multiple access (NGMA). Our discussion spans a broad range of fundamental topics: from typical system models, through intriguing problem formulation in RA design, to the exploration of various potential optimization solution methodologies. Initially, we identify three types of channels in future wireless cellular networks over which NGMA will be implemented, namely, natural channels, reconfigurable channels, and functional channels. Natural channels are traditional uplink and downlink communication channels; reconfigurable channels are defined as channels that can be proactively reshaped via emerging platforms or techniques, such as intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and movable/fluid antenna (M/FA); and functional channels support not only communication but also other functionalities simultaneously, with typical examples, including integrated sensing and communication (ISAC) and joint computing and communication (JCAC) channels. Then, we introduce NGMA models applicable to these three types of channels that cover most of the practical communication scenarios of future wireless communications. Subsequently, we articulate the key optimization technical challenges inherent in the RA design for NGMA, categorizing them into rate-, power-, and reliability-oriented RA designs. The corresponding optimization approaches for solving the formulated RA design problems are then presented. Finally, the simulation results are presented and discussed to elucidate the practical implications and insights derived from RA designs in NGMA.

This paper considers a Gaussian multi-input multi-output (MIMO) wiretap channel with a legitimate transmitter, a legitimate receiver (Bob), an eavesdropper (Eve), and a cooperative jammer. All nodes may be equipped with multiple antennas. Traditionally, the jammer transmits Gaussian noise (GN) to enhance the security. However, using this approach, the jamming signal interferes not only with Eve but also with Bob. In this paper, besides the GN strategy, we assume that the jammer can also choose to use the encoded jammer (EJ) strategy, i.e., instead of GN, it transmits a codeword from an appropriate codebook. In certain conditions, the EJ scheme enables Bob to decode the jamming codeword and thus cancel the interference, while Eve remains unable to do so even if it knows all the codebooks. We first derive an inner bound on the system’s secrecy rate under the strong secrecy metric, and then consider the maximization this bound through precoder design in a computationally efficient manner. In the single-input multi-output (SIMO) case, we prove that although non-convex, the power control problems can be optimally solved for both GN and EJ schemes. In the MIMO case, we propose to solve the problems using the matrix simultaneous diagonalization (SD) technique, which requires quite a low computational complexity. Simulation results show that by introducing a cooperative jammer with coding capability, and allowing it to switch between the GN and EJ schemes, a dramatic increase in the secrecy rate can be achieved. In addition, the proposed algorithms can significantly outperform the current state of the art benchmarks in terms of both secrecy rate and computation time.

As the array dimension of massive MIMO systems increases to unprecedented levels, two problems occur. First, the spatial stationarity assumption along the antenna elements is no longer valid. Second, the large array size results in an unacceptably high power consumption if high-resolution analog-to-digital converters are used. To address these two challenges, we consider a Bussgang linear minimum mean square error (BLMMSE)-based channel estimator for large scale massive MIMO systems with one-bit quantizers and a spatially non-stationary channel. Whereas other works usually assume that the channel covariance is known at the base station, we consider a plug-in BLMMSE estimator that uses an estimate of the channel covariance and rigorously analyze the distortion produced by using an estimated, rather than the true, covariance. To cope with the spatial non-stationarity, we introduce dithering into the quantized signals and provide a theoretical error analysis. In addition, we propose an angular domain fitting procedure which is based on solving an instance of non-negative least squares. For the multi-user data transmission phase, we further propose a BLMMSE-based receiver to handle one-bit quantized data signals. Our numerical results show that the performance of the proposed BLMMSE channel estimator is very close to the oracle-aided scheme with ideal knowledge of the channel covariance matrix. The BLMMSE receiver outperforms the conventional maximum-ratio-combining and zero-forcing receivers in terms of the resulting ergodic sum rate.

A communication setup is considered where a single transmitter wishes to convey messages to one or two receivers and simultaneously estimate the states of the receivers through the backscattered signals of the emitted waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. In this paper, we model the communication channel as a simple memoryless channel with independent and identically distributed (i.i.d.) time-varying state sequences and we model the backscattered signals by (strictly causal) generalized feedback. For single-receiver systems of this form, we fully characterize the capacity-distortion tradeoff, defined as the largest rate at which a message can reliably be conveyed to the receiver while simultaneously allowing the transmitter to sense the state sequence with a given allowed distortion. Our results show a tradeoff between the achievable rates and distortions, and that this tradeoff only stems from a common choice of the input distribution (the waveform) but not from other properties of the utilized codes. To better illustrate the capacity-distortion tradeoff, we propose a numerical method to compute the optimal inputs (waveforms) that achieve the desired tradeoff. For two-receiver systems with two states, we characterize the capacity-distortion tradeoff region of physically degraded broadcast channels (BC) as a rather straightforward extension of the single receiver case. Here, a tradeoff not only arises between sensing and communication performances but also between the various rates and the distortions of the different states. Similarly to the single-receiver case, the optimal co-design scheme exploits the generalized feedback signals only for sensing but not for improving communication performance. This is different for general two-receiver BCs, where optimal co-design schemes exploit generalized feedback also to improve capacity. However, as we show, also for BCs the optimal sensing performance only depends on the chosen input distribution (waveform) but not on the code construction used to accomplish the communication task. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions, in which case no tradeoff between sensing and communication occurs. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication, both for single-receiver and BC systems.

This paper investigates the privacy problem in coded caching. Recently, it was shown that the seminal MAN coded caching scheme leaks the demand information of each user to the other users in the system. Many works have considered coded caching with demand privacy, while every non-trivial existing coded caching scheme with private demands was built on the fact that the cache information of each user is private to the others. However, most of these schemes leak the users’ cache information. As a consequence, in most realistic settings (e.g., video streaming) where the system is used over time with multiple sequential transmission rounds, these schemes leak demand privacy beyond the first round. This observation motivates our new formulation of coded caching with simultaneously private demands and caches in this paper. For this new model, we first show that an existing coded caching scheme with private demands, referred to as the virtual users scheme, can also preserve the privacy of the users’ caches. However, this scheme suffers from its extremely high subpacketization. The main contribution of this paper is a new construction that generates private coded caching schemes by leveraging two-server private information retrieval (PIR) schemes. We show that if in the PIR scheme the demand is uniform over all files and the queries are independent, the resulting caching scheme is private on both the demands and the caches; otherwise, the resulting scheme is private only on the demands. This first result constructs coded caching schemes from a particular class of PIR schemes, which is a new “structural” result in its own merit. We then construct new two-server PIR schemes with uniform demand and independent queries, such that the resulting caching scheme has a subpacketization level that is significantly reduced compared to the virtual users scheme. Interestingly we propose a new construction of two-server PIR schemes with uniform demand and independent queries by exploiting coded caching schemes. By applying the seminal Maddah-Ali and Niesen coded caching scheme in our construction, the resulting two-server PIR scheme is proved to be order-optimal under the constraint of uniform demand and independent queries. This is a second new “structural” result that somehow closes the loop in the relationship between coded caching and PIR. As a by-product of our new construction, we obtain a new demand private that improves the load of the state-of-the-art demand private caching schemes known so far. Finally, to explore a broader tradeoff between cache privacy and transmission load, we relax the cache privacy constraint and introduce the definition of cache information leakage. Then, again as a by-product of our new construction, we propose new schemes with perfect demand privacy and imperfect cache privacy that achieve an order-gain in load with respect to the scheme with perfect privacy on both demands and caches. This also establishes a first non-trivial achievability result in the tradeoff between load and cache privacy, for demand-private caching schemes.

This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a curious-but-honest server aggregates the trained models without retrieving other information about users’ local data. Secure aggregation generally contains two phases, namely key sharing phase and model aggregation phase. Due to the common effect of user dropouts in federated learning, the model aggregation phase should contain two rounds, where in the first round the users transmit masked models and, in the second round, according to the identity of surviving users after the first round, these surviving users transmit some further messages to help the server decrypt the sum of users’ trained models. The objective of the considered information theoretic formulation is to characterize the capacity region of the communication rates from the users to the server in the two rounds of the model aggregation phase, assuming that key sharing has already been performed offline in prior. In this context, Zhao and Sun completely characterized the capacity region under the assumption that the keys can be arbitrary random variables. More recently, an additional constraint, known as “uncoded groupwise keys,” has been introduced. This constraint entails the presence of multiple independent keys within the system, with each key being shared by precisely S users, where S is a defined system parameter. The capacity region for the information theoretic secure aggregation problem with uncoded groupwise keys was established in our recent work subject to the condition S > K - U, where K is the number of total users and U is the designed minimum number of surviving users (which is another system parameter). In this paper we fully characterize the capacity region for this problem by matching a new converse bound and an achievable scheme. Experimental results over the Tencent Cloud show the improvement on the model aggregation time compared to the original secure aggregation scheme.

Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server. The “secure” nature of such aggregation consists of the fact that no information about the local users’ data must be leaked to the server except the aggregated local models. In order to guarantee security, some keys may be shared among the users (this is referred to as the key sharing phase). After the key sharing phase, each user masks its trained model which is then sent to the server (this is referred to as the model aggregation phase). This paper follows the information theoretic secure aggregation problem originally formulated by Zhao and Sun, with the objective to characterize the minimum communication cost from the$\mathsf K$users in the model aggregation phase. Due to user dropouts, which are common in real systems, the server may not receive all messages from the users. A secure aggregation scheme should tolerate the dropouts of at most${\mathsf K}-{\mathsf U}$users, where$\mathsf U$is a system parameter. The optimal communication cost is characterized by Zhao and Sun, but with the assumption that the keys stored by the users could be any random variables with arbitrary dependency. On the motivation that uncoded groupwise keys are more convenient to be shared and could be used in large range of applications besides federated learning, in this paper we add one constraint into the above problem, namely, that the key variables are mutually independent and each key is shared by a group of$\mathsf S$users, where$\mathsf S$is another system parameter. To the best of our knowledge, all existing secure aggregation schemes (with information theoretic security or computational security) assign coded keys to the users. We show that if${\mathsf S}\gt {\mathsf K}-{\mathsf U}$, a new secure aggregation scheme with uncoded groupwise keys can achieve the same optimal communication cost as the best scheme with coded keys; if${\mathsf S}\leq {\mathsf K}-{\mathsf U}$, uncoded groupwise key sharing is strictly sub-optimal. Finally, we also implement our proposed secure aggregation scheme into Amazon EC2, which are then compared with the existing secure aggregation schemes with offline key sharing.

This paper investigates the achievable region of a K-user discrete memoryless (DM) multiple access wiretap (MAC-WT) channel, where each user transmits both secret and open (i.e., non-confidential) messages. All these messages are intended for the legitimate receiver (Bob), while the eavesdropper (Eve) is only interested in the secret messages. In the achievable coding strategy, the confidential information is protected by open messages and also by the introduction of auxiliary messages. When introducing an auxiliary message, one has to ensure that, on one hand, its rate is large enough for protecting the secret message from Eve and, on the other hand, the resulting sum rate (together with the secret and open message rate) does not exceed Bob’s decoding capability. This yields an inequality structure involving the rates of all users’ secret, open, and auxiliary messages. To obtain the rate region, the auxiliary message rates must be eliminated from the system of inequalities. A direct application of the Fourier-Motzkin elimination procedure is elusive since a) it requires that the number of users K is explicitly given, and b) even for small$K = 3, 4, \ldots $, the number of inequalities becomes extremely large. We prove the result for general K through the combined use of Fourier-Motzkin elimination procedure and mathematical induction. This paper adopts the strong secrecy metric, characterized by information leakage. To prove the achievability under this criterion, we analyze the resolvability region of a K-user DM-MAC channel (not necessarily a wiretap channel). In addition, we show that users with zero secrecy rate can play different roles and use different strategies in encoding their messages. These strategies yield non-redundant (i.e., not mutually dominating) rate inequalities. By considering all possible coding strategies, we provide a new achievable region for the considered channel, and show that it strictly improves those already known in the existing literature by considering a specific example.

This paper studies a novel multi-access coded caching (MACC) model in the two-dimensional (2D) topology, which is a generalization of the one-dimensional (1D) MACC model proposed by Hachem et al. The 2D MACC model is formed by a server containing$N$files,$K_{1}\times K_{2}$cache-nodes with$M$files located at a grid with$K_{1}$rows and$K_{2}$columns, and$K_{1}\times K_{2}$cache-less users where each user is connected to$L^{2}$nearby cache-nodes. The server is connected to the users through an error-free shared link, while the users can retrieve the cached content of the connected cache-nodes without cost. Our objective is to minimize the worst-case transmission load over all possible users’ demands. In this paper, we first propose a grouping scheme for the case where$K_{1}$and$K_{2}$are divisible by$L$. By partitioning the cache-nodes and users into$L^{2}$groups such that no two users in the same group share any cache-node, we use the shared-link coded caching scheme proposed by Maddah-Ali and Niesen for each group. Then for any model parameters satisfying$\min \{K_{1},K_{2}\}\geq L$, we propose a transformation approach which constructs a 2D MACC scheme from two classes of 1D MACC schemes in vertical and horizontal projections, respectively. As a result, we can construct 2D MACC schemes that achieve maximum local caching gain and improved coded caching gain, compared to the baseline scheme by a direct extension from 1D MACC schemes. In addition, we propose new information theoretic converse bounds under the uncoded placement constraint by leveraging the network topology.

We consider a hybrid streaming scheme based on cache-enabled orthogonal multipoint multicast (OMPMC) and on-demand single-point unicast (SPUC) transmission. The network contains two types of nodes, cache-equipped helper nodes (HNs) handling content-centric OMPMC, and cellular base stations (BSs) handling user-centric SPUC. The OMPMC service streams cached files across the network. Users whose demands cannot be satisfied by OMPMC, either because of poor signal quality or because the requested file is not cached at HNs, are served by SPUC; requested files are fetched from the core network and unicast to users using group-specific beamforming transmissions. We consider the overall network radio resource consumption to satisfy the users’ requests for a given outage probability. This yields a parametric constrained optimization problem over the cache and resource allocations of the OMPMC component, as well as the multi-user beamforming scheme of the SPUC component. We devise a surface-following approach on the basis of path-following method to find the optimal traffic streaming solution. Simulation results show that the hybrid scheme provides a more promising trade-off between resource consumption and service outage probability, compared to OMPMC-only and SPUC-only alternatives.

This work proposes a maximum likelihood (ML)- based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multistatic configuration using energy-efficient hybrid digital-analog (HDA) arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field (FF) assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the NF of the arrays, a second estimation based on NF assumptions is carried out to obtain more accurate estimates. In particular, when operating in the near-filed of the transmitter (Tx), we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers (Rxs). We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.

We consider a user-centric cell-free massive MIMO wireless network withLremote radio units, each withMantennas, servingKsingle-antenna user devices (UEs). Most of the current literature considers the regimeLM≫K, where theKUEs are active on each time-frequency slot, and evaluates the system performance in terms ofergodic rates. In this paper, we take a quite different viewpoint. We observe that the regime ofLM≫Kcorresponds to a lightly loaded system with low sum spectral efficiency (SE). In contrast, in most relevant scenarios, the number of UEs is much larger than the total number of antennas (think of a sport event withK~ 10, 000 users andML~ 200 antennas). To achieve high sum SE and handleK≫ML, users must be scheduled over the time-frequency resource. The number of active usersKact⩽Kmust be carefully chosen such that: 1) the network operates close to its maximum SE; 2) the active user set must be chosen dynamically over time in order to enforce fairness in terms of per-user time-averagedthroughput rates. The fairness scheduling problem is canonically formulated as the maximization of a suitable concave componentwise non-decreasingnetwork utility functionof the per-user rates. The intermitted user transmission due to scheduling imposes slot-by-slot coding/decoding, which in turn prevents the achievability of ergodic rates. Hence, we model the per-slot service rates using information outage probability. In order to obtain a tractable problem, we make a “decoupling” assumption on the CDF of the instantaneous mutual information seen at each UEkreceiver. We approximately enforce this condition by introducing a conflict graph that prevents the simultaneous scheduling of users with large pilot contamination conflict and propose an adaptive scheme for instantaneous service rate scheduling based on locally estimating the mutual information CDF at each UE. Overall, the proposed dynamic scheduling is the first to address such system dimensions with tens of thousand users in a scalable way, is robust to system model uncertainties, and can be easily implemented in practice.

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.

The array design of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) environment is investigated. Properly designed uniform array configurations at the transmitter (Tx) and receiver (Rx) can extract maximum spatial multiplexing gain only for a fixed transmit distance between the Tx and Rx arrays and for a fixed orientation of the arrays. However, such designs suffer from significant capacity variations when the position and/or orientation of the arrays is modified. To alleviate this, we examine robust, joint design of non-uniform Tx and Rx arrays, where the minimum capacity over a range of varying array positions and orientations is maximized. First, we show that, by leveraging convex relaxation, the joint Tx and Rx array design problem can be solved with convex optimization techniques in an iterative manner. Moreover, an alternative design method based on dynamic programming (DP) is proposed, which is shown to outperform the convex optimization approach. As the DP algorithm is quite demanding in terms of computational complexity, a modified DP-based algorithm is also proposed, where the Tx and Rx arrays are designed to have the same configuration. It is shown that the resulting non-uniform array configurations with the proposed designs outperform both uniform and non-uniform array designs of the literature in terms of the system robustness.

Coded caching (CC) schemes exploit the cumulative cache memory of the users and simple linear coding to turn unicast traffic (individual file requests) into a multicast transmission. For the originally proposed$K$-user single-server/single shared link network model, CC yields an$O(K)$gain with respect to conventional uncoded caching with the same per-user memory. While several information-theoretic optimality results for a variety of problems and carefully crafted network topologies have been proved, the gains and suitability of CC for practical scenarios such as content streaming over existing wireless networks have not yet been fully demonstrated. In this work, we consider CC for on-demand video streaming over WLANs where multiple users are served simultaneously by multiple spatially distributed access points (AP). Users sequentially request video “chunks”. The CC scheme operates above the IP layer, leaving the underlying standard physical layer and MAC layer untouched. The cache placement is completely asynchronous and decentralized, and the users are placed at random over the network coverage area. For such a system, we consider the region of achievable long-term average delivery rate (defined as the number of video chunks delivered per unit of time) and study the per-user rate distribution under proportional fairness scheduling. We also consider reduced complexity scheduling strategies and compare them with standard state-of-the-art techniques such as conventional (uncoded) caching and collision avoidance by allocating APs on different sub-channels (i.e., frequency reuse).

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system em-ploying a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture that uses Orthogonal Frequency Division Multiplexing (OFDM) digital modulation. This framework takes raw signals as input and utilizes a Variational Autoencoder (VAE) followed by a regression network to output the spatial extent and location of extended targets. Owing to the HDA setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. The proposed solution is motivated as a remedy for the increasing computational complexity associated with high-resolution extended target estimation in multi-carrier digital modulations such as OFDM. In addition, it is well known that off-grid delay-Doppler shifts which are present in the doubly-dispersive channels in the high mobility scenarios expected in ISAC applications, exhibit leakage effects that adversely affect the parameter estimation performance. Due to the data-centric nature of the proposed method, these effects can be learned by the network. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation. 1

In this paper, we study the pulse shaping for delay-Doppler (DD) communications. We start with constructing a basis function in the DD domain following the properties of the Zak transform. Particularly, we show that the constructed basis functions are globally quasi-periodic while locally twisted-shifted, and their significance in time and frequency domains are then revealed. We further analyze the ambiguity function of the basis function, and show that fully localized ambiguity function can be achieved by constructing the basis function using periodic signals. More importantly, we prove that time and frequency truncating such basis functions naturally leads to approximate delay and Doppler orthogonalities, if the truncating windows are periodic within the support. Motivated by this, we propose a DD Nyquist pulse shaping scheme considering signals with periodicity. Finally, our conclusions are verified by using various strictly or approximately periodic pulses.

In this paper, we propose an integrated sensing and communications (ISAC) system where a base station (BS) equipped with an antenna array and a co-located radar receiver transmits data packets while simultaneously tracking the position of users. We restrict our attention to the simplest hardware architecture, where the beamforming array can generate beams from a discrete codebook and the receiver is equipped with a single analog to digital converter, thereby allowing for scalaronly measurements where angular information is lost. Under such restrictive constraints, the observation likelihoods are hard to model, which motivates us to learn them via neural networks. This learned likelihoods are then incorporated into a state space model where Bayesian filtering can be performed. We test our method in complicated road geometries and show that our tracker is capable of following high mobility users most of the time. Furthermore, when the track of a user is lost, it often takes only a few measurements until is is recovered, disposing of the need for time consuming beam alignment procedures.

To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview paper, we briefly describe the pathloss prediction problem, the provided datasets, the challenge task and the challenge evaluation methodology. Finally, we present the results of the challenge.

In this work, we propose a compressed sensing framework for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system employing a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture which uses Orthogonal Time Frequency Space (OTFS) digital modulation. In such a setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. OTFS is widely considered as a robust modulation to deal with the doubly-dispersive channel in the high mobility scenarios expected in ISAC applications, however it suffers from leakage effects in the presence of fractional Doppler/delay (i.e., off-grid) shifts. By taking the inherent structure of the leakage effect into consideration and casting the multi-block measurements in a Multiple Measurement Vector (MMV) setting, we develop the Joint Hierarchical Sparsity concept based on which, we formulate a soft-thresholding iterative parameter estimation framework. This framework exploits the jointly hierarchical structure of the MMV setting for improved (radar-) parameter estimation. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation.

In this paper we study the message detection (MD) problem for a chip-asynchronous random access (RA) multiple antennas receiver and a Rayleigh block-fading AWGN channel with a pure path delay. Although our approach can be used to detect users in a grant-free random access system, where each user sporadically and without waiting for a permission from a base station (BS) transmits a short message, we in particular consider unsourced random access (U-RA), where those messages are from a common codebook. The first and arguably the most important task of the U-RA receiver is to detect the list of transmitted messages. Due to the propagation through the considered channel, those messages are received as a superposition at the BS with delays. In order to reduce the overhead for timing synchronisation, which would be wasteful for short messages, in this work we provide an asynchronous operating mode for MD in U-RA. To do this, we show that when using a chip matched filter at the BS, the contribution of each transmitted message in the sampled received signal can be described as a superposition of two zero-padded versions of that message. We include this observation in the compressed sensing (CS) formulation of the MD problem, and solve it using the multiple measurement vectors approximate message passing (MMV-AMP) algorithm and a dedicated 2-level hierarchical sparsity (2-LHS) denoiser. Our numerical experiments show that the proposed scheme can accurately detect U-RA messages in the considered channel without any overhead for timing synchronization.

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to “effective channels”. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.

We present a novel and hardware-efficient space-fed antenna system that exploits the eigenmodes of the over-the-air propagation matrix from a small active multi-antenna feeder (AMAF) to a large reflective intelligent surface (RIS), both configured as standard linear arrays and placed in the near-field of each other. We demonstrate the flexibility of the proposed architecture by showing that it is capable of generating beam shapes for multiple applications, such as very narrow beams with low side lobes for space division multiple access communications, flat-top wide-angle patterns for short-range automotive sensing and sectorial beaconing, and monopulse-like patterns for radar angular tracking. A key parameter in our designs is the AMAF-RIS distance which must be optimized and it is generally much less than the Rayleigh distance. For a given AMAF array size, the optimal AMAF-RIS distance increases as a function of RIS size. The AMAF-RIS loss is almost exactly compensated by the larger aperture gain of the RIS, resulting in an almost constant RIS gain for increasing RIS sizes. This allows different beam angle selectivities to be selected for the same center beam gain. Active RF amplification is done at the AMAF only, thus resulting in a much lower hardware complexity than conventional active phased arrays for the same beamforming performance.

Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server, without revealing any other information about the local users' data. This paper follows a recent information theoretic secure aggregation problem with user dropouts, where the objective is to characterize the minimum communication cost from the$\mathrm{K}$users to the server during the model aggregation. All existing secure aggregation protocols let the users share and store coded keys to guarantee security. On the motivation that uncoded groupwise keys are more convenient to be shared and could be used in large range of practical applications, this paper is the first to consider uncoded groupwise keys, where the keys are mutually independent and each key is shared by a group of$\mathrm{S}$users. We show that if$\mathrm{S}$is beyond a threshold, a new secure aggregation protocol with uncoded groupwise keys, referred to as GroupSecAgg, can achieve the same optimal communication cost as the best protocol with coded keys. The experiments on Amazon EC2 show the considerable improvements on the key sharing and model aggregation times compared to the state-of-the art.

This paper studies a novel multi-access coded caching (MACC) model where the topology between users and cache nodes is a generalization of those already studied in previous work, such as combinatorial and cross-resolvable design topologies. Our goal is to minimize the worst-case transmission load in the delivery phase from the server over all possible user requests. By formulating the access topology as two classical combinatorial structures, t-design and t-group divisible design, we propose two classes of coded caching schemes for a flexible number of users, where the number of users can scale linearly, polynomially or exponentially with the number of cache nodes. In addition, our schemes can unify most schemes for the shared link network and unify many schemes for the multi-access network except for the cyclic wrap-around topology.

We consider fairness scheduling in a user-centric cell-free massive MIMO network, where L remote radio units, each with M antennas, serve $K \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where ${K_{{\text{act}}}} \approx \frac{{LM}}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users K in the network is usually much larger. This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. We impose throughput fairness among UEs with a scheduling approach aiming to maximize a concave component-wise non-decreasing network utility function of the per-user throughput rates. In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. Combined with fairness scheduling, this requires pilot and cluster reassignment at each scheduling slot, involving an enormous overhead of control signaling exchange between network entities. We propose a fixed pilot and cluster assignment scheme (independent of the scheduling decisions), which outperforms the baseline method in terms of UE throughput, while requiring much less control information exchange between network entities.

Distributed Linearly Separable Computation problem under the cyclic assignment is studied in this paper. It is a problem widely existing in cooperated distributed gradient coding, real-time rendering, linear transformers, etc. In a distributed computing system, a master asks N distributed workers to compute a linearly separable function from K datasets. The task function can be expressed as Kclinear combinations of K messages, where each message is the output of one individual function of one dataset. Straggler effect is also considered, such that from the answers of each Nrworker, the master should recover the task. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of (coded) messages which should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. Various distributed computing scheme were proposed in the literature with a well-known cyclic data assignment, but the (order) optimality of this problem remains open, even under the cyclic assignment. This paper proposes a new computing scheme with the cyclic assignment based on interference alignment, which is near optimal under the cyclic assignment.

To counter the large isotropic pathloss in Millimeter-wave (mmWave) communications, high beamforming gain by means of large antenna arrays is required. Joint Communication and Sensing (JCAS) is predicted to be a major feature of future communication systems, but suitable channel models and performance metrics are still in development. This paper investigates the information-theoretic limits of JCAS using a channel model proposed in [1], which consists of a binary state-dependent channel with unit-delayed feedback and an in-block memory (iBM) [2] of the fixed block length. This model is sufficiently simple to treat, yet captures some fundamental aspects of beam acquisition (BA) where the feedback models the backscatter signal as in a radar system, thus fitting the paradigm of JCAS. When the transmission cost is bounded on average, we simplify the capacity computation as an optimization problem by exploring a small set of optimal average input costs at each channel use based on feedback. The converse proof inspires a capacity-achieving strategy that uniformly explores a given number of beam directions under the cost constraint. The sensing performance is characterized in terms of the state estimation error. We present the minimum distortion without communication constraint as another optimization problem, which can be solved by the successive convex approximation (SCA) method similar to the capacity computation problem. Numerical examples are provided to illustrate the performance of the proposed strategies and show a very small gap between the estimation error achieved by the capacity-achieving strategy and the minimum possible error. Hence, for the model at hand sensing comes essentially "for free", i.e., there exists a JCAS strategy that pays very little in terms of sensing performance while being optimal with respect to the communication rate.

Integrated sensing and communications (ISAC) is recognized as a key enabling technology for future wireless networks. To shed light on the fundamental performance limits of ISAC systems, this paper studies the deterministic-random tradeoff between sensing and communications (S&C) from a rate-distortion perspective under vector Gaussian channels. We model the ISAC signal as a random matrix that carries information, whose realization is perfectly known to the sensing receiver, but is unknown to the communication receiver. We characterize the sensing mutual information conditioned on the random ISAC signal, and show that it provides a universal lower bound for distortion metrics of sensing. Furthermore, we prove that the distortion lower bound is minimized if the sample covariance matrix of the ISAC signal is deterministic. We then offer our understanding of the main results by interpreting wireless sensing as non-cooperative source-channel coding, and reveal the deterministic-random tradeoff of S&C for ISAC systems. Finally, we provide sufficient conditions for the achievability of the distortion bound by analyzing specific examples.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

This paper investigates the information-theoretic secrecy problem for a K-user discrete memoryless (DM) multiple-access wiretap (MAC-WT) channel. Instead of using the weak secrecy criterion characterized by information leakage rate, we adopt the strong secrecy metric defined by information leakage to better protect the confidential information. We provide an achievable rate region and prove its achievability by providing a coding scheme and analyzing the output statistics in terms of (average) variational distance. We show that the rate region obtained in previous works on the subject is a special case of ours. We also show that the achievability proof in such works is incomplete, because it is assumed that certain inequalities hold while they may not in some cases. We solve this problem by constructing an inequality structure for the rates of all users’ secret and redundant messages, and analyzing the conditions required to maintain this structure.

Coded distributed computing (CDC), originally proposed by Li et al., leverages coded multicast messages to exchange computed intermediate values among the distributed computing nodes, such that the overall communication load could be reduced by a factor of r, the number of input files assigned to each node. However, in the original CDC framework, each output function/task is composed of intermediate values from all input files. In this paper, we propose a new CDC problem for sparse functions with structured support, where each output function depends on a subset of the input files. For a symmetric structured support for which the input files are divided into G equal-length batches and each output function depends on the same number of G′batches, we propose a novel CDC scheme that is strictly better by a factor G/G′than directly employing the original CDC scheme in the considered problem. Furthermore, by proposing a new converse bound, we prove that the communication load of the proposed CDC scheme is order optimal within a constant multiplicative factor of 6.

Sensing capabilities as an integral part of the network have been identified as a novel feature of sixth-generation (6G) wireless networks. As a key driver, millimeter-wave (mmWave) communication largely boosts speed, capacities, and connectivity. In order to maximize the potential of mmWave communication, precise and fast beam acquisition (BA) is crucial, since it compensates for a high pathloss and provides a large beamforming gain. Practically, the angle-of-departure (AoD) remains almost constant over numerous consecutive time slots, the backscatter signal experiences some delay, and the hardware is restricted under the peak power constraint. This work captures these main features by a simple binary beam-pointing (BBP) channel model with in-block memory (iBM) [1], peak cost constraint, and one unit-delayed feedback. In particular, we focus on the sensing capabilities of such a model and characterize the performance of the BA process in terms of the Hamming distortion of the estimated channel state. We encode the position of the AoD and derive the minimum distortion of the BBP channel under the peak cost constraint with no communication constraint. Our previous work [2] proposed a joint communication and sensing (JCAS) algorithm, which achieves the capacity of the same channel model. Herein, we show that by employing this JCAS transmission strategy, optimal data communication and channel estimation can be accomplished simultaneously. This yields the complete characterization of the capacity-distortion tradeoff for this model.

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of non-uniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.

In this paper, we develop two active sensing strategies for a millimeter wave (mmWave) band Integrated Sensing and Communication (ISAC) system adopting a realistic hybrid digital-analog (HDA) architecture. To maintain a desired SNR level, initial beam acquisition (BA) must be established prior to data transmission. In the considered setup, a Base Station (BS) transmitter (Tx) transmits data via a digitally modulated waveform and a co-located radar receiver simultaneously performs radar estimation from the backscattered signal. In this BA scheme a single common data stream is broadcast over a wide angular sector such that the radar receiver can detect the presence of not yet acquired users and perform coarse parameter estimation (angle of arrival, time of flight, and Doppler). As a result of the HDA architecture, we consider the design of multi-block adaptive RF-domain "reduction matrices" (from antennas to RF chains) at the radar receiver, to achieve a compromise between the exploration capability in the angular domain and the directivity of the beamforming patterns. Our numerical results demonstrate that the proposed approaches are able to reliably detect multiple targets while significantly reducing the initial acquisition time.

The pilot contamination in cell-free massive multiple-input-multiple-output (CF-mMIMO) must be addressed for accommodating a large number of users. In previous works, we have investigated a decontamination method called subspace projection (SP). The SP separates interference from co-pilot users by using the orthogonality of the principal components of the users’ channel subspaces. For CF-mMIMO system with SP, non-overloaded pilot assignment (PA) and overloaded PA can be considered. Non-overloaded PA, where each radio unit (RU) does not assign the same pilot to different users, limits the number of associated RUs per each UE and this reduces the potential spectral efficiency (SE) of the system. On the other hand, non-overloaded PA reduces channel estimation error induced by contamination. This paper compares non-overloaded PA and overloaded PA, and introduces overloaded PA methods adjusted for the decontamination in order to improve the sum SE of CF systems. Numerical simulations show that the overloaded PA methods give higher SE than that of non-overloaded PA at a high user density scenario.

We proposeSwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of$N \in \mathbb {N}$distributed users, each of size$L \in \mathbb {N}$, trained on their local data, in a privacy-preserving manner.SwiftAgg+can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most$D=o(N)$dropout users,SwiftAgg+achieves a per-user communication load of$\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$symbols and a server communication load of$\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$symbols, with a worst-case information-theoretic security guarantee, against any subset of up to$T=o(N)$semi-honest users who may also collude with the curious server. Moreover, the proposedSwiftAgg+allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for$T< N-D$and for any$K\in \mathbb {N}$,SwiftAgg+can achieve the server communication load of$\left({1+\frac {T}{K}}\right)L$symbols, and per-user communication load of up to$\left({1+\frac {T+D}{K}}\right)L$symbols, where the number of pair-wise active connections in the network is$\frac {N}{2}(K+T+D+1)$.

Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).

Motivated by beamformed millimeter-wave (mmWave) communication, we consider the optimal tradeoff between reliable communication rate and state estimation error for a new state-dependent channel model with in-block memory referred to as the binary beam-pointing (BBP) channel. The multiantenna base station (BS) uses a finite beamforming codebook (i.e., discrete beam directions) and associates the target user to the most favorable beam, i.e., the beam directed along the strongest propagation path from BS to the user in 5G and IEEE 802.11ad mmWave communication systems. We model the target user’s Angle-of-Departure (AoD) as the state of a state-dependent channel. Since the AoD remains almost constant over several consecutive time slots, the channel has memory. In addition, we assume the BS receives implicit causal feedback (e.g., modeling a backscatter signal as in radar) and consider a joint communication and sensing (JCAS) problem where the BS is also interested in explicitly estimate the channel state (quantized AoD). We derive a closed-form solution to the capacity of the BBP channel model under the peak input cost constraint. Under the average constraint, we present an implicit capacity result, where the capacity is given as the solution of an optimization problem, and we provide an algorithm for its numerical computation. Finally, for the JCAS problem at hand, we provide the minimum distortion under the peak input constraint and show that this coincides with that obtained by the capacity-achieving strategy. This completely characterizes the capacity-distortion tradeoff for the BBP channel under peak input constraint. For the average constraint case, numerical results show that our capacity-achieving strategy yields a state estimation error very close to the theoretical minimum, showing the near-optimality of the proposed strategy for the JCAS problem under the average cost constraint.

This paper studies the fundamental limits of the shared-link coded caching problem with correlated files, where a server with a library of${\mathsf N}$files communicates with${\mathsf K}$users who can locally cache${\mathsf M}$files. Given an integer${\mathsf r}\in [{\mathsf N}]$, correlation is modelled as follows: each${\mathsf r}$-subset of files contains a unique common block. The tradeoff between the cache size and the average transmitted load over the uniform demand distribution is studied. First, a converse bound under the constraint of uncoded cache placement (i.e., each user directly stores a subset of the library bits) is derived. Then, a caching scheme for the case where every user demands a distinct file (possible for${\mathsf N}\geq {\mathsf K}$) is shown to be optimal under the constraint of uncoded cache placement. This caching scheme is further proved to be decodable and optimal under the constraint of uncoded cache placement when (i)${\mathsf K} {\mathsf r} {\mathsf M}\leq 2 {\mathsf N}$or${\mathsf K} {\mathsf r} {\mathsf M}\geq ({\mathsf K}-1) {\mathsf N}$or${\mathsf r}\in \{1,2, {\mathsf N}-1, {\mathsf N}\}$, and (ii) when the number of distinct demanded files is no larger than four. Finally, a new delivery scheme based on interference alignment which jointly serves the users’ demands is shown to be order optimal to within a factor of 2 under the constraint of uncoded cache placement. As an extension, the above exact and order optimal results can be extended to the worst-case load. As by-products, an extension of the proposed scheme for${\mathsf M}= {\mathsf N}/ {\mathsf K}$is shown to reduce the load of state-of-the-art schemes for the coded caching problem where the users can request multiple files; the proposed scheme for distinct demands can be extended to the coded distributed computing problem with a central server, which achieves the optimal transmission load over the binary field.

Caching prefetches some library content at users’ memories during the off-peak times (i.e., placement phase), such that the number of transmissions during the peak-traffic times (i.e., delivery phase) are reduced. A coded caching strategy was originally proposed by Maddah-Ali and Niesen (MN) leading to a multicasting gain compared to the conventional uncoded caching, where each message in the delivery phase is useful to multiple users simultaneously. The load of the MN scheme is optimal under uncoded placement, but the subpacketization level is$O\left({2^{H\left({\frac {M}{N}}\right)K}}\right)$, where$K$is the number of users,$\frac {M}{N}$is the memory ratio of each user and$H\left({\frac {M}{N}}\right)$is the binary entropy at$\frac {M}{N}$. In order to reduce the subpacketization while retaining the multicast opportunities in the delivery phase, Yan et al. proposed a combinatorial structure called placement delivery array (PDA) to design coded caching schemes with uncoded placement and clique-covering delivery. In this paper, we consider the coded caching problem from the perspective of PDA. First we propose a Cartesian product method, which constructs an$mK_{1}$-user PDA based on the piece-wise$m$-fold Cartesian product of a special$K_{1}$-user PDA (called a base PDA) while keeping the memory ratio and load unchanged. Since a base PDA must satisfy some restrictive constraints, we propose a transformation from any existing PDA to a base PDA, which makes the Cartesian product method applicable to any existing PDA. As applications of the Cartesian product method, three new coded caching schemes (i.e., Schemes A, B, C) are obtained, whose performance are validated via analytical and numerical comparisons. It is worth noting that Scheme A is asymptotically optimal under uncoded placement, in the sense that the achieved coded caching gain is only decreased by 1 with respect to the coded caching gain of the MN scheme. When the number of users is$K=mq$and memory ratio is$\frac {z}{q}$, the needed subpacketization is at most$O\left ({\sqrt {\frac {K}{q}}2^{-\frac {K}{q}}}\right)$of that of the MN scheme for large$m$, which implies that for fixed number of users and memory ratio, when we choose$q$and$z$coprime, the subpacketization can be reduced the most, since the value of$\frac {K}{q}$is maximized. Moreover, Scheme A works for arbitrary memory ratio.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for the next-generation wireless networks, which provides significant performance gains over individual sensing and communications (S&C) systems via the shared use of wireless resources. The characterization of the S&C performance tradeoff is at the core of the theoretical foundation of ISAC. In this paper, we consider a point-to-point (P2P) ISAC model under vector Gaussian channels, and propose to use the Cramér-Rao bound (CRB)-rate region as a basic tool for depicting the fundamental S&C tradeoff. In particular, we consider the scenario where a unified ISAC waveform is emitted from a dual-functional ISAC transmitter (Tx), which simultaneously communicates information to a communication receiver (Rx) and senses targets with the help of a sensing Rx. In order to perform both S&C tasks, the ISAC waveform is required to be random to convey communication information, with realizations being perfectly known at both the ISAC Tx and the sensing Rx as a reference sensing signal as in typical radar systems. In this context, we treat the ISAC waveform as a random but known nuisance parameter in the sensing signal model, and define a Miller-Chang type CRB for the analysis of the sensing performance. As the main contribution of this paper, we characterize the S&C performance at the two corner points of the CRB-rate region, namely,$P_{\mathrm{ SC}}$indicating the maximum achievable communication rate constrained by the minimum CRB, and$P_{\mathrm{ CS}}$indicating the minimum achievable CRB constrained by the maximum communication rate. In particular, we derive the high-SNR communication capacity at$P_{\mathrm{ SC}}$, and provide lower and upper bounds for the sensing CRB at$P_{\mathrm{ CS}}$. We show that these two points can be achieved by the conventional Gaussian signalling and a novel strategy relying on the uniform distribution over the set of semi-unitary matrices, i.e., the Stiefel manifold, respectively. Based on the above-mentioned analysis, we provide an outer bound and various inner bounds for the achievable CRB-rate regions. Our main results reveal a two-fold tradeoff in ISAC systems, consisting of the subspace tradeoff (ST) and the deterministic-random tradeoff (DRT) that depend on the resource allocation and data modulation schemes employed for S&C, respectively. Within this framework, we examine the state-of-the-art ISAC signalling strategies and study a number of illustrative examples, which are validated through numerical simulations.

We consider the cache-aided multiple-input single-output (MISO) broadcast channel, which consists of a server with$L$antennas and$K$single-antenna users, where the server contains$N$files of equal length and each user is equipped with a local cache of size$M$files. Each user requests an arbitrary file from library. The objective is to design a coded caching scheme based on uncoded placement and one-shot linear delivery, to achieve the maximum sum Degree-of-Freedom (sum-DoF) with low subpacketization. It was shown in the literature that under the constraint of uncoded placement and one-shot linear delivery, the maximum sum-DoF is$\min \left\{{L+\frac {KM}{N},K}\right\}$. However, previously proposed schemes for this setting incurred either an exponential subpacketization order in$K$, or required specific conditions in the system parameters$L$,$K$,$M$and$N$. In this paper, we propose a new combinatorial structure called multiple-antenna placement delivery array (MAPDA). Based on MAPDA and Latin square, the first proposed scheme achieves the maximum sum-DoF$\min \left\{{L+\frac {KM}{N},K}\right\}$with the subpacketization of$K$when$\frac {KM}{N}+L=K$. Subsequently, for the general case we propose a transformation approach to construct an MAPDA from any$g$-regular PDA (a class of placement delivery arrays for the shared link caching problem where each integer in the array occurs$g$times). When$g$-regular PDA corresponds to the Maddah-Ali and Niesen scheme, the resulting MAPDA yields the maximum sum-DoF$\min \left\{{L+\frac {KM}{N},K}\right\}$with reduced subpacketization compared to the existing schemes. The general scheme can be extended to the multiple independent single-antenna transmitters (servers) corresponding to the cache-aided interference channel proposed by Naderializadeh et al. and the scenario of transmitters equipped with multiple antennas.

This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. Consequently, we propose a search algorithm that solves the problem at hand while reducing the variable search space significantly. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement.

We consider cellular network caching with network-wide Orthogonal Multipoint Multicast (OMPMC) delivery. We apply a probabilistic model for content placement at the Base Stations (BSs). Content is delivered with multipoint multicast operating in file-specific orthogonal resources: all BSs caching a distinct file synchronously multicast it to requesting users in a dedicated resource. For a network modeled as a Poisson Point Process (PPP), an expression for the outage probability is derived. The outage-minimizing cache policy is found from a joint constrained optimization problem over cache placement and resource allocation. We devise principles by which the solution in one propagation environment can be generalized to another. To reduce computational complexity, we obtain a sub-optimal solution based on convex relaxation. We obtain an upper bound of the gap between the optimal and sub-optimal solutions. We compare the outage performance of OMPMC with delivery polices from the literature. Simulation results show that exploiting OMPMC with optimal cache placement and resource allocation outperforms single point cache delivery policies with a wide margin.

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In Discovery mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and performs coarse estimation of their parameters (angle of arrival, range, and velocity). In Tracking mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a beam-space approach where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramér-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

We study the problem of providing channel state information (CSI) at the transmitter in multi-user “massive” MIMO systems operating in frequency division duplexing (FDD). The wideband MIMO channel is a vector-valued random process correlated in time, space (antennas), and frequency (subcarriers). The base station (BS) broadcasts periodically$\beta _{\mathrm{ tr}}$pilot symbols from its$M$antenna ports to$K$single-antenna users (UEs). Correspondingly, the$K$UEs send feedback messages about their channel state using$\beta _{\mathrm{ fb}}$symbols in the uplink (UL). Using results from remote rate-distortion theory, we show that, as${\sf snr}\to \infty $, the optimal feedback strategy achieves a channel state estimation mean squared error (MSE) that behaves as$\Theta {(}1)$if$\beta _{\mathrm{ tr}} < r$and as$\Theta \left ({{\sf snr}^{-\alpha }}\right)$when$\beta _{\mathrm{ tr}} \ge r$, where$\alpha = \min (\beta _{\mathrm{ fb}}/r, 1)$, where$r$is the rank of the channel covariance matrix. The MSE-optimal rate-distortion strategy implies encoding of long sequences of channel states, which would yield completely stale CSI and therefore poor multiuser precoding performance. Hence, we consider three practical “one-shot” CSI strategies with minimum one-slot delay and analyze their large-SNR channel estimation MSE behavior. These are: (1) digital feedback via entropy-coded scalar quantization (ECSQ), (2) analog feedback (AF), and (3) local channel estimation at the UEs via compressed sensing and digital feedback. These schemes have different requirements in terms of knowledge of the channel statistics at the UE and at the BS. In particular, the latter strategy requires no statistical knowledge and is closely inspired by a CSI feedback scheme currently proposed in 3GPP standardization. It is shown that ECSQ achieves optimal MSE at the price of a slight increase in feedback rate which vanishes for large SNR. AF achieves the optimal MSE decay rate of$\Theta ({\sf snr}^{-1})$whenever$\beta _{\mathrm{ tr}},\beta _{\mathrm{ fb}} \ge r$but is sub-optimal if$\beta \ge r$and$\beta _{\mathrm{ fb}} < r$. The 3GPP-inspired scheme is shown, via numerical simulations, to achieves performance similar to ECSQ and AF when the multipath channel is sufficiently sparse in the angle-delay domain, but suffers from a large performance gap if this requirement is not met.

This paper considers the implementation of compressive sensing (CS) approaches for beam alignment (BA) in multiuser millimeter wave (mmWave) MIMO systems. We particularly consider wideband time-varying channels in the practical low SNR regime. We examine two different time scales for beam-switching in the BA training phase at both the base station (BS) and the user equipment (UE). We also compare different time scales for running the CS algorithm at the UE, with their corresponding overhead and complexity. We propose an overarching trial-based protocol that re- initializes the BA process at particular times. We also propose a new approach to designing the CS sensing matrix (SM), based on a deterministic construction. Rows of our proposed SM are Kronecker product decomposable, making it ideal for the BA problem. We show that when block-based beam switching is employed in combination with running the CS algorithm Every Epoch (CS-EE), our proposed SM gives superior performance compared to the other approaches. Moreover, our proposed overarching trial-based protocol enhances the performance even further. We also show that running the CS algorithm Every Block (CS-EB) outperforms CS-EE at the cost of higher complexity and overhead.

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.

We consider a hybrid delivery scheme for streaming content, combining cache-enabled Orthogonal Multipoint Multicast (OMPMC) and on-demand Single-Point Unicast (SPUC) transmissions for heterogeneous networks. The OMPMC service transmits cached files through the whole network to interested users, and users not being satisfied by this service are assigned to the SPUC service to be individually served. The SPUC fetches the requested files from the core network and unicasts them to UEs using cellular beamforming transmissions. We optimize the delivery scheme to minimize the average resource consumption in the network. We formulate a constrained optimization problem over the cache placement and resource allocation of the OMPMC component, as well as the multi-user beamforming scheme of the SPUC component. We apply a path-following method to find the optimal traffic offloading solution. The solutions portray a contrast between the total amount of consumed resources and service outage probability. Simulation results show that the hybrid scheme provides a better tradeoff between the amount of network-wide consumed resources and the service outage probability, as compared to schemes from the literature.

In this paper, we investigate the delay-Doppler (D-D) domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output orthogonal time frequency space (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain inter-ference in a symbol-by-symbol manner. We also demonstrate the theoretical results on the sum-rate performance of the proposed scheme. In particular, we show that the sum-rate of the proposed scheme increases logarithmically with the number of antennas, while increases linearly with the number of users. Our numerical results align well with our findings and also verify the effectiveness of the proposed scheme.

In this paper, we study the potentials of spatially-spread orthogonal time frequency space (SS-OTFS) modulation for integrated sensing and communication (ISAC) transmissions. The most favourable feature of SS-OTFS modulation is that it forms beams according to a pre-determined angular grid, which is different from the conventional beamforming, where dedicated beams are formed according to the a priori information on the angle of departures (AoDs). According to the delay-Doppler domain channel characteristics, we first derive the input-output relationships for SS-OTFS-enabled ISAC system in a typical downlink multi-user MIMO (MU-MIMO) scenario. Based on those relationships, we further study the angular domain channel features and discuss the system design. Our numerical results have demonstrated the advantages of the proposed scheme over the conventional beamforming counterpart in terms of the signal-to-interference-plus-noise ratio (SINR).

This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on Time of Arrival (ToA) or Angle of Arrival information. In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit (CPU), which may be located in the cloud. Alternatively, the localization can be performed locally at the user. Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps. The proposed method does not require pre-sampling of the environment; and is suitable for real-time applications, thanks to the RadioUNet, a neural network-based radio map estimator. We also introduce two datasets that allow numerical comparisons of RSS and ToA methods in realistic urban environments.

In the coded caching literature, the notion of privacy is considered only against demands. On the motivation that multi-round transmissions almost appear everywhere in real communication systems, this paper formulates the coded caching problem with private demands and caches. Only one existing private caching scheme, which is based on introducing virtual users, can preserve the privacy of demands and caches simultaneously, but at the cost of an extremely large subpacketization exponential in the product of the number of users (K) and files (N) in the system. In order to reduce the subpacketization while satisfying the privacy constraints, we propose a novel approach which constructs private coded caching schemes through private information retrieval (PIR). Based on this approach, we propose novel schemes with private demands and caches which have a subpacketization level in the order exponential with K instead of NK in the virtual user scheme. As a by-product, for the coded caching problem with private demands, a private coded caching scheme could be obtained from the proposed approach, which generally improves the memory-load tradeoff of the private coded caching scheme by Yan and Tuninetti.

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N distributed users, each of size L, trained on their local data, in a privacy-preserving manner. Compared with state-of-the-art secure aggregation protocols, SwiftAgg significantly reduces the communication overheads without any compromise on security. Specifically, in presence of at most D dropout users, SwiftAgg achieves a server communication load of (T +1)L and a per-user communication load of up to (T+D+1)L, with a worst-case information-theoretic security guarantee, against any subset of up to T semi-honest users who may also collude with the curious server. The key idea of SwiftAgg is to partition the users into groups of size T+D+1, then in the first phase, secret sharing and aggregation of the individual models are performed within each group, and then in the second phase, model aggregation is performed on T +D+1 sequences of users across the groups. If a user in a sequence drops out in the second phase, the rest of the sequence remains silent. This design allows only a subset of users to communicate with each other, and only the users in a single group to directly communicate with the server, eliminating the requirements of 1) all-to-all communication network across users; and 2) all users communicating with the server, for other secure aggregation protocols. This helps to substantially slash the communication costs of the system.

We consider the problem of estimating the fading coefficients of a frequency-selective, spatially correlated channel via Downlink (DL) training and Uplink (UL) feedback in frequency division duplexing (FDD) massive MIMO systems. Using ratedistortion theory, we derive optimal bounds on the achievable channel state estimation error in terms of the number of training pilots in DL (βtr) and feedback dimension in UL (βfb), with random, spatially isotropic pilots. It is shown that when the number of training pilots exceeds the channel covariance rank (r), the optimal rate-distortion feedback strategy achieves an estimation error decay of ΘpSNR−αq in estimating the channel state, where α = minpβfb{r,1q is the so-called quality scaling exponent (QSE). We then discuss an "analog" feedback strategy, showing that it achieves the optimal QSE for a wide range of training and feedback dimensions with no channel covariance knowledge and simple signal processing at the user side. Our findings are supported by numerical simulations comparing these strategies in terms of channel state mean squared error and achievable ergodic sum-rate in DL with zero-forcing precoding.

Hachem et al. formulated a multiaccess coded caching model which consists of a central server connected to K users via an error-free shared link, and K cache-nodes. Each cache-node is equipped with a local cache and each user can access L neighbouring cache-nodes in a cyclic wraparound fashion. In this paper, we take the privacy of the users’ demands into consideration, i.e., each user, while retrieving its own demanded file, cannot obtain any information on the demands of the other users. By storing some private keys at the cache-nodes, we develop a novel transformation approach to turn any non-private coded caching scheme (satisfying some constraints) into a private one.

Caching is an efficient technique to reduce the peak-time traffic by prefetching some library content at users’ memories during the off-peak hours. Maddah-Ali and Niesen (MN) proposed the first coded caching scheme, which achieves a multicasting gain over the conventional uncoded caching. However, its high subpacketization makes it impractical. In order to reduce the subpacketization while retaining the multicast opportunities, Yan et al. proposed a combinatorial structure called placement delivery array (PDA) to design coded caching schemes. In this paper, we propose a new framework for constructing a PDA for mK1users, by taking the m-fold Cartesian product of a PDA for K1users. By applying the proposed framework to the MN scheme, a new coded caching scheme is obtained, which works for any number of users and any memory regime. While reducing the coded caching gain by only one, the needed subpacketization is at most $O\left( {\sqrt {\frac{K}{q}} {2^{ - \frac{K}{q}}}} \right)$ of that of the MN scheme, where K is the number of users, 0 < z/q < 1 is the memory ratio of each user, and q, z are coprime.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and Rayleigh fading. Due to the bottleneck constraint, it is impossible for the relays to inform the destination node of the perfect channel state information (CSI) in each realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also three achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

This paper considers the cache-aided multiple-input single-output (MISO) broadcast channel (BC) consisting of one server and K users, where the server with L antennas accesses to N files and each user with single antenna has a cache of M files. The objective of this problem is to maximize the sum Degree-of-Freedom (sum-DoF) of the system. It was proved that under uncoded cache placement and one-shot zero-forcing (ZF) delivery, the maximum sum-DoF is L + KM/N. However, previously proposed schemes achieving the maximum sum-DoF either require an exponential order of K or work for some limited cases. In this paper, we propose a new combinatorial structure called multiple-antenna placement delivery array (MAPDA), which generalizes the coded caching schemes under uncoded cache placement and one-shot ZF delivery. We then propose two schemes (for the case $\frac{{KM}}{N} + L = K$ and the general case, respectively), which can achieve the maximum sum-DoF with reduced subpacketization with respect to the existing schemes. The first scheme is based on the Latin square, while the second scheme is obtained by an approach which transforms a class of schemes for the original shared-link coded caching model to the MISO system.

This paper formulates the multi-access coded caching (MACC) problem under the two-dimensional (2D) topology, which is a generalization of the one-dimensional (1D) MACC problem originally considered by Hachem et al. The novel 2D MACC system includes a server containing N files, K1×K2cache-nodes (each of size M units) placed on a grid with K1rows and K2columns, and K1×K2cache-less users, each of which accesses to L2nearby cache-nodes. More precisely, focus on any row (or column) of the grid, each user can access L consecutive cache-nodes in a cyclic wrap-around fashion, referred to as row (or column) 1D MACC problem in the 2D MACC system. The users are connected to the server through an error-free shared link, while they can also retrieve the content stored at the accessible cache-nodes without cost. Our objective is to minimize the worst-case transmission load among all possible users’ demands. This work proposes a baseline scheme firstly, which directly extends an existing 1D MACC scheme to the 2D model by using a Minimum Distance Separable (MDS) code. Then two improved schemes are designed. In the grouping scheme, we divide the cache-nodes and users into L2groups by their positions, such that any two users in the same group do not share any cache-node, and then utilize the seminal shared-link coded caching scheme proposed by Maddah-Ali and Niesen for each group. Then we propose the hybrid scheme, consisting in a highly non-trivial way to construct a 2D MACC scheme by using two 1D MACC problems under vertical and horizontal projections.

We consider the cache-aided multiuser private information retrieval (MuPIR) problem with a focus on the special case of two messages, two users and arbitrary number of databases where the users have distinct demands of the messages. We characterize the optimal memory-load trade-off for the considered MuPIR problem by proposing a novel achievable scheme and a tight converse. The proposed achievable scheme uses the idea of cache-aided interference alignment (CIA) developed in the literature by the same authors. The proposed converse uses a tree-like decoding structure to incorporate both the decodability and privacy requirements of the users. While the optimal characterization of the cache-aided MuPIR problem is challenging in general, this work provides insight into understanding the general structure of the cache-aided MuPIR problem.

Due to the increasing interest for wireless communications at higher frequencies, line-of-sight multiple-input multiple-output (LoS MIMO) systems are expected to be deployed in many future applications. In such systems, maximum capacity is achieved with optimal antenna placement at the transmit (Tx) and receive (Rx) arrays. Regarding uniform circular arrays (UCAs), alignment of the Tx and Rx arrays is also necessary to extract full capacity. However, there are scenarios where the arrays are tilted and hence, alignment may not always be guaranteed. Recently, it was shown that the capacity performance of a tilted array is determined by its projection onto the plane perpendicular to the line between the Tx and Rx. So, an elliptical array can be designed so that when tilted, such projection results in an aligned UCA. We first propose an array design consisting of several concentric elliptical subarrays, where each subarray is selected to compensate a range of tilts, enabling large capacity over a range of tilting angles. Moreover, we propose the use of a more flexible uniform planar array with antenna selection to approximate the elliptical subarrays in order to compensate different tilts.

Cell-free massive MIMO is a variant of multiuser MIMO and massive MIMO, in which the total number of antennas LM is distributed among the L remote radio units (RUs) in the system, enabling macrodiversity and joint processing. Due to pilot contamination and system scalability, each RU can only serve a limited number of users. Obtaining the optimal number of users simultaneously served on one resource block (RB) by the L RUs regarding the sum spectral efficiency (SE) is not a simple challenge though, as many of the system parameters are intertwined. For example, the dimension $\tau_{p}$ of orthogonal Demodulation Reference Signal (DMRS) pilots limits the number of users that an RU can serve. Thus, depending on $\tau_{p}$, the optimal user load yielding the maximum sum SE will vary. Another key parameter is the users’ uplink transmit power $P_{\mathrm{tx}}^{\mathrm{ue}}$, where a trade-off between users in outage, interference and energy inefficiency exists. We study the effect of multiple parameters in cell-free massive MIMO on the sum SE and user outage, as well as the performance of different levels of RU antenna distribution. We provide extensive numerical investigations to illuminate the behavior of the system SE with respect to the various parameters, including the effect of the system load, i.e., the number of active users to be served on any RB. The results show that in general a system with many RUs and few RU antennas yields the largest sum SE, where the benefits of distributed antennas reduce in very dense networks.

In this paper, we empirically study the behavior of the near-field over-the-air (NF-OTA) propagation matrix between two antenna arrays, with applications to reflective intelligent surface (RIS) based over-the-air beamforming antenna for very high carrier frequencies (mmWaves and sub-THz) and very large signal bandwidth. The NF-OTA propagation matrix T is a deterministic complex matrix which depends on the feeder array and the RIS array sizes and their geometry. We conduct numerical investigations of the rank of T and its left singular vectors, which play an important role in determining the OTA beamforming capability of the whole architecture.

We present mean communication rate loss (MCRL) values due to the beam cusping phenomenon inherent to grid of beams based wireless systems, which are widely used/proposed in millimeter-wave and sub-THz bands. We consider array antenna elements with non-zero aperture and axisymmetric radiation pattern with a certain directivity, unlike the ideal isotropic antenna elements widely considered in theoretical papers on array processing and RF beamforming. The array antenna elements have a half power beam width of ninety degrees in this work which is typical of commonly used microstrip patch antenna elements. We perform Monte Carlo numerical experiments to obtain MCRL values for different spatial dimensions of multiple input multiple output (MIMO) systems, different angular fields of view, and different beamforming codebook sizes. We show that increasing the beam grid density beyond a certain threshold does not help and therefore a certain cusping loss is unavoidable even for continuous beam steering with directive antenna elements. Further, we also show the quantitative impact on the MCRL values as the axisymmetric radiation pattern directivity of the modelled antenna element is increased.

We consider an optimal cache-placement-and-delivery-policy where traffic is offloaded from Single-Point Unicast (SPUC) service by using network-level Orthogonal Multipoint Multicast (OMPMC) scheme. The files are classified into two sets. The most popular files are cached at the BSs using a probabilistic approach and are served by OMPMC. The remaining files are fetched from the core network on demand and served by SPUC. Optimal compound scheme is analyzed, based on resource allocation between OMPMC and multi-antenna SPUC schemes. If a user is not able to successfully receive the requested file due to its experienced signal-to-interference-plus-noise ratio, its request is in outage. A closed-form expression is derived for the total outage probability based on stochastic geometry for the compound scheme. An optimization problem is formulated to design the caching policy for the compound scheme. The optimal solution to this problem is obtained by finding optimal cache placement, bandwidth allocation, and file classification. Simulation results show that the compound scheme outperforms other caching schemes in terms of the total outage probability.

We consider a scalable user-centric wireless network with dynamic cluster formation as defined by Björnsson and Sanguinetti. After having shown the importance of dominant channel subspace information for uplink (UL) pilot decontamination and having examined different UL combining schemes in our previous work, here we investigate precoding strategies for the downlink (DL). Distributed scalable DL precoding and power allocation methods are evaluated for different antenna distributions, user densities and UL pilot dimensions. We compare distributed power allocation methods to a scheme based on a particular form of UL-DL duality which is computable by a central processor based on the available partial channel state information. The new duality method achieves almost symmetric "optimistic ergodic rates" for UL and DL while saving considerable computational complexity since the UL combining vectors are reused as DL precoders.

We consider the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we treat the slow fading scenario where the coherence blocklength is large compared to the number of active users and a message can be transmitted in a single fading coherence block. Unsourced random access refers to a form of grant-free random access where users are constrained to use the same codebook and therefore are a priori indistinguishable. The receiver must recover the list of transmitted messages up to permutations. In this paper, we propose an approach based on splitting the user messages into two parts. First, a small block of bits selects a relatively short codeword from a common “pilot” codebook. Then the remaining message bits are encoded by a standard block code for the Gaussian channel. The receiver makes use of a multiple measurement vector approximate message passing (MMV-AMP) algorithm to estimate the active user channels from the “pilot” part, and then uses the estimated channels to perform coherent maximum ratio combining (MRC) to decode the second part. We provide an accurate closed-form approximated analysis of the proposed scheme. Furthermore, we analyze the MRC decoding when successive interference cancellation is performed over groups of users, striking an attractive tradeoff between complexity and performance. Finally, we investigate the impact of power control policies, taking into account the unique nature of massive random access. As a byproduct, we also present an extension of the MMV-AMP algorithm which allows pathloss coefficients to be treated as deterministic unknowns by performing maximum likelihood estimation in each step of the MMV-AMP algorithm.

This paper investigates the secrecy achievable region of multiple access wiretap (MAC-WT) channels where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver (or Bob for brevity) but only the confidential messages need to be protected from the eavesdropper (Eve). We first consider a discrete memoryless (DM) MAC-WT channel where both Bob and Eve jointly decode their interested messages. By using random coding, we find an achievable rate region, within which perfect secrecy can be realized, i.e., all users can communicate with Bob with arbitrarily small probability of error, while the confidential information leaked to Eve tends to zero. Due to the high implementation complexity of joint decoding, we also consider the DM MAC-WT channel where Bob simply decodes messages independently while Eve still applies joint decoding. We then extend the results in the DM case to a Gaussian vector (GV) MAC-WT channel. Based on the information theoretic results, we further maximize the sum secrecy rate of the GV MAC-WT system by designing precoders for all users. Since the problems are non-convex, we provide iterative algorithms to obtain suboptimal solutions. Simulation results show that compared with existing schemes, secure communication can be greatly enhanced by the proposed algorithms, and in contrast to the works which only focus on the network secrecy performance, the system spectrum efficiency can be effectively improved since open messages can be simultaneously transmitted.

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync&loc) problem in ultra-dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs’ clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by the exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packets, DePF draws on the multiple signal classification algorithm that is fed with the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and, thus, jointly estimates the position and clock parameters of the MUs. The simulation results verify the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of the extended Kalman filter- and linearized BRF-based joint sync&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs from a single AP, for 90% of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.

Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultaneously useful to multiple users. Most prior works on coded caching are based on the assumption that each user may request all content in the library. However, in many applications the users are interested only in a limited set of content that depends on their location. For example, assisted self-driving vehicles may access super High-Definition maps of the area through which they are travelling. Motivated by these considerations, this paper formulates the coded caching problem for location-based content with edge cache nodes. The considered problem includes a content server with access to${\mathsf N}$location-based files (e.g., High-Definition maps),${\mathsf K}$edge cache nodes located at different regions, and${\mathsf K}$users (i.e., vehicles) each of which is in the serving region of one cache node and can retrieve the cached content of this cache node with negligible cost. Depending on the location, each user only requests a file from a location-dependent subset of the library. The objective is to minimize the worst-case load (i.e., the worst-case number of broadcasted bits from the content server among all possible demands). For this novel coded caching problem, we propose a highly non-trivial converse bound under uncoded cache placement (i.e., each cache node directly copies some library bits in its cache), which shows that a simple achievable scheme is optimal under uncoded cache placement. In addition, this achievable scheme is also proved to be generally order optimal within a factor of 3. Finally, we extend the coded caching problem for location-based content to the multiaccess coded caching topology originally proposed by Hachemet al., where each user is connected to${\mathsf L}$nearest cache nodes. When${\mathsf L}\geq 2$, we characterize the exact optimality on the worst-case load.

This paper formulates a distributed computation problem, where a master asks${\mathsf N}$distributed workers to compute a linearly separable function. The task function can be expressed as${\mathsf K}_{\mathrm{ c}}$linear combinations of${\mathsf K}$messages, where each message is a function of one dataset. Our objective is to find the optimal tradeoff between the computation cost (number of uncoded datasets assigned to each worker) and the communication cost (number of symbols the master must download), such that from the answers of any${\mathsf N}_{\mathrm{ r}}$out of${\mathsf N}$workers the master can recover the task function with high probability, where the coefficients of the${\mathsf K}_{\mathrm{ c}}$linear combinations are uniformly i.i.d. over some large enough finite field. The formulated problem can be seen as a generalized version of some existing problems, such as distributed gradient coding and distributed linear transform. In this paper, we consider the specific case where the computation cost is minimum, and propose novel achievability schemes and converse bounds for the optimal communication cost. Achievability and converse bounds coincide for some system parameters; when they do not match, we prove that the achievable distributed computing scheme is optimal under the constraint of a widely used ‘cyclic assignment’ scheme on the datasets. Our results also show that when${\mathsf K}= {\mathsf N}$, with the same communication cost as the optimal distributed gradient coding scheme proposed by Tandonet al. from which the master recovers one linear combination of${\mathsf K}$messages, our proposed scheme can let the master recover any additional${\mathsf N}_{\mathrm{ r}}-1$independent linear combinations of messages with high probability.

Coded caching is a promising technique to smooth out network traffic by storing part of the library content at the users’ local caches. The seminal work on coded caching for single file retrieval by Maddah-Ali and Niesen (MAN) showed the existence of a global caching gain that scales with the total memory in the system, in addition to the known local caching gain in uncoded systems. This paper formulates a novel cache-aided matrix multiplication retrieval problem, relevant for data analytics and machine learning applications. In the considered problem, each cache-aided user requests the product of two matrices from the library. A structure-agnostic solution is to treat each possible matrix product as an independent file and use the MAN coded caching scheme for single file retrieval. This paper proposes two structure-aware schemes, which partition each matrix in the library by either rows or columns and let a subset of users cache some sub-matrices, that improve on the structure-agnostic scheme. For the case where the library matrices are “fat” matrices, the structure-aware row-partition scheme is shown to be order optimal under some constraint.

In the coded caching problem, as originally formulated by Maddah-Ali and Niesen, a server communicates via a noiseless shared broadcast link to multiple users that have local storage capability. In order for a user to decode its demanded file from the coded multicast transmission, the demands of all the users must be globally known, which may violate the privacy of the users. To overcome this privacy problem, Wan and Caire recently proposed several schemes that attain coded multicasting gain while simultaneously guarantee information theoretic privacy of the users’ demands. In Device-to-Device (D2D) networks, the demand privacy problem is further exacerbated by the fact that each user is also a transmitter, which appears to be needing the knowledge of the files demanded by the remaining users in order to form its coded multicast transmission. This paper shows how to solve this seemingly infeasible problem. The main contribution of this paper is the development of new achievable and converse bounds for D2D coded caching that are to within a constant factor of one another when privacy of the users’ demands must be guaranteed even in the presence of colluding users (i.e., when some users share cached contents and demanded file indices). First, a D2D private caching scheme is proposed, whose key feature is the addition of virtual users in the system in order to “hide” the demands of the real users. By comparing the achievable D2D private load with an existing converse bound for the shared-link model without demand privacy constraint, the proposed scheme is shown to be order optimal, except for the very low memory size regime with more files than users. Second, in order to shed light into the open parameter regime, a new achievable scheme and a new converse bound under the constraint of uncoded cache placement (i.e., when each user stores directly a subset of the bits of the library) are developed for the case of two users, and shown to be to within a constant factor of one another for all system parameters. Finally, the two-user converse bound is extended to any number of users by a cut-set type argument. With this new converse bound, the virtual users scheme is shown to be order optimal in all parameter regimes under the constraint of uncoded cache placement and user collusion.

Many recent works in the literature declare that Orthogonal Time-Frequency-Space (OTFS) modulation is a promising candidate technology for high mobility communication scenarios. However, a truly fair comparison with its direct concurrent and widely used Orthogonal Frequency-Division Multiplexing (OFDM) modulation has not yet been provided. In this paper, we present such a fair comparison between the two digital modulation formats in terms of achievable communication rate. In this context, we explicitly address the problem of channel estimation by considering, for each modulation, a pilot scheme and the associated channel estimation algorithm specifically adapted to sparse channels in the Doppler-delay domain, targeting the optimization of the pilot overhead to maximize the overall achievable rate. In our achievable rate analysis we consider also the presence of a guard interval or cyclic prefix. The results are supported by numerical simulations, for different time-frequency selective channels including multiple scattering components and under non-perfect channel state information resulting from the considered pilot schemes. This work does not claim to establish in a fully definitive way which is the best modulation format, since such choice depends on many other features which are outside the scope of this work (e.g., legacy, intellectual property, ease and know-how for implementation, and many other criteria). Nevertheless, we provide the foundations to properly compare multi-carrier communication systems in terms of their information theoretic achievable rate potential, within meaningful and sensible assumptions on the channel models and on the receiver complexity (both in terms of channel estimation and in terms of soft-output symbol detection).

We consider the pilot assignment problem in large-scale distributed multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing structures on the UE-RRH connectivity, we model the network by a partially-connected interference network, so that the pilot assignment problem can be cast as a topological interference management problem with multiple groupcast messages. Building upon such connection, we formulate the topological pilot assignment (TPA) problem in two different ways with respect to whether or not the to-be-estimated channel connectivity pattern is knowna priori. When it is known, we formulate the TPA problem as a low-rank matrix completion problem that can be solved by a simple alternating projection algorithm. Otherwise, we formulate it as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. With respect to two different formulations of the TPA problem, we evaluate the efficiency of the proposed algorithms under the cell-free massive MIMO setting.

In this paper, we consider user selection and downlink precoding for an over-loaded single-cell massive multiple-input multiple-output (MIMO) system in frequency division duplexing (FDD) mode, where the base station is equipped with a dual-polarized uniform planar array (DP-UPA) and serves a large number of single-antenna users. Due to the absence of uplink-downlink channel reciprocity and the high-dimensionality of channel matrices, it is extremely challenging to design downlink precoders using closed-loop channel probing and feedback with limited spectrum resource. To address these issues, a novel methodology – active channel sparsification (ACS) – has been proposed recently in the literature for uniform linear array (ULA) to design sparsifying precoders, which substantially reduces channel feedback overhead. Pushing forward this line of research, we aim to facilitate the potential deployment of ACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA with explicit user selection and making the current ACS implementation simplified. To this end, by leveraging Toeplitz matrix theory, we start with the spectral properties of channel covariance matrices from the lens of their matrix-valued spectral density function. Inspired by these properties, we extend the original ACS using scalar-weight bipartite graph representation to the matrix-weight counterpart. Building upon such matrix-weight bipartite graph representation, we propose a multi-dimensional ACS (MD-ACS) method, which is a generalization of original ACS formulation and is more suitable for DP-UPA antenna configurations. The nonlinear integer program formulation of MD-ACS can be classified as a generalized multi-assignment problem (GMAP), for which we propose a simple yet efficient greedy algorithm to solve it. Simulation results demonstrate the performance improvement of the proposed MD-ACS with greedy algorithm over the state-of-the-art methods based on the QuaDRiGa channel models.

We consider the pilot assignment problem in cell-free massive multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing topological structures on the UE-RRH connectivity, we model the network by a partially-connected interference network and formulate the topological pilot assignment (TPA) problem as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. The efficiency of the proposed algorithms is evaluated in cell-free massive MIMO networks.

Deep unfolding showed to be a very successful approach for accelerating and tuning classical signal processing algorithms. In this paper, we propose learned Gaussian-mixture AMP (L-GM-AMP) - a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding’s learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. The robust and flexible denoiser is a byproduct of modelling source prior with a Gaussian-mixture (GM), which can well approximate continuous, discrete, as well as mixture distributions. Its parameters are learned using standard backpropagation algorithm. To demonstrate robustness of the proposed algorithm, we conduct Monte-Carlo (MC) simulations for both mixture and discrete distributions. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.

This paper studies the multiaccess caching systems formulated by Hachem et al., including a central server containing$N$files connected to$K$cache-less users through an error-free shared link, and$K$cache-nodes, each equipped with a cache memory size of$M$files. Each user has access to$L$neighbouring cache-nodes with a cyclic wrap-around topology. The coded caching scheme proposed by Hachem et al. suffers from the case that$L$does not divide$K$, where the needed number of transmissions (a.k.a. load) is at most four times the load expression for the case where$L$divides$K$. Our main contribution is to propose a novel transformation approach to smartly extend the MN scheme to the multiaccess caching systems, such that the load expression of the scheme by Hachem et al for the case where$L$divides$K$, remains achievable in full generality. The resulting scheme has the maximum local caching gain (i.e., the cached contents stored at any$L$neighbouring cache-nodes are different such that each user can totally retrieve$L M$files from the connected cache-nodes) and the same coded caching gain as the related MN scheme. Moreover, our transformation approach can also be used to extend other coded caching schemes (satisfying some conditions) for the original MN caching systems to the multiaccess systems, such that the resulting scheme achieves the maximum local caching gain and the same coded caching gain as the considered caching scheme.

The work focuses on optimizing coded caching under asynchronous demands. We consider a single-stream setting where users are allowed to request content at arbitrary time-slots. Aiming to minimize the total system delay required to serve all users, i.e. from the moment of the first request to the delivery of the last bit of requested information, we design a pair of placement and delivery algorithms and show that the achievable performance is within a multiplicative factor of 2 from the optimal, under the assumption of uncoded placement, and within a multiplicative factor of 4.02 in the general placement case. Interesting characteristics of our algorithms are that i) a placement phase agnostic to the users' arrival times is adequate to provide a near-optimal delay, and ii) the proposed delivery algorithm requires low complexity and, at the same time, requires no non-causal information. Further, we show that systems are able to withstand some degree of asynchronicity without an increase in the delay compared to an equivalent synchronous setting. Finally, we highlight an interesting connection between coded caching under asynchronous demands and coded caching in wireless environments under uneven channel strengths.

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a subset of input datasets to each server, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. Novel information theoretic converse bound on the randomness size and some achievable schemes are proposed, where they coincide in some cases.

This paper formulates the shared-link cache-aided matrix multiplication retrieval problem. Matrix multiplication is an essential building block for distributed computing applications. Different from the original coded caching single file retrieval model, in the considered problem each cache-aided user requests the product of two matrices from a library that contains N matrices. A trivial solution, agnostic to the structure of matrix multiplication, is to treat each of the N2possible matrix products as a file in the original single file retrieval coded caching problem. Such a solution can be improved by leveraging the correlation among the entries in the matrix product. In this paper, two structure-aware schemes are proposed, which partition each library matrix either by rows or by columns, and let a subset of users cache some sub-matrices; in the delivery, coded multicast messages are created to leverage the cached content and the correlation among the entries in the requested matrix products. These schemes outperform two baseline schemes, where one sends packets without coding and the other lets each user directly recover the two input matrices. Order optimality results are derived in some parameter regimes.

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel. Due to the bottleneck constraint, it is impossible for the oblivious relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node can get the perfect CSI at no cost and two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

In the problem of cache-aided multiuser private information retrieval (MuPIR), a set of$K_{\mathrm{u}}$cache-equipped users wish to privately download a set of messages from$N$distributed databases each holding a library of$K$messages. The system works in two phases: the cache placement (prefetching) phase in which the users fill up their cache memory, and the private delivery phase in which the users' demands are revealed and they download an answer from each database so that the their desired messages can be recovered while each individual database learns nothing about the identities of the requested messages. The goal is to design the placement and the private delivery phases such that the load, which is defined as the total number of downloaded bits normalized by the message size, is minimized given any user memory size. This paper considers the MuPIR problem with two messages, arbitrary number of users and databases where uncoded prefetching is assumed, i.e., the users directly copy some bits from the library as their cached contents. We propose a novel MuPIR scheme inspired by the Maddah-Ali and Niesen (MAN) coded caching scheme. The proposed scheme achieves lower load than any existing schemes, especially the product design (PD), and is shown to be optimal within a factor of 8 in general and exactly optimal at very high or very low memory regimes.

Intelligent reflecting surfaces (IRS) are a novel technology envisaged to significantly improve the performance of next generation wireless communication networks, utilizing passive reflecting elements arranged in planar arrays to reconfigure the wireless propagation environment. This study investigates the use of Intelligent Reflecting Surfaces for Vulnerable Road Users (VRU) such as pedestrians, bicycles, and wheelchair users. This can be made possible by recent advances in IRS technology and can significantly improve the radar visibility of VRUs. In this work we propose a potential use case for IRS which aims to improve the detection of traffic users by automotive radar irrespective of the object's orientation which may severely impact its observable radar cross section. Furthermore, this approach can be extended to form a network where multiple radar sensors can become aware of a VRU's presence even in cases where the users have not been directly observed by the respective sensor. Numerical results are provided to show that the proposed approach can enhance the radar detection capability of VRUs and can help to overcome the challenges due to the orientation-dependent radar cross section of targets.

We consider optimal cache placement and delivery for Orthogonal Multipoint Multicasting (OMPMC) cellular systems. In OMPMC, all Base Stations (BSs) that cache a file transmit identical signals in a dedicated frequency resource. The simultaneous transmissions create artificial multipath propagation, which creates Inter-Block Interference (IBI) and Inter-Carrier Interference (ICI) in Orthogonal Frequency Division Multiplexing systems where the Cyclic Prefix (CP) is shorter than the maximum propagation delay. The placement of files at BS caches is based on a probabilistic model. A file request is in outage if the average signal-to-interference-and-noise ratio associated with a request is less than a threshold. We formulate the cache policy and bandwidth allocation as a joint optimization problem aiming to minimize the total outage probability, and considering the effect of IBI and ICI. Despite that the outage probability does not have a closed form expression, we are able to devise an algorithm to find the optimal solution based on predictor-corrector approach. Simulations results are used to demonstrate the capability of the proposed algorithm to find the optimum cache policy. Simulation results show that the effect of ICI/IBI has to be considered in designing OMPMC caching policy.

As millimeter wave wireless communications are expected to be widely deployed in future applications, line-of-sight multiple-input multiple-output (LoS MIMO) systems are likely to become more important. The performance of LoS MIMO systems relies heavily on the placement of the antennas at the transmit (Tx) and receive (Rx) arrays, where the optimal antenna placement depends on the distance between the arrays. However, as a LoS MIMO system may be required to operate over a range of distances, the variation of the capacity and the spatial multiplexing gain are key design factors. Considering uniform circular arrays (UCA), by assuming that more than one UCAs are available at the Rx, we propose three array selection schemes for robust design of LoS MIMO UCA systems over a range of distances between the arrays. Their performance is evaluated in terms of their practical implications and their robustness with regard to a suitably defined measure.

In this paper, we study the robust secure unmanned aerial vehicle (UAV) communication system, where a UAV with full-duplex (FD) capability simultaneously receives the information signal from a ground unit (GU) and transmits jamming signal to degrade the wiretap capability of potential multiple ground eavesdroppers (Eves). With the consideration of estimation error of Eves' locations, we aim to maximize the average worst secrecy rate inside a certain flight period of the UAV by jointly optimizing the transmit power of the GU and UAV as well as the trajectory of the UAV. The resulting problem is intractable due to its non-convex nature and strongly coupled variables. Furthermore, the estimation error of Eves' locations results in an infinite number of constraints, which makes the problem even more difficult. To tackle this difficulty, we first propose an iterative algorithm based on the Schur complement lemma and successive inner approximation method to efficiently solve the problem suboptimally under the estimated Eves' locations. Then, in order to cope with the of Eves' location errors, we develop a cutting-set method, which solves the problem by alternating between optimal power-trajectory design and worst-case Eves' locations analysis. Via simulation, we show the improvement of the proposed algorithm compared to other benchmark algorithms under high FD self-interference cancellation levels.

The coded caching scheme originally proposed by Maddah-Ali and Niesen (MAN) transmits coded multicast messages from a server to users equipped with caches via a capacitated shared-link and was shown to be information theoretically optimal within a constant multiplicative factor. This work extends the MAN scheme to a class of two-hop wired-wireless networks including one server connected via fronthaul links to a layer of H helper nodes (access points/base stations), which in turn communicate via a wireless access network to K users, each equipped with its own cache. Two variants are considered, which differ in the modeling of the access segment. Both models should be regarded as abstractions at the “network layer” for physical scenarios such as local area networks and cellular networks, spatially distributed over a certain coverage area. The key of our approach consists of routing MAN-type multicast messages through the network and formulating the optimal routing scheme as an optimization problem that can be solved exactly or for which we give powerful heuristic algorithms. Our approach addresses at once many of the open practical problems identified as stumbling blocks for the application of coded caching in practical scenarios, namely: asynchronous streaming sessions, finite file size, scalability of the scheme to large and spatially distributed networks, user mobility and random activity (users joining and leaving the system at arbitrary times), decentralized prefetching of the cache contents, end-to-end encryption of HTTPS requests, which renders the helper nodes oblivious of the users' demands.

This paper considers the multiaccess coded caching systems formulated by Hachemet al., including a central server containing$N$files connected to$K$cache-less users through an error-free shared link, and$K$cache-nodes, each equipped with a cache memory size of$M$files. Each user has access to$L$neighbouring cache-nodes with a cyclic wrap-around topology. The coded caching scheme proposed by Hachemet al.suffers from the case that$L$does not divide$K$, where the needed number of transmissions (a.k.a. load) is at most four times the load expression for the case where$L$divides$K$. Our main contribution is to propose a noveltransformationapproach to smartly extend the schemes satisfying some conditions for the well known shared-link caching systems to the multiaccess caching systems. Then we can get many coded caching schemes with different subpacketizations for multiaccess coded caching system. These resulting schemes have the maximum local caching gain (i.e., the cached contents stored at any$L$neighbouring cache-nodes are different such that the number of retrieval packets by each user from the connected cache-nodes is maximal) and the same coded caching gain as the original schemes. Applying the transformation approach to the well-known shared-link coded caching scheme proposed by Maddah-Ali and Niesen, we obtain a new multiaccess coded caching scheme that achieves the same load as the scheme of Hachemet al.but for any system parameters. Under the constraint of the cache placement used in this new multiaccess coded caching scheme, our delivery strategy is approximately optimal when$K$is sufficiently large. Finally, we also show that the transmission load of the proposed scheme can be further reduced by compressing the multicast message.

Millimeter wave (mmWave) communication is expected to play a central role in next generation mobile systems (5G) and beyond, by providing multi-Gbps data rates. However, the severe pathloss and sensitivity to blockages at mmWave frequencies significantly challenge practical implementations. One effective way to mitigate these effects and to increase the communication range is beamforming in combination with relaying. In this paper, we study the beam scheduling problem for mmWave half-duplex (HD) relay networks, where the relay topology can be arbitrary. Based on theoretically optimal scheduling results, we first implement a network simplification procedure to reduce the network topology complexity, and then propose two practically relevant beam scheduling schemes: the deterministic edge coloring (EC) scheduler and the adaptive backpressure (BP) scheduler. The former consists of a very simple one-time computation of the sequence of scheduling states, which is then repeated periodically. The one-time computation depends on the underlying network topology, and therefore it must be repeated when such topology changes. As such, this approach is more suited to quasi-static scenarios. The latter is an “online” approach which updates scheduling weights and solves at each time slots a weighted sum rate maximization. Hence, it's computational complexity may be significantly higher than that of EC, but it is better suited to dynamic time-varying scenarios. With the aid of computer simulations, we show that both the proposed schedulers guarantee network stability within the network capacity. Particularly, in comparison with two baseline schemes, the proposed schedulers achieve much smaller queuing backlogs, much smaller backlog fluctuations, and much lower packet end-to-end delays.

This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient coding and distributed linear transform. A master asks${\mathsf {N}}$distributed workers to compute a linearly separable function of${\mathsf {K}}$datasets, which is a set of${\mathsf {K}}_{\mathrm{ c}}$linear combinations of${\mathsf {K}}$equal-length messages (each message is a function of one dataset). We assign some datasets to each worker in an uncoded manner, who then computes the corresponding messages and returns some function of these messages, such that from the answers of any${\mathsf {N}}_{\mathrm{ r}}$out of${\mathsf {N}}$workers the master can recover the task function with high probability. In the literature, the specific case where${\mathsf {K}}_{\mathrm{ c}}=1$or where the computation cost is minimum has been considered. In this paper, we focus on the general case (i.e., general${\mathsf {K}}_{\mathrm{ c}} $and general computation cost) and aim to find the minimum communication cost. We first propose a novel converse bound on the communication cost under the constraint of the popularcyclic assignment(widely considered in the literature), which assigns the datasets to the workers in a cyclic way. Motivated by the observation that existing strategies for distributed computing fall short of achieving the converse bound, we propose a novel distributed computing scheme for some system parameters. The proposed computing scheme is optimal for any assignment when${\mathsf {K}}_{\mathrm{ c}}$is large and is optimal under the cyclic assignment when the numbers of workers and datasets are equal or${\mathsf {K}}_{\mathrm{ c}}$is small. In addition, it is order optimal within a factor of 2 under the cyclic assignment for the remaining cases.

We consider the problem of cache-aided Multiuser Private Information Retrieval (MuPIR) which is an extension of the single-user cache-aided PIR problem to the case of multiple users. In cache-aided MuPIR, each of the$K_{\mathrm{ u}}$cache-equipped users wishes to privately retrieve a message out of$K$messages from$N$databases each having access to the entire message library. Demand privacy requires that any individual database learns nothing about the demands of all users. The users are connected to each database via an error-free shared-link. In this paper, we aim to characterize the optimal trade-off between user cache memory and communication load for such systems. First, we propose a novel approach ofcache-aided interference alignment (CIA), for the MuPIR problem with$K=2$messages,$K_{\mathrm{ u}}=2$users and$N\ge 2$databases. The CIA approach is optimal when the cache placement is uncoded. For general cache placement, the CIA approach is optimal when$N=2$and 3 verified by the computer-aided converse approach. Second, for the general case, we propose aproduct design(PD) which incorporates the PIR code into the linear caching code. The product design is shown to be order optimal within a multiplicative factor of 8 and is exactly optimal in the high memory regime.

We combine three different approaches to greatly enhance the defect reconstruction ability of active thermographic testing. As experimental approach, laser-based structured illumination is performed in a stepwise manner. As an intermediate signal processing step, the virtual wave concept is used in order to effectively convert the notoriously difficult to solve diffusion-based inverse problem into a somewhat milder wave-based inverse problem. As a final step, a compressed-sensing-based optimization procedure is applied which efficiently solves the inverse problem by making advantage of the joint sparsity of multiple blind measurements. To evaluate our proposed processing technique, we investigate an additively manufactured stainless steel sample with eight internal defects. The concerted super resolution approach is compared to conventional thermographic reconstruction techniques and shows an at least four times better spatial resolution.

This paper proposes a new model for wireless relay networks referred to as “1-2-1 network”, where two nodes can communicate only if they point “beams” at each other, otherwise no signal can be exchanged or interference can be generated. This model is motivated by millimeter wave communications where, due to the high path loss, a link between two nodes can exist only if beamforming gain at both sides is established, while in the absence of beamforming gain the signal is received well below the thermal noise floor. The main contributions in this paper include: (a) the development of a constant gap approximation for the unicast and multicast capacities of the proposed network model, i.e., a characterization of the network unicast and multicast capacities to within an additive gap, which only depends on the number of nodes and is independent of the channel coefficients and operating SNR; and (b) the design of algorithms that run in polynomial time in the number of nodes and compute the approximate unicast and multicast capacities, as well as their corresponding optimal beam scheduling strategies. These results are derived both forfull-duplexandhalf-duplexmodes of operation at the relays: while in full-duplex the transmit and receive beams at a relay can be simultaneously active, in half-duplex only one can be active at each point in time. The relation between the approximate multicast capacity and minimum unicast capacity is explored in full-duplex 1-2-1 networks and shown to be dependent on the network structure and the number of destinations, unlike in classical wireless (i.e., without 1-2-1 constraints) full-duplex networks. Finally, network simplification results are proved for the 1-2-1 network model by exploiting the structure of the linear program that represents the approximate capacity.

In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station with a large number M of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the M-dimensional channel vector of each user remains constant over a coherence block containing L signal dimensions in time-frequency. In the considered setting, the number of potential users Ktotis much larger than L but at each time slot only Katotof them are active. Previous results, based on compressed sensing, require that Ka≤ L, which is a bottleneck in massive deployment scenarios. In this work, we show that such limitation can be overcome when the number of base station antennas M is sufficiently large. More specifically, we prove that with a coherence block of dimension L and a number of antennas M such that Ka/M = o(1), one can identify Ka= O(L2/log2(Ktot/ Ka)) active users, which is much larger than the previously known bounds. We also provide two algorithms. One is based on Non-Negative Least-Squares, for which the above scaling result can be rigorously proved. The other consists of a low-complexity iterative componentwise minimization of the likelihood function of the underlying problem. While for this algorithm a rigorous proof cannot be given, we analyze a constrained version of the Maximum Likelihood (ML) problem (a combinatorial optimization with exponential complexity) and find the same fundamental scaling law for the number of identifiable users. Therefore, we conjecture that the low-complexity (approximated) ML algorithm also achieves the same scaling law and we demonstrate its performance by simulation. We also compare the discussed methods with the (Bayesian) MMV-AMP algorithm, recently proposed for the same setting, and show superior performance and better numerical stability. Finally, we use the discussed approximated ML algorithm as the inner decoder in a concatenated coding scheme for unsourced random access, a grant-free uncoordinated multiple access scheme where all users make use of the same codebook, and the receiver must produce the list of transmitted messages, irrespectively of the identity of the transmitters. We show that reliable communication is possible at any Eb/N0provided that a sufficiently large number of base station antennas is used, and that a sum spectral efficiency in the order ofO(Llog(L)) is achievable.

Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number Kaare active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. We present a concatenated coding construction for U-RA on the AWGN channel, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. We propose a modified version of the approximate message passing (AMP) algorithm as an inner decoder and give a precise asymptotic analysis of the error probabilities of the AMP decoder and of a hypothetical optimal inner MAP decoder. This analysis shows that the concatenated construction under optimal decoding can achieve a vanishing per-user error probability in the limit of large blocklength and a large number of active users at sum-rates up to the symmetric Shannon capacity, i.e. as long as KaR2(1+KaSNR). This extends previous point-to-point optimality results about SPARCs to the unsourced multiuser scenario. Furthermore, we give an optimization algorithm to find the power allocation for the inner SPARC code that minimizes the SNR required to achieve a given target per-user error probability with the AMP decoder.

We study the generalized degrees-of-freedom (GDoF) of cellular networks under finite precision channel state information at the transmitters (CSIT). We consider downlink settings modeled by the interfering broadcast channel (IBC) under no multi-cell cooperation, and the overloaded multiple-input-single-output broadcast channel (MISO-BC) under full multi-cell cooperation. We focus on three regimes of interest: the mc-TIN regime, where a scheme based on treating inter-cell interference as noise (mc-TIN) was shown to be GDoF optimal for the IBC; the mc-CTIN regime, where the GDoF region achievable by mc-TIN is convex without the need for time-sharing; and the mc-SLS regime which extends a previously identified regime, where a simple layered superposition (SLS) scheme is optimal for the 3-transmitter-3-user MISO-BC, to overloaded cellular-type networks with more users than transmitters. We first show that the optimality of mc-TIN for the IBC extends to the entire mc-CTIN regime when CSIT is limited to finite precision. The converse proof of this result relies on a new application of aligned images bounds. We then extend the IBC converse proof to the counterpart overloaded MISO-BC, obtained by enabling full transmitter cooperation. This, in turn, is utilized to show that a multi-cell variant of the SLS scheme is optimal in the mc-SLS regime under full multi-cell cooperation, albeit only for 2-cell networks. The overwhelming combinatorial complexity of the GDoF region stands in the way of extending this result to larger networks. Alternatively, we appeal to extremal network analysis, recently introduced by Chan et al., and study the GDoF gain of multi-cell cooperation over mc-TIN in the three regimes of interest. We show that this extremal GDoF gain is bounded by small constants in the mc-TIN and mc-CTIN regimes, yet scales logarithmically with the number of cells in the mc-SLS regime.

Coded caching has the potential to greatly reduce network traffic by leveraging the cheap and abundant storage available in end-user devices so as to create multicast opportunities in the delivery phase. In the seminal work by Maddah-Ali and Niesen (MAN), the shared-link coded caching problem was formulated, where each user demands one file (i.e., single file retrieval). This article generalizes the MAN caching problem formulation from single file retrieval on the binary filed to general scalar linear function retrieval on an arbitrary finite field. The proposed novel scheme is linear, based on MAN uncoded cache placement, and leverages ideas from interference alignment. Quite surprisingly, the worst-case load of the proposed scheme among all possible demands is the same as the one of the scheme by Yu, Maddah-Ali, and Avestimehr (YMA) for single file retrieval. The proposed scheme has thus the same optimality guarantees as YMA, namely, it is optimal under the constraint of uncoded cache placement, and is optimal to within a factor 2 otherwise. Some extensions of the proposed scheme are then discussed. It is shown that the proposed scheme works not only on arbitrary finite field, but also on any commutative ring. The key idea of this article can be also extended to all scenarios to which the original MAN scheme has been extended, including but not limited to demand-private retrieval and Device-to-Device networks.

Maddah-Ali and Niesen (MAN) in 2014 showed that coded caching in single bottleneck-link broadcast networks allows serving an arbitrarily large number of cache-equipped users with a total link load (bits per unit time) that does not scale with the number of users. Since then, the general topic of coded caching has generated enormous interest both from the information theoretic and (network) coding theoretic viewpoint, and from the viewpoint of applications. Building on the MAN work, this paper considers a particular network topology referred to as cache-aided Fog Radio Access Network (Fog-RAN), that includes a Macro-cell Base Station (MBS) co-located with the content server, several cache-equipped Small-cell Base Stations (SBSs), and many users without caches. Some users are served directly by the MBS broadcast downlink, while other users are served by the SBSs. The SBSs can also exchange data via rounds of direct communication via a side channel, referred to as “sidelink”. For this novel Fog-RAN model, the fundamental tradeoff among (a) the amount of cache memory at the SBSs, (b) the load on the downlink (from MBS to directly served users and SBSs), and (c) the aggregate load on the sidelink is studied, under the standard worst-case demand scenario. We propose a converse bound whose key novelty is to jointly bound the downlink load an the sidelink load. For the achievability, by leveraging the network topology, we propose two classes of memory-loads point, where the SBS sidelink load is minimum and the MBS downlink load is minimum, respectively. By memory-sharing between these two classes of memory-loads points, some exact or order optimality results are obtained. Several existing models (e.g., Device-to-Device coded caching, single bottleneck-link coded caching with shared caches, single bottleneck-link caching coded caching with cache-less users) are recovered as special cases of this network model and by-product results of independent interest are given. Finally, the role of topology-aware versus topology-agnostic caching is discussed.

Visible light communications (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a small-scale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for low-outage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point x (transmitter location) to any point y on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter-receiver locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between transmitter and receiver. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, in a very accurate and computationally efficient manner. Our proposed method, termed RadioUNet, learns from a physical simulation dataset, and generates pathloss estimations that are very close to the simulations, but are much faster to compute for real-time applications. Moreover, we propose methods for transferring what was learned from simulations to real-life. Numerical results show that our method significantly outperforms previously proposed methods.

We consider an optimal cache-placement-and-delivery-policy using Network-level Orthogonal Multipoint Multicasting (OMPMC) for wireless networks. The placement of files in caches of Base Station (BS) is based on a probabilistic model, with controlled cache placement probabilities. File delivery is based on multipoint multicast and network-based orthogonal transmission; all BSs in the network caching a file transmit it synchronously in dedicated radio resources. If the average signal-to-noise ratio associated to a file at a requesting user is less than a threshold, the request is in outage. We derive a closed-form expression for the outage probability for a network modeled as a Poisson Point Process. An optimal caching policy is solved from an optimization problem, and compared to a threshold-based policy, suboptimal partial solutions, and single-point cache delivery. Simulation results show that exploiting OMPMC with optimal cache and bandwidth allocation significantly improves the overall outage probability as compared to single point delivery.

Coded caching is an information theoretic scheme to reduce high peak hours traffic by partially prefetching files in the users local storage during low peak hours. This paper considers heterogeneous decentralized caching systems where users' caches and content library files may have distinct sizes. The server communicates with the users through a Gaussian broadcast channel. The main contribution of this paper is a novel joint coded caching and modulation strategy to map the multicast messages generated in the coded caching delivery phase to the symbols of a signal constellation, such that users can leverage their cached content to demodulate the desired symbols with higher reliability and for the sake of simplicity, in this paper we focus only on “uncoded” modulation and symbol-by-symbol error probability. However, our scheme in conjunction with multilevel coded modulation can be extended to channel coding over a larger block lengths.

We study the fundamental limits of multi-cell cooperation in downlink cellular networks under the assumption of finite precision channel state information at the transmitters (CSIT). By appealing to extremal network theory, recently introduced by Chan et al. [1], we characterize the extremal GDoF gain of multi-cell cooperation over non-cooperative multi-cell TIN in three weak inter-cell interference regimes: the mc-TIN regime, where multi-cell TIN is GDoF optimal under no cooperation; the mc-CTIN regime, where the GDoF region achieved through multi-cell TIN is convex without the need for time-sharing; and the mc-SLS regime, where a cooperative scheme based on simple layered superposition was shown to be optimal in small networks. We show that the extremal GDoF gain is bounded by a constant factor in the mc-TIN and mc-CTIN regimes, and scales logarithmically with the number of cells in the mc-SLS regime. The analysis is enabled by a new cooperative outer bound for cellular networks based on the aligned images approach.

Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the “clean” CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.

We study the problem of maximum-likelihood (ML) estimation of an approximate common eigenstructure, i.e. an approximate common eigenvectors set (CES), for an ensemble of covariance matrices given a collection of their associated i.i. d vector realizations. This problem has a direct application in multi-user MIMO communications, where the base station (BS) has access to instantaneous user channel vectors through pilot transmission and attempts to perform joint multi-user Downlink (DL) precoding. It is widely accepted that an efficient implementation of this task hinges upon an appropriate design of a set of common “virtual beams” that captures the common eigenstructure among the user channel covariances. In this paper, we propose a novel method for obtaining this common eigenstructure by casting it as an ML estimation problem. We prove that in the special case where the covariances are jointly diagonalizable, the global optimal solution of the proposed ML problem coincides with the common eigenstructure. Then we propose a projected gradient descent (PGD) method to solve the ML optimization problem over the manifold of unitary matrices and prove its convergence to a stationary point. Through exhaustive simulations, we illustrate that in the case of jointly diagonalizable covariances, our proposed method converges to the exact CES. Also, in the general case where the covariances are not jointly diagonalizable, it yields a solution that approximately diagonalizes all covariances. Besides, the empirical results show that our proposed method outperforms the well-known joint approximate diagonalization of eigenmatrices (JADE) method in the literature.

In spatial channel models used in multi-antenna wireless communications, the propagation from a single-antenna transmitter (e.g. a user) to an M-antenna receiver (e.g. a Base Station) occurs through scattering clusters located in the far field of the receiving array. The angular power spread function (APSF) of the corresponding M-dim channel vector describes the angular density of the received signal power at the array. In many applications, such as channel sounding and Uplink Downlink covariance transformation in FDD systems, estimating the APSF is required either implicitly or explicitly. However, the existing literature on the subject has mainly focused on channel covariance estimation from a set of noisy pilot observations. It is also assumed that the APSF consists only of discrete components corresponding to Line-of-Sight (LoS) paths and specular scattering. It turns out that while covariance estimation is a well-posed problem, APSF estimation is a much harder task and is in general ill-posed. The reason is that the propagation environment can also include diffuse scattering elements, resulting in continuous APSF components. Therefore, the APSF is a function belonging to the infinite-dimensional space of nonnegative measures over the angle domain. In this paper, we show that under a geometrically-consistent, group-sparse structure on the APSF, which is prevalent in massive MIMO channels, one is able to estimate the APSF properly. We propose an algorithm based on deep neural networks (DNNs) that learns this structure and yields precise APSF estimates, even when the number of available pilot observations is relatively small. We empirically show that our proposed method outperforms the state-of-the-art method in various performance metrics.

This paper considers the MapReduce-like coded distributed computing framework originally proposed by Li et al., which uses coding techniques when distributed computing servers exchange their computed intermediate values, in order to reduce the overall traffic load. In their original model, servers are connected via an error-free common communication bus allowing broadcast transmissions. However, this assumption is one of the major limitations for practical implementations since real-world data centers may have network topologies far more involved than a single broadcast bus. We formulate a topological coded distributed computing problem, where the computing servers communicate with each other through some switch network. By using a special instance of fat-tree topologies, referred to as t-ary fat-tree proposed by Al-Fares et al. which can be built by some inexpensive switches, we propose a coded distributed computing scheme to achieve the optimal max-link communication load (defined as the maximum load over all links) over any network topology.

In this paper we propose a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane. Our method, termed RadioUNet, is a deep neural network. For applications such as user-cell site association and device-to-device (D2D) link scheduling, an accurate knowledge of the pathloss function for all pairs of locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between the points. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, very accurately and extremely quickly. Our proposed method generates pathloss estimations that are very close to estimations given by physical simulation, but much faster. Moreover, experimental results show that our method significantly outperforms previously proposed methods based on radial basis function interpolation and tensor completion.

Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and user grouping. Considering recently proposed massive MIMO systems, covariance estimation proves to be challenging due to the large number of antennas (M >> 1) employed in the base station. In this case, the number of pilot transmissions N becomes comparable to the number of antennas and standard estimators, such as the sample covariance, yield a poor estimate of the true covariance and are hence undesirable. In this paper, we propose a Maximum-Likelihood (ML) massive MIMO covariance estimator, based on a parametric representation of the channel angular spread function (ASF). The parametric representation emerges from super-resolving discrete ASF components plus approximating its continuous components using carefully chosen limited-support density function. We maximize the likelihood function using a Concave-Convex procedure, which is initialized via a non-negative least-squares optimization problem. Our simulation results show that the proposed method outperforms the state of the art in various estimation quality metrics.

In order to preserve the privacy of the users demands from other users, in this paper we formulate a novel information theoretic Device-to-Device (D2D) private caching model by adding a trusted server. In the delivery phase, the trusted server collects the users demands and sends a query to each user, who then broadcasts packets according to this query. Two D2D private caching schemes (uncoded and coded) are proposed in this paper, which are shown to be order optimal.

This paper investigates the capacity region of a discrete memoryless (DM) multiple access wiretap (MAC-WT) channel where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver but only the confidential messages need to be protected from the eavesdropper. By using random coding, we find an achievable secrecy rate region, within which perfect secrecy can be realized, i.e., all users can communicate with the legitimate receiver with arbitrarily small probability of error, while the confidential information leaked to the eavesdropper tends to zero.

In this work, we study bounds on the capacity of full-duplex Gaussian 1-2-1 networks with imperfect beamforming. In particular, different from the ideal 1-2-1 network model introduced in [1], in this model beamforming patterns result in side-lobe leakage that cannot be perfectly suppressed. The 1-2-1 network model captures the directivity of mmWave network communications, where nodes communicate by pointing main-lobe "beams" at each other. We characterize the gap between the approximate capacities of the imperfect and ideal 1-2-1 models for the same channel coefficients and transmit power. We show that, under some conditions, this gap only depends on the number of nodes. Moreover, we evaluate the achievable rate of schemes that treat the resulting side-lobe leakage as noise, and show that they offer suitable solutions for implementation.

Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number Kaare active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. Recently a concatenated coding construction for U-RA on the AWGN channel was presented, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. In this work we show that this concatenated construction can achieve a vanishing per-user error probability in the limit of large blocklength and a large number of active users at sum-rates up to the symmetric Shannon capacity, i.e. as long as KaR2(1 + KaSNR). This extends previous point-to-point optimality results about SPARCs to the unsourced multiuser scenario. Additionally, we calculate the algorithmic threshold, that is a bound on the sum-rate up to which the inner decoding can be done reliably with the low-complexity AMP algorithm.

In this work, we study the generalized degrees- of-freedom (GDoF) of downlink and uplink cellular networks, modeled as Gaussian interfering broadcast channels (IBC) and Gaussian interfering multiple access channels (IMAC), respectively. We focus on regimes of low inter-cell interference, where single-cell transmission with power control and treating inter-cell interference as noise (mc-TIN) is GDoF optimal. Recent works have identified two relevant regimes in this context: one in which the GDoF region achieved through mc-TIN for both the IBC and IMAC is a convex polyhedron without the need for time-sharing (mc-CTIN regime), and a smaller (sub)regime where mc-TIN is GDoF optimal for both the IBC and IMAC (mc-TIN regime). In this work, we extend the mc-TIN framework to cellular scenarios where channel state information at the transmitters (CSIT) is limited to finite precision. We show that in this case, the GDoF optimality of mc-TIN extends to the entire mc-CTIN regime, where GDoF benefits due to interference alignment (IA) are lost. Our result constitutes yet another successful application of robust outer bounds based on the aligned images (AI) approach.

We consider cache-aided wireless communication scenarios where each user requests both a file from an a-priori generated cacheable library (referred to as `content'), and an uncacheable `non-content' message generated at the start of the communication session. This scenario is easily found in real-world wireless networks, where the two types of traffic coexist and share limited radio resources. We focus our investigation on single-transmitter wireless networks with cache-aided receivers, where the wireless channel is modelled by a degraded Gaussian broadcast channel (GBC). For this setting, we study the (normalized) delay-rate trade-off, which characterizes the content delivery time and non-content communication rates that can be achieved simultaneously. We propose a scheme based on the separation principle, which isolates the coded caching problem from the physical layer transmission problem, and prove its information-theoretic order optimality up to a multiplicative factor of 2.01. A key insight emerging from our scheme is that substantial amounts of non-content traffic can be communicated while maintaining the minimum content delivery time, achieved in the absence of non-content messages; compliments of `topological holes' arising from asymmetries in wireless channel gains.

We focus on two basic millimeter wave (mmWave) relay networks and for each network, we propose three beam scheduling methods to approach the network information theoretic capacity. The proposed beam scheduling methods include the deterministic horizontal continuous edge coloring (HC-EC) scheduler, the adaptive back pressure (BP) scheduler and the adaptive low-delay new back pressure (newBP) scheduler. With the aid of computer simulations, we show that within the network capacity range, the proposed schedulers provide good guarantees for the network stability, meanwhile achieve very low packet end-to-end delay.

This paper considers cache-aided device-to-device (D2D) networks where a trusted server helps to preserve the privacy of the users' demands. Specifically, the trusted server collects the users' demands before the delivery phase and sends a query to each user, who then broadcasts multicast packets according to this query. Recently the Authors proposed a D2D private caching scheme that was shown to be order optimal except for the very low memory size regime, where the optimality was proved by comparing to a converse bound without privacy constraint. The main contribution of this paper is a novel converse bound for the studied model where users may collude (i.e., some users share cache contents and demanded files, and yet cannot infer what files the remaining users have demanded) and under the placement phase is uncoded. To the best of the Author's knowledge, such a general bound is the first that genuinely accounts for the demand privacy constraint. The novel converse bound not only allows to show that the known achievable scheme is order optimal in all cache size regimes (while the existing converse bounds cannot show it), but also has the potential to be used in other variants of demand private caching.

In the shared-link coded caching problem, formulated by Maddah-Ali and Niesen (MAN), each cache-aided user demands one file (i.e., single file retrieval). This paper generalizes the MAN problem so as to allow users to request scalar linear functions (aka, linear combinations with scalar coefficients) of the files. We propose a novel coded delivery scheme, based on MAN uncoded cache placement, that allows for the decoding of arbitrary scalar linear functions of the files on arbitrary finite fields. Surprisingly, it is shown that the load for cache-aided scalar linear function retrieval depends on the number of linearly independent functions that are demanded, akin to the cache-aided single-file retrieval problem where the load depends on the number of distinct file requests. The proposed scheme is proved to be optimal under the constraint of uncoded cache placement, in terms of worst-case load, and within a factor 2 otherwise.

We study the optimality of power control and treating interference as noise (TIN) in the M × N X channel, from the generalized degrees-of-freedom (GDoF) and constant- gap capacity perspectives. A result by Geng, Sun and Jafar shows that if there exist K = min(M, N) transmitter-receiver pairs such that each direct link strength is no less than the sum of the strongest incoming and strongest outgoing cross link strengths (all in dB), then it is optimal to reduce the M × N X channel to a K-user interference channel and use TIN. The proof of this result relies on a deterministic approximation of the original Gaussian network, specifically for the case M <; N. Here we present a simpler proof by working directly with the original Gaussian network. Our proof relies on a new "less noisy under interference" order exhibited by TIN-optimal M × N X channels, akin to the "less noisy" order in broadcast channels.

Millimeter wave band communication is an enabler technology for multi Gbps data rates, though with associated technical challenges. Among them are the very high pathloss and the crucial importance of smart signal processing to overcome it. This paper addresses the problem of identifying the location of the strongest scatterers of the channel in the angle domain. The base station in our system operates agnostically with respect to the users, who estimate the strongest path locally and communicate it back just at the end of the process, resulting in a scalable system. The users decide adaptively the directions to test based on a posterior matching approach. Numerical results confirm the validity of our scheme, especially when the time available for beam alignment is limited.

Commercial off-the-shelf (COTS) IEEE 802.11ac WiFi systems only use a low number of antennas. This limits the multi-user MIMO (MU-MIMO) performance and, hence, the throughput of such systems, especially in dense environments. We present a unique solution based on hybrid digital-analog (HDA) beamforming to overcome the limitation of COTS WiFi hardware and effectively increase the MU-MIMO gain and to unlock the potential of MU-MIMO in WiFi. We use COTS WiFi hardware in combination with a self-developed beamforming module and novel algorithms for the HDA approach, leveraging the MU-MIMO precoding of the WiFi system. The implemented control and signal processing software is fully transparent to the WiFi part, i.e., no protocol changes are needed. We demonstrate the increase in MU-MIMO gain using unmodified COTS end-user terminals.

This paper investigates the secrecy rate performance of a device-to-device (D2D) underlaid cellular network with an eavesdropper. Both the base station (BS) and the eavesdropper are assumed to have multiple antennas and apply minimum mean-square error (MMSE) receivers for detection. Different from the literature, mainly focused on enhancing the security of cellular communication using D2D jammers while neglected the secrecy performance of D2D communication, this paper aims to maximize the instantaneous weighted sum secrecy rate (IWSSR) of both cellular and D2D users. The maximization of IWSSR with respect to the transmit power of mobile users is a non-convex problem with non-differentiable objective function. In order to obtain efficient feasible solutions, we transform the IWSSR maximization problem to a min-max problem, relax the non-negative operator in secrecy rate expressions and then propose an alternative algorithm to solve the remaining problem. Simulation results show that the IWSSR of the network can be effectively increased by the proposed algorithm and, compared with exhaustive search (ES), the proposed algorithm performs very close to ES and involves much less computational complexity.

We consider the information-theoretic optimality of treating inter-cell interference as noise (multi-cell TIN) in downlink cellular networks. We focus on scenarios modeled by the Gaussian interfering broadcast channel (IBC), comprising K mutually interfering Gaussian broadcast channels (BCs), each formed by a base station communicating independent messages to an arbitrary number of users. We establish a new power allocation duality between the IBC and its dual interfering multiple access channel (IMAC), which entails that the corresponding generalized degrees-of-freedom regions achieved through multi-cell TIN and power control (TINA regions) for both networks are identical. As by-products of this duality, we obtain an explicit characterization of the IBC TINA region from a previously established characterization of the IMAC TINA region; and identify a multi-cell convex-TIN regime in which the IBC TINA region is a polyhedron (hence convex) without the need for time-sharing. We then identify a smaller multi-cell TIN regime in which the IBC TINA region is optimal and multi-cell TIN achieves the entire capacity region of the IBC, up to a constant gap. This is accomplished by deriving a new genie-aided outer bound for the IBC, that reveals a novel BC-type order that holds amongst users in each constituent BC (or cell) under inter-cell interference, which in turn is not implied by previously known BC-type orders (i.e. degraded, less noisy and more capable orders). The multi-cell TIN regime that we identify for the IBC coincides with a corresponding multi-cell TIN regime previously identified for the IMAC, hence establishing a comprehensive uplink-downlink duality of multi-cell TIN in the GDoF (and approximate capacity) sense.

This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We also note that the achievable rate lower bound based on massive MIMO “channel hardening”, widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.

Data shuffling of training data among different computing nodes (workers) has been identified as a core element to improve the statistical performance of modern large-scale machine learning algorithms. Data shuffling is often considered as one of the most significant bottlenecks in such systems due to the heavy communication load. Under a master-worker architecture (where a master has access to the entire dataset and only communication between the master and the workers is allowed) coding has been recently proved to considerably reduce the communication load. This work considers a different communication paradigm referred to as decentralized data shuffling, where workers are allowed to communicate with one another via a shared link. The decentralized data shuffling problem has two phases: workers communicate with each other during the data shuffling phase, and then workers update their stored content during the storage phase. The main challenge is to derive novel converse bounds and achievable schemes for decentralized data shuffling by considering the asymmetry of the workers' storages (i.e., workers are constrained to store different files in their storages based on the problem setting), in order to characterize the fundamental limits of this problem. For the case of uncoded storage (i.e., each worker directly stores a subset of bits of the dataset), this paper proposes converse and achievable bounds (based on distributed interference alignment and distributed clique-covering strategies) that are within a factor of 3/2 of one another. The proposed schemes are also exactly optimal under the constraint of uncoded storage for either large storage size or at most four workers in the system.

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.

We consider a joint radar parameter estimation and communication system using orthogonal time frequency space (OTFS) modulation. The scenario is motivated by vehicular applications where a vehicle (or the infrastructure) equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. Provided that the radar-equipped transmitter is ready to send data to its target receiver, this setting naturally assumes that the receiver has been already detected. In a point-to-point communication setting over multipath time-frequency selective channels, we study the joint radar and communication system from two perspectives, i.e., the radar parameter estimation at the transmitter as well as the data detection at the receiver. For the radar parameter estimation part, we derive an efficient approximated Maximum Likelihood algorithm and the corresponding Cramér-Rao lower bound for range and velocity estimation. Numerical examples demonstrate that multi-carrier digital formats such as OTFS can achieve as accurate radar estimation as state-of-the-art radar waveforms such as frequency-modulated continuous wave (FMCW). For the data detection part, we focus on separate detection and decoding and consider a soft-output detector that exploits efficiently the channel sparsity in the Doppler-delay domain. We quantify the detector performance in terms of its pragmatic capacity, i.e., the achievable rate of the channel induced by the signal constellation and the detector soft-output. Simulations show that the proposed scheme outperforms concurrent state-of-the-art solutions. Overall, our work shows that a suitable digitally modulated waveform enables to efficiently operate joint radar parameter estimation and communication by achieving full information rate of the modulation and near-optimal radar estimation performance. Furthermore, OTFS appears to be particularly suited to the scope.

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to the low power consumption with respect to its fully digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures that can be regarded as extreme cases, namely, the fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In the FC architecture each RF antenna port is connected to all antenna elements of the array, while in the OSPS architecture the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition and data communication phases, such that the latter takes place by using the beam direction information obtained by the former. We use the state-of-the-art beam alignment (BA) scheme previously proposed by the authors and consider a family of MU-MIMO precoding schemes well adapted to the beam information extracted from the BA phase. We also evaluate the power efficiency of the two HDA architectures taking into account the power dissipation at different hardware components as well as the power backoff under typical power amplifier constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, while the OSPS architecture is advantageous with respect to the FC case in terms of hardware complexity and power efficiency, at the sole cost of a slightly longer BA time-to-acquisition due to its reduced beam angle resolution.

We propose a novel randomized channel sparsifying hybrid precoding (RCSHP) design to reduce the signaling overhead of channel estimation and the hardware cost and power consumption at the base station (BS), in order to fully harvest benefits of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. RCSHP allows time-sharing among multiple analog precoders, each serving a compatible user group. The analog precoder is adapted to the channel statistics to properly sparsify the channel for the associated user group, such that the resulting effective channel (product of channel and analog precoder) not only has enough spatial degrees of freedom (DoF) to serve this group of users, but also can be accurately estimated under the limited pilot budget. The digital precoder is adapted to the effective channel based on the duality theory to facilitate the power allocation and exploit the spatial multiplexing gain. We formulate the joint optimization of the time-sharing factors and the associated sets of analog precoders and power allocations as a general utility optimization problem, which considers the impact of effective channel estimation error on the system performance. Then we propose an efficient stochastic successive convex approximation algorithm to provably obtain Karush-Kuhn-Tucker (KKT) points of this problem.

In this paper, the capacity scaling of multicell massive MIMO systems is investigated in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. In order to achieve the capacity results, spatial (de)spreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users per cell scales linearly with base station antennas in multicell environments, unlimited capacity is achievable under the sparse angular support model as long as the effective signal-to-noise ratio is away from zero.

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to its low power consumption with respect to its digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures for HDA MU-MIMO, i.e., the fully-connected (FC) architecture where each RF antenna port is connected to all antenna elements of the array; and the one-stream-per-subarray (OSPS) architecture where the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition phase and data communication phase, such that the latter takes place by using the beam direction information obtained in the former phase. For each phase, we propose our own BA and precoding schemes that outperform the counterparts in the literature. We also evaluate the power efficiency of the two HDA architectures taking into account the practical hardware impairments, e.g., the power dissipation at different hardware components as well as the potential power backoff under typical power amplifier (PA) constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, but the OSPS architecture outperforms the FC case in terms of hardware complexity and power efficiency, only at the cost of a slightly longer time of initial beam acquisition.

In this paper, we study multiple-antenna wireless communication networks, where a large number of devices simultaneously communicate with an access point. The capacity region of multiple-input multiple-output massive multiple access channels (MIMO mMAC) is investigated. While joint typicality decoding is utilized to establish the achievability of capacity region for conventional MAC with fixed number of the users, the technique is not directly applicable for the MIMO mMAC. Instead, an information-theoretic approach based on Gallager's error exponent analysis is exploited to characterize the finite dimension region of the MIMO mMAC. Theoretical results reveal that the region is dominated by the sum rate constraint only, and the individual user rates are dominated by specific factors that correspond to the allocation of the sum rate. The rate in conventional MAC is not achievable when the number of users is comparable with codelength, which is due to the fact that successive interference cancellation cannot guarantee an arbitrary small error decoding probability for MIMO mMAC. The results further imply that, asymptotically, the individual user rate is independent of the number of transmit antennas, and channel hardening makes the individual user rate close to that when only statistic knowledge of channel is available at transmitter. The finite dimension region of MIMO mMAC is a generalization of the symmetric rate in Chen et al. (2017).

In the context of coded caching in a K-user, L-cells cellular network, we show that by using a simple decentralized content base replication scheme and exploiting spatial reuse, a very good file delivery throughput with small file subpacketization order can be achieved. In particular, this is close to the throughput achieved in a L-servers linear network or L-antenna fully connected MIMO network (up to a multiplicative factor that depends mainly on the cellular frequency reuse), where the coded caching gain and the L-fold spatial multiplexing act additively. This is another manifestation of the fact that spatial reuse is a key approach to tackle the subpacketization order problem of coded caching.

This paper studies the 1-2-1 half-duplex network model, where two half-duplex nodes can communicate only if they point "beams" at each other; otherwise, no signal can be exchanged or interference can be generated. The main result of this paper is the design of two polynomial-time algorithms that: (i) compute the approximate capacity of the 1-2-1 half-duplex network and, (ii) find the network schedule optimal for the approximate capacity. The paper starts by expressing the approximate capacity as a linear program with an exponential number of constraints. A core technical component consists of building a polynomial-time separation oracle for this linear program, by using algorithmic tools such as perfect matching polytopes and Gomory-Hu trees.

This paper studies the multicast capacity of full-duplex 1-2-1 networks. In this model, two nodes can communicate only if they point "beams" at each other; otherwise, no signal can be exchanged. The main result of this paper is that the approximate multicast capacity can be computed by solving a linear program in the activation times of links connecting pairs of nodes. This linear program has two appealing features: (i) it can be solved in polynomial-time in the number of nodes; (ii) it allows to efficiently find a network schedule optimal for the approximate capacity. Additionally, the relation between the approximate multicast capacity and the minimum approximate unicast capacity is studied. It is shown that the ratio between these two values is not universally equal to one, but it depends on the number of destinations in the network, as well as graph-theoretic properties of the network.

This paper studies the optimal achievable performance of compressed sensing based unsourced random-access communication over the real AWGN channel. "Unsourced" means that every user employs the same codebook. This paradigm, recently introduced by Polyanskiy, is a natural consequence of a very large number of potential users of which only a finite number is active in each time slot. The resemblance of compressed sensing based communication and sparse regression codes (SPARCs), a novel type of point-to-point channel codes, allows us to design and analyse an efficient unsourced random-access code. Finite blocklength simulations show that the combination of AMP decoding, with suitable approximations, together with an outer code recently proposed by Amalladinne et. al. outperforms state of the art methods in terms of required energyper-bit at lower decoding complexity.

A memoryless state-dependent multiple access channel (MAC) is considered where two transmitters wish to convey a respective message to a receiver while simultaneously estimating the respective channel state via generalized feedback. The scenario is motivated by a joint radar and communication system where the radar and data applications share the same bandwidth. An achievable capacity-distortion tradeoff region is derived that outperforms a resource-sharing scheme through a binary erasure MAC with binary states.

We investigate non-negative least squares (NNLS) for the recovery of sparse non-negative vectors from noisy linear and biased measurements. We build upon recent results from [1] showing that for matrices whose row-span intersects the positive orthant, the nullspace property (NSP) implies compressed sensing recovery guarantees for NNLS. Such results are as good as for ℓ1-regularized estimators but require no tuning at all. A bias in the sensing matrix improves this auto-regularization feature of NNLS and the NSP then determines the sparse recovery performance only. We show that NSP holds with high probability for shifted symmetric subgaussian matrices and its quality is independent of the bias. As tool for proving this result we established a debiased version of Mendelson's small ball method.

This paper studies the fundamental limits of the shared-link caching problem with correlated files, where a server with a library of N files communicates with K users who can store M files. Given an integer r G ∈ [N], correlation is modelled as follows: each r-subset of files contains one and one only common block. The tradeoff between the cache size and the average transmitted load is considered. First, a converse bound under the constraint of uncoded cache placement (i.e., each user directly caches a subset of the library bits) is derived. Then, an interference alignment scheme is proposed. The proposed scheme achieves the optimal average load under uncoded cache placement to within a factor of 2 in general, and it is exactly optimal for (i) users demand distinct files, (ii) large or small cache size, namely KrM/N ≤ 2 or KrM/N ≥ K - 1, and (iii) large or small correlation, namely r ∈{1, 2, N - 1, N}. As a by-product, the proposed scheme reduces the (worst-case or average) load of existing schemes for the caching problem with multi-requests.

This paper considers a novel cache-aided Fog Radio Access Network (Fog-RAN) architecture including a Macro-cell Base Station (MBS), several Small-cell Base Stations (SBSs), and users. Some users, not in the reach of any SBS, are directly served by the MBS, while the other users are "offloaded" and receive information only from the SBSs through high throughput links. In order to alleviate the load in the wireless front-haul links between the MBS and the SBSs, caching is employed at the SBSs. The MBS sends coded packets to the SBSs and to the directly served users via wireless multicast transmission on a common downlink channel, modeled as an error-free shared link of fixed capacity. Subsequently, the SBSs communicate among one another in a Device-to-Device (D2D) fashion so as each SBS obtains enough information to decode the files demanded by its connected users. The access links between SBSs and users are assumed to operate at a sufficiently high rate such that they are not the system bottleneck. For this novel Fog-RAN model, the memory-loads tradeoff for the worst-case demands is investigated. The main contributions of this paper are: (i) a novel symmetric inter-file coded cache placement scheme, (ii) a novel D2D delivery scheme to handle the inter-SBS communication phase, that is order optimal when each SBS serves the same number of users, and (iii) a novel asymmetric cache placement with file subpacketization dependent on the network structure, which is exactly optimal in some memory size regimes.

We use stochastic network calculus to investigate the delay performance of a multiuser MISO system with zero-forcing beamforming. First, we consider ideal assumptions with long codewords and perfect CSI at the transmitter, where we observe a strong channel hardening effect that results in very high reliability with respect to the maximum delay of the application. We then study the system under more realistic assumptions with imperfect CSI and finite blocklength channel coding. These effects lead to interference and to transmission errors, and we derive closed-form approximations for the resulting error probability. Compared to the ideal case, imperfect CSI and finite length coding cause massive degradations in the average transmission rate. Surprisingly, the system nevertheless maintains the same qualitative behavior as in the ideal case: as long as the average transmission rate is higher than the arrival rate, the system can still achieve very high reliability with respect to the maximum delay.

The decentralized coded caching scheme of Maddah-Ali and Niesen for the shared link network achieves an order-optimal memory-load tradeoff when the file size goes to infinity. It is then successively shown by Shanmugam et al. that, in the practical operating regime where the file size is finite, such a scheme yields a much less attractive coded caching gain. In this paper, we focus on designing decentralized coded caching schemes that can achieve low worst case loads of the shared link when the file size is finite and maintain order-optimal memory-load tradeoffs when the file size grows to infinity. First, we propose a decentralized coded caching design framework for designing decentralized coded caching schemes that can achieve significantly lower worst case loads than Maddah-Ali-Niesen's decentralized coded caching scheme in the finite file size regime while maintaining order-optimal memory-load tradeoffs when the file size grows to infinity. Then, within the proposed framework, we propose a decentralized coded caching scheme, which is simple and tractable, and can achieve a low worst case load in both the finite and infinite file size regimes. We analyze the worst case load of the proposed scheme and show that it outperforms Maddah-Ali-Niesen's and Shanmugam et al.'s decentralized schemes in the finite file size regime when the number of users is not too small. We also analyze the asymptotic worst case load of the proposed scheme when the file size goes to infinity and show that the proposed scheme achieves an order-optimal memory-load tradeoff. Finally, we analytically characterize the behavior of the worst case coded caching gain of the proposed scheme as a function of the required file size when the file size is large.

In this paper, we study the design of secure communication for time-division duplex multi-cell multi-user massive multiple-input-multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission of the users. We exploit both the received pilot's and the received data signals for uplink channel estimation. We show analytically that when both the number of transmit antennas and the length of the data vector tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station, provided their signal powers are different. This finding reveals that decreasing (instead of increasing) the desired user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this observation, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. For the special case of a single-cell single-user system with independent and identically distributed fading, the obtained expression reveals that the secrecy rate scales logarithmically with the number of transmit antennas. This is the same scaling law as for the achievable rate of a single-user massive MIMO system in the absence of eavesdroppers. The numerical results indicate that the proposed scheme achieves significant secrecy rate gains compared with alternative approaches based on matched filter precoding with artificial noise generation and null space transmission.

We consider a cache-aided wireless device-to-device (D2D) network of the type introduced by Ji et al., where the placement phase is orchestrated by a central server. We assume that the devices' caches are filled with uncoded data, and the whole content database is contained in the collection of caches. After the cache placement phase, the files requested by the users are serviced by inter-device multicast communication. For such a system setting, we provide the exact characterization of the optimal load-memory trade-off under the assumptions of uncoded placement and one-shot delivery. In particular, we derive both the minimum average (under uniformly distributed demands) and the minimum worst-case sum-load of the D2D transmissions, for given individual cache memory size at disposal of each user. Furthermore, we show that the performance of the proposed scheme is within factor 4 of the information-theoretic optimum. Capitalizing on the one-shot delivery property, we also propose an extension of the presented scheme that provides robustness against random user inactivity.

We study a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such a channel, quantize-map-and-forward (QMF) [and its generalization of noisy network coding (NNC) and short message NNC] achieves the cut-set upper bound within a constant additive gap, where the gap grows linearly with the number of relay stages K. This gap, however, may not be acceptable for practical communication systems with multihop transmissions (e.g., a wireless backhaul operating at high frequencies). Recently, we improved the capacity scaling by using a forward sliding-window (SW) decoding and by optimizing the quantization level at each relay, obtaining the gap that grows logarithmically as log K. Furthermore, the improved scheme has lower decoding complexity and delay than the general QMF and NNC approaches. In this paper, we further improve the performance by presenting a mixed scheme in which each relay can perform either decode-and-forward (DF) or the improved QMF (with SW decoding) and can choose to perform rate-splitting to enable partial interference cancellation. In general, the optimization of the relay DF/QMF configuration is combinatorial. Nevertheless, we provide that a simple greedy algorithm finds an optimal configuration under some practically reasonable assumptions. We derive an achievable rate that is easily computable and show that the proposed mixed scheme outperforms the QMF-only schemes. We demonstrate that the performance improvement increases with K, which indicates that the mixed scheme is indeed beneficial for multihop transmission.

We investigate the potentials of applying the coded caching paradigm in wireless networks. In order to do this, we investigate physical layer schemes for downlink transmission from a multiantenna transmitter to several cache-enabled users. As the baseline scheme, we consider employing coded caching on the top of max-min fair multicasting, which is shown to be far from optimal at high-SNR values. Our first proposed scheme, which is near-optimal in terms of DoF, is the natural extension of multiserver coded caching to Gaussian channels. As we demonstrate, its finite SNR performance is not satisfactory, and thus we propose a new scheme in which the linear combination of messages is implemented in the finite field domain, and the one-shot precoding for the MISO downlink is implemented in the complex field. While this modification results in the same near-optimal DoF performance, we show that this leads to significant performance improvement at finite SNR. Finally, we extend our scheme to the previously considered cache-enabled interference channels, and moreover we provide an ergodic rate analysis of our scheme. Our results convey the important message that although directly translating schemes from the network coding ideas to wireless networks may work well at high-SNR values, careful modifications need to be considered for acceptable finite SNR performance.

The coded caching scheme proposed by Maddah-Ali and Niesen (MAN) critically hinges on the ability of the system to deliver a common coded multicast message from a server to all users in the system at a fixed rate, independent of the number of users. In order to apply this paradigm to a spatially distributed wireless network, it is important to make sure that such a common multicast rate does not vanish, as the number of users in the network and/or the network area increase. This paper starts from a variant of the MAN scheme successively proposed for the so-called combination network, where the multicast message is further encoded by a maximum distance separable (MDS) code, and the MDS-coded blocks are sent to multiple spatially distributed single-antenna edge nodes (ENs), transmitting at a fixed rate with no channel state information. The users have multiple antennas. They obtain receiver channel state information from the standard downlink pilots and can select to decode a desired number of EN transmissions while either nulling or treating as noise the others. The system is reminiscent of the so-called evolved Multimedia Broadcast Multicast Service, since the fundamental underlying transmission mechanism is multipoint multicasting, where each user can independently (in a user-centric manner) decide which EN to decode, without any explicit association of users with ENs. We study the performance of the proposed system when users and ENs are distributed according to homogeneous Poisson point processes in the plane, and the propagation is affected by Rayleigh fading and distance-dependent pathloss. Our analysis allows the optimization of the PHY parameters (PHY coding rate at the ENs and MDS coding rate) for given MAN scheme parameters. The proposed scheme achieves full spatial scalability in the following sense: for an extended network with arbitrary constant ratio of users per EN and area A = O(NE), where NE denotes the number of ENs, the system achieves a per-user delivery rate that does not vanish as NE→ ∞.

In Time Division Duplex reciprocity-based massive MIMO, it is essential to compute the downlink precoding matrix over all OFDM resource blocks within a small fraction of the uplink-downlink slot duration. Because of this harsh computation latency constraint, early implementations of massive MIMO considered the simple Conjugate Beamforming (ConjBF) precoding method. On the other hand, it is well-known that in the regime of a large but finite number of antennas, the Regularized Zero-Forcing (RZF) precoding is generally much more effective than ConjBF. In order to close the gap between ConjBF and RZF, while meeting the latency constraint, truncated polynomial expansion (TPE) methods have been proposed. In this paper, we present a novel TPE method that outperforms previously proposed methods in the non-symmetric case of users with different channel correlations, subject to the condition that the covariance matrices of the user channel vectors can be approximated, for a large number of antennas, by a family of matrices with common eigenvectors. This condition is met, for example, by uniform linear and uniform planar arrays in far-field conditions. The proposed method is computationally simple and lends itself to classical power allocation optimization such as min-sum power and max-min rate. We provide a detailed analysis of the computation latency vs computation resources, specifically targeted to a highly parallel FPGA hardware architecture. We conclude that the proposed TPE method can effectively close the performance gap between ConjBF and RZF with computation latency of less than one LTE OFDM symbol, as assumed in Marzetta's work on massive MIMO.

The decoupling of data and control planes in the forthcoming 5G wireless networks enables the efficient implementation of multitier architectures, where coverage and connectivity are guaranteed by top-tier macro-cells, and at the same time, high throughput and low latency are achieved locally by lower tiers in the hierarchy. This paper considers a new architecture for such lower tiers, dubbed fog massive multi-in multi-out (MIMO), where the user equipment (UE) nodes can connect to a “fog” of the densely deployed remote radio heads (RRHs) in a seamless, user-centric, and opportunistic manner. In the case of dense small cells, traditional cellular architectures inherently give rise to frequent handovers and pilot sequence re-assignments, which typically incur excessive protocol overhead and latency. In the proposed fog massive MIMO architecture, the UEs implicitly associate themselves with the most convenient RRHs in a completely autonomous manner. Each UE makes use of the uplink pilot sequences that contain equal-weight codewords and enable a novel “on-the-fly” pilot contamination control mechanism. We analyze the spectral efficiency and the outage probability of the proposed architecture via stochastic geometry, using some recent results on unique coverage in Boolean models, and provide a detailed comparison with the benchmark represented by massive MIMO cellular system with a genie-aided minimum-distance user-cell association. Our analysis, corroborated by extensive system simulation, reveals that there exists a “sweet spot” of the per pilot user load (number of users per pilot), such that the proposed system achieves a spectral efficiency close to that of the genie-aided cellular system. In these conditions, it is possible to achieve the low protocol overhead and low user RRH association latency promised by the fog massive MIMO at virtually no significant performance cost in terms of system spectral efficiency with respect to the cellular benchmark.

We propose a novel method for massive multiple-input multiple-output (massive MIMO) in frequency division duplexing (FDD) systems. Due to the large frequency separation between uplink (UL) and downlink (DL) in FDD systems, channel reciprocity does not hold. Hence, in order to provide DL channel state information to the base station (BS), closed-loop DL channel probing, and channel state information (CSI) feedback is needed. In massive MIMO, this typically incurs a large training overhead. For example, in a typical configuration with M ≃200 BS antennas and fading coherence block of T ≃ 200 symbols, the resulting rate penalty factor due to the DL training overhead, given by max{0, 1 - M/T }, is close to 0. To reduce this overhead, we build upon the well-known fact that the angular scattering function of the user channels is invariant over frequency intervals whose size is small with respect to the carrier frequency (as in current FDD cellular standards). This allows us to estimate the users' DL channel covariance matrix from UL pilots without additional overhead. Based on this covariance information, we propose a novel sparsifying precoder in order to maximize the rank of the effective sparsified channel matrix subject to the condition that each effective user channel has sparsity not larger than some desired DL pilot dimension Tdl, resulting in the DL training overhead factor max{0, 1 - Tdl/T } and CSI feedback cost of Tdl pilot measurements. The optimization of the sparsifying precoder is formulated as a mixed integer linear program, that can be efficiently solved. Extensive simulation results demonstrate the superiority of the proposed approach with respect to the concurrent state-of-the-art schemes based on compressed sensing or UL/DL dictionary learning.

This paper studies the performance of delay-constrained hybrid automatic repeat request (HARQ) protocols. Particularly, we propose a fast HARQ protocol where, to increase the end-to-end throughput, some HARQ feedback signals and successive message decodings are omitted. Considering quasi-static channels and a bursty communication model, we derive closed-form expressions for the message decoding probabilities as well as the throughput, the expected delay, and the error probability of the HARQ setups. The analysis is based on the recent results on the achievable rates of finite-length codes and shows the effect of the codeword length on the system performance. Moreover, we evaluate the effect of various parameters, such as imperfect channel estimation and hardware on the system performance. As demonstrated, the proposed fast HARQ protocol reduces the packet transmission delay considerably compared with the state-of-the-art HARQ schemes. For example, with typical message decoding delay profiles and a maximum of 2,..., 5 transmission rounds, the proposed fast HARQ protocol can improve the expected delay compared with standard HARQ by 27%, 42%, 52% and 60%, respectively, independently of the code rate/fading model.

Communication at millimeter wave (mm-wave) bands is expected to become a key ingredient of the next generation (5G) wireless networks. Effective mm-wave communications require fast and reliable methods for beamforming at both the user equipment (UE) and the base station sides, in order to achieve a sufficiently large signal-to-noise ratio after beamforming. We refer to the problem of finding a pair of strongly coupled narrow beams at the transmitter and receiver as the beam alignment problem. In this paper, we propose an efficient BA scheme for single-carrier mm-wave communications. In the proposed scheme, the BS periodically probes the channel in the downlink via a pre-specified pseudo-random beamforming codebook and pseudo-random spreading codes, letting each UE estimate the angle-of-arrival/angle-of-departure (AoA-AoD) pair of the multipath channel for which the energy transfer is maximum. We leverage the sparse nature of mm-wave channels in the AoA-AoD domain to formulate the BA problem as the estimation of a sparse non-negative vector. Based on the recently developed non-negative least squares technique, we efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We evaluate the performance of the proposed scheme under a realistic channel model, where the propagation channel consists of a few multipath components each having different delays, AoAs-AoDs, and Doppler shifts. The channel model parameters are consistent with the experimental channel measurements. The simulation results indicate that the proposed method is highly robust to fast channel variations caused by the large Doppler spread between the multipath components. Furthermore, we also show that after achieving BA, the beamformed channel is essentially frequency-flat, such that single-carrier communication needs no equalization in the time domain.

Numerical evidence suggests that compressive sensing (CS) approaches for wideband massive MIMO channel estimation can achieve very good performance with limited training overhead by exploiting the sparsity of the physical channel. However, analytical characterization of the (minimum) training overhead requirements is still an open issue. By observing that the wideband massive MIMO channel can be represented by a vector that is not simply sparse but has well defined structural properties, referred to as hierarchical sparsity, we propose low complexity channel estimators for the uplink multiuser scenario that take this property into account. By employing the framework of the hierarchical restricted isometry property, rigorous performance guarantees for these algorithms are provided suggesting concrete design goals for the user pilot sequences. For a specific design, we analytically characterize the scaling of the required pilot overhead with increasing number of antennas and bandwidth, revealing that, as long as the number of antennas is sufficiently large, it is independent of the per user channel sparsity level as well as the number of active users. These analytical insights are verified by simulations demonstrating also the superiority of the proposed algorithm over conventional CS algorithms that ignore the hierarchical sparsity property.

We analyze a MISO downlink channel where a multi-antenna transmitter communicates with a large number of single-antenna receivers. Using linear beamforming or nonlinear precoding techniques, the transmitter can serve multiple users simultaneously during each transmission slot. However, increasing the number of users, i.e., the multiplexing gain, reduces the beamforming gain, which means that the individual data rates decrease. We use stochastic network calculus to analyze the queueing delay that occurs due to the time-varying data rates. Our results show that the optimal number of users, i.e., the optimal trade-off between multiplexing gain and beamforming gain, depends on incoming data traffic and its delay requirements.

In this paper, we propose a novel method for efficient implementation of a massive Multiple-Input Multiple- Output (massive MIMO) system with Frequency Division Duplexing (FDD) operation. Our main objective is to reduce the large overhead incurred by Downlink (DL) common training and Uplink (UL) feedback needed to obtain channel state information (CSI) at the base station. Our proposed scheme relies on the fact that the underlying angular distribution of a channel vector, also known as the angular scattering function, is a frequency-invariant entity yielding a ULDL reciprocity and has a limited angular support. We estimate this support from UL CSI and interpolate it to obtain the corresponding angular support of the DL channel. Finally we exploit the estimated support of the DL channel of all the users to design an efficient channel probing and feedback scheme that maximizes the total spectral efficiency of the system. Our method is different from the existing compressed-sensing (CS) based techniques in the literature. Using support information helps reduce the feedback overhead from O(s logM) in CS techniques to O(s) in our proposed method, with s andM being sparsity order of the channel vectors and the number of base station antennas, respectively. Furthermore, in order to control the channel sparsity and therefore the DL common training and UL feedback overhead, we introduce the novel concept of active channel sparsification. In brief, when the fixed pilot dimension is less than the required amount for reliable channel estimation, we introduce a pre-beamforming matrix that artificially reduces the effective channel dimension of each user to be not larger than the DL pilot dimension, while maximizing both the number of served users and the number of probed angles. We provide numerical experiments to assess the performance of our method and compare it with the state-of-the-art CS technique.

Millimeter-Wave (mmWave) communication has come into the spotlight as an enabling approach for next generation wireless networks. Communication at mmWave is however challenging due to the large path loss and limited power. This implies that antenna arrays with large directional gain are required both at the Base Station (BS) and the user sides. Finding the strongest narrow beam pair connecting the BS and the user is referred to as Beam Alignment (BA). In this paper, we propose an efficient BA scheme for multi-user systems via estimating the second order statistics of the channel. In the proposed scheme, the BS probes the channel in the downlink letting each user estimate its own channel, where all the users within the BS coverage are trained simultaneously. We formulate the channel estimation at the user side as a Compressed Sensing (CS) of a non- negative sparse vector and use the recently developed Non- Negative Least Squares (NNLS) technique to solve it efficiently. We evaluate our method via numerical simulations and compare it with other competitive algorithms. It has been verified that the proposed approach incurs less training overhead, exhibits higher efficiency in multi-user scenarios, and is highly robust to fast time- varying channels.

We propose a novel scheme for estimating the large- scale gains of the channels between user terminals (UTs) and base stations (BSs) in a cellular system. The scheme leverages TDD operation, uplink (UL) training by means of properly designed non-orthogonal pilot codes, and massive antenna arrays at the BSs. Subject to Q resource elements allocated for UL training and using the new scheme, a BS is able to estimate the large- scale channel gains of K users transmitting UL pilots in its cell and in nearby cells, provided K ≤Q2. Such knowledge of the large-scale channel gains of nearby out-of-cells users can be exploited at the BS to mitigate interference to the out-of-cell users that experience the highest levels of interference from the BS. We investigate the large-scale gain estimation performance provided by a variety of non-orthogonal pilot codebook designs. Our simulations suggest that among all the code designs considered, Grassmannian line-packing type codes yield the best large-scale channel gain estimation performance.

In this paper, we study the design of secure communication for time division duplexing multi-cell multi-user massive multiple-input multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission phase of the desired users. We exploit both the received pilots and data signals for uplink channel estimation. We show analytically that when the number of transmit antennas and the length of the data vector both tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station if their signal powers are different. This finding reveals that decreasing (instead of increasing) the desire user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this result, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. Numerical results indicate that under strong active attacks, the proposed design achieves significant secrecy rate gains compared to the conventional design employing matched filter precoding and artificial noise generation.

Ahstract-This paper considers massive multiple-input multiple-output (MIMO) downlink system with imperfect channel state information at transmitter (CSIT). A new optimization framework for the joint user-scheduling, power control, and precoding design is presented. The key idea of the proposed optimization framework is to equivalently reformulate an sum-spectral efficiency maximization problem into the maximization problem of the product of Rayleigh quotients. Although this reformulated optimization problem is not convex, the sub-optimal solution that guarantees the first-order optimality condition is found using the proposed general power iteration algorithm. One major observation is that the proposed sub-optimal solutions provide significant gains in terms of the sum-spectral efficiency compared to the conventional user-scheduling algorithm conjunction with zero-forcing precoding for the massive MIMO systems.

This paper proposes a new model for wireless relay networks referred to as “1-2-1 network”, where two nodes can communicate only if they point “beams” at each other, while if they do not point beams at each other, no signal can be exchanged or interference can be generated. This model is motivated by millimeter wave communications where, due to the high path loss, a link between two nodes can exist only if beamforming gain at both sides is established, while in the absence of beamforming gain the signal is received well below the thermal noise floor. The main result in this paper is that the 1-2-1 network capacity can be approximated by routing information along at most 2N + 2 paths, where N is the number of relays connecting a source and a destination through an arbitrary topology.

In this paper, we study the problem of activity detection (AD) in a massive MIMO setup, where the Base Station (BS) has M ≫ 1 antennas. We consider a block fading channel model where the M-dim channel vector of each user remains almost constant over a coherence block (CB) containing Dc signal dimensions. We study a setting in which the number of potential users Kcassigned to a specific CB is much larger than the dimension of the CB Dc(Kc≫ Dc) but at each time slot only Ac≪ Kcof them are active. Most of the previous results, based on compressed sensing, require that Ac≤ Dc, which is a bottleneck in massive deployment scenarios such as Internet-of-Things (IoT) and Device-to-Device (D2D) communication. In this paper, we show that one can overcome this fundamental limitation when the number of BS antennas M is sufficiently large. More specifically, we derive a scaling law on the parameters (M, Dc, Kc, Ac) and also Signal-to-Noise Ratio (SNR) under which our proposed AD scheme succeeds. Our analysis indicates that with a CB of dimension Dc, and a sufficient number of BS antennas M with Ac/M=o(1), one can identify the activity of Ac=O(Dc2/log2((Kc)/(Ac))) active users, which is much larger than the previous bound Ac=O(Dc) obtained via traditional compressed sensing techniques. In particular, in our proposed scheme one needs to pay only a poly-logarithmic penalty O(log2((Kc)/(Ac))) for increasing the number of potential users Kc, which makes it ideally suited for AD in IoT setups. We propose low-complexity algorithms for AD and provide numerical simulations to illustrate our results.

In this paper, we study the problem of multiband (frequency-variant) covariance interpolation with a particular emphasis towards massive MIMO applications. In massive MIMO, the communication between each Base Station (BS) with M ≫ 1 antennas and each single-antenna user occurs through a collection of scatterers in the environment, where the channel vector of each user at BS antennas consists in a weighted linear combination of the array responses of the scatterers, where each scatterer has its own angle of arrival (AoA) and complex channel gain. The array response at a given AoA depends on the wavelength of the incoming planar wave and is naturally frequency dependent. While in typical wireless communication applications the signal bandwidth is narrow enough, such that the channel second-order statistics (notably, the channel covariance matrix) can be considered frequency independent, in many other applications such as Frequency Division Duplexing (FDD) the uplink (UL) and the downlink (DL) channels are separated by a large frequency interval, such that the dependence of the channel covariance on frequency cannot be ignored. In this paper, we show that although this dependence is generally negligible for a small number of antennas M, it results in a considerable distortion of the covariance matrix when M → ∞. Moreover, we prove that this frequency-dependent distortion can be fully compensated by a suitable covariance interpolation in frequency. We analyze the covariance interpolation problem mathematically and prove its stability under a very mild reciprocity condition on the angular power spread function (PSF) of the users. We also investigate the validity of our results using numerical simulations.

A communication setup is considered where a transmitter wishes to simultaneously sense its channel state and convey a message to a receiver. The state is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. state sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on state sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of joint sensing and communication as compared to a separation-based approach.

This paper considers a new architecture underpinned by on-the-fly pilot contamination control, termed Fog massive MIMO, where the users are able to establish high-throughput and low-latency data links in a seamless and opportunistic manner, as they travel through a dense fog of high capacity Remote Radio Heads (RRHs). Utilizing some recent results on unique coverage in Boolean models, we analyze the spectral efficiency and outage probability of the proposed architecture via stochastic geometry. Our analysis, supported by extensive system simulation, reveals that there exists a “sweet spot” of the per-pilot user load (number of users per pilot), such that the proposed system achieves spectral efficiency close to that of the ideal cellular massive MIMO baseline, while exhibiting the simplicity and low-latency of completely user-centric operations.

The use of Unmanned Aerial Vehicles (UAVs) for remote sensing and surveillance applications has become increasingly popular in the last decades. This paper investigates the communication between a UAV and a final control center (CC), using static relays located on the ground, to overcome the intermittent connectivity between the two end points, due to the UAV flight. Backpressure and FlashLinQ routing and scheduling algorithms are jointly applied to this scenario. Backpressure has been shown to be able stabilize any input traffic within the network capacity region without requiring knowledge of traffic arrival rates and channel state probabilities. FlashLinQ is used in the scheduling phase to derive a maximal feasible subset of links which can coexist on a given slot without causing harmful interference to each other. Moreover, to overcome the limit on long end-to-end delays of backpressure, we propose a modified algorithm, where relays are selected depending on their proximity to the CC and on the UAV trajectory. Through extensive simulations, we demonstrate that, compared to the benchmark solution based on backpressure, the proposed algorithm is able to reduce delay significantly without any loss in throughput gain.

Existing coded caching schemes fail to simultaneously achieve efficient content placement for non-uniform file popularity and efficient content delivery in the presence of common requests, and hence may not achieve desirable average load under a non-uniform, possibly very skewed, popularity distribution. In addition, existing coded caching schemes usually require the splitting of a file into a large number of subfiles, i.e., high subpacketization, and hence may cause huge implementation complexity. To address the above two challenges, we first present a class of centralized coded caching schemes consisting of a general content placement strategy specified by a file partition parameter, enabling efficient and flexible content placement, and a specific content delivery strategy, enabling load reduction by exploiting common requests of different users. Then we consider two cases, namely, the case without considering the subpacketization issue and the case considering the subpacke-tization issue. In the first case, we formulate the coded caching optimization problem over the considered class of schemes with N2Kvariables to minimize the average load under an arbitrary file popularity. Imposing some conditions on the file partition parameter, we transform the original optimization problem into a linear optimization problem with N(K + 1) variables under an arbitrary file popularity and a linear optimization problem with K +1 variables under the uniform file popularity. We also show that Yu et al.'s centralized coded caching scheme corresponds to an optimal solution of our problem. In the second case, taking into account the subpacketization issue, we first formulate the coded caching optimization problem over the considered class of schemes to minimize the average load under an arbitrary file popularity subject to a subpacketization constraint involving the ℓ0-norm. By imposing the same conditions and using an exact DC (difference of two convex functions) reformulation method, we convert the original problem with N2Kvariables into a simplified DC problem with N(K + 1) variables. Then, we use a DC algorithm to solve the simplified DC problem.

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, also considering an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.

Welcome to the second part of the IEEE JSAC special issue on Caching for Communication Systems and Networks. The goal of this special issue is to present the multiple facets of caching, from information theory to networking and services, and explore the role of memory in communications. This is a very timely topic due to recent technological and theoretical advances summarized in the tutorial paper that appears in the first part of the issue[1].

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution toward 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

This paper investigates a secure wireless-powered multiantenna multicasting system, where multiple power beacons (PBs) supply power to a transmitter in order to establish a reliable communication link with multiple legitimate users in the presence of multiple eavesdroppers. The transmitter has to harvest radio frequency energy from multiple PBs due to the shortage of embedded power supply before establishing its secure communication. We exploit a novel and practical scenario that the PBs and the transmitter may belong to different operators and a hierarchical energy interaction between the PBs and the transmitter is considered. Specifically, the monetary incentives are required for the PBs to assist the transmitter for secure communications. This leads to the formulation of a Stackelberg game for the secure wireless-powered multiantenna multicasting system, where the transmitter and the PB are modeled as leader and follower, respectively, each maximizing their own utility function. The closed-form Stackelberg equilibrium of the formulated game is then derived, where we study various scenarios of eavesdroppers and legitimate users that can have impact on the optimality of the derived solutions. Finally, numerical results are provided to validate our proposed schemes.

The problem of secret-key based authentication under a privacy constraint on the source sequence is considered. The identifier measurements during authentication are assumed to be controllable via a cost-constrained “action” sequence. Single-letter characterizations of the optimal trade-off among the secret-key rate, storage rate, privacy-leakage rate, and action cost are given for the four problems where noisy or noiseless measurements of the source are enrolled to generate or embed secret keys. The results are relevant for several user-authentication scenarios, including physical and biometric authentications with multiple measurements. Our results include, as special cases, new results for secret-key generation and embedding with action-dependent side information without any privacy constraint on the enrolled source sequence.

We consider a wireless device-to-device caching network where n nodes are placed on a regular grid of area A (n). Each node caches LCF (coded) bits from a library of size LF bits, where L is the number of files and F is the size of each file. Each node requests a file from the library independently according to a popularity distribution. Under a commonly used “physical model” and Zipf popularity distribution, we characterize the optimal per-node capacity scaling law for extended networks (i.e., A (n) = n). Moreover, we propose a cache-induced hierarchical cooperation scheme and associated cache content placement optimization algorithm to achieve the optimal per-node capacity scaling law. When the path loss exponent α <; 3, the optimal per-node capacity scaling law achieved by the cache-induced hierarchical cooperation can be significantly better than that achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the per-node capacity scaling law for wireless caching networks under the physical model and Zipf distribution with an arbitrary skewness parameter τ. While scaling law analysis yields clean results, it may not accurately reflect the throughput performance of a large network with a finite number of nodes. Therefore, we also analyze the throughput of the proposed cache-induced hierarchical cooperation for networks of practical size. The analysis and simulations verify that cache-induced hierarchical cooperation can also achieve a large throughput gain over the cache-assisted multihop scheme for networks of practical size.

The topological interference management (TIM) problem studies partially-connected interference networks with no channel state information except for the network topology (i.e., connectivity graph) at the transmitters. In this paper, we consider a similar problem in the uplink cellular networks, while message passing is enabled at the receivers (e.g., base stations), so that the decoded messages can be routed to other receivers via backhaul links to help further improve network performance. For this TIM problem with decoded message passing (TIM-MP), we model the interference pattern by conflict digraphs, connect orthogonal access to the acyclic set coloring on conflict digraphs, and show that one-to-one interference alignment boils down to orthogonal access because of message passing. With the aid of polyhedral combinatorics, we identify the structural properties of certain classes of network topologies where orthogonal access achieves the optimal degrees-of-freedom (DoF) region in the information-theoretic sense. The relation to the conventional index coding with simultaneous decoding is also investigated by formulating a generalized index coding problem with successive decoding as a result of decoded message passing. The properties of reducibility and criticality are also studied, by which we are able to prove the linear optimality of orthogonal access in terms of symmetric DoF for the networks up to four users with all possible network topologies (218 instances). Practical issues of the tradeoff between the overhead of message passing and the achievable symmetric DoF are also discussed, in the hope of facilitating efficient backhaul utilization.

Massive MIMO is a variant of multi-user MIMO in which the number of antennas at the base station (BS) M is very large and typically much larger than the number of served users (data streams) K. Recent research has widely investigated the system-level advantages of the massive MIMO, and in particular, the beneficial effect of increasing the number of antennas M. These benefits, however, come at the cost of a dramatic increase in hardware and computational complexity. This is partly due to the fact that the BS needs to compute precoding/receiving vectors in order to coherently transmit/detect data to/from each user, where the resulting complexity grows proportionally to the number of antennas M and the number of served users K. Recently, different algorithms based on tools from asymptotic random matrix theory and/or approximated message passing have been proposed to reduce such complexity. The underlying assumption in all these techniques, however, is that the exact statistics (covariance matrix) of the channel vectors of the users is a priori known. This is far from being realistic, especially taking into account that, in the high-dim regime of M ≫ 1, estimating the channel covariance matrices of the users is also challenging in terms of both computation and storage requirements. In this paper, we propose a novel technique for computing the precoder/detector in a massive MIMO system. Our method is based on the randomized Kaczmarz algorithm and does not require a priori knowledge of the statistics of users' channel vectors. We analyze the performance of our proposed algorithm theoretically and compare its performance with that of other techniques based on random matrix theory and approximate message passing via numerical simulations. Our results indicate that our proposed technique is computationally very competitive and yields quite a comparable performance while it does not require the knowledge of the statistics of users' channel vectors.

A well-known lower bound widely used in the massive MIMO literature hinges on channel hardening, i.e., the phenomenon for which, thanks to the large number of antennas, the effective channel coefficients resulting from beamforming tend to deterministic quantities. If the channel hardening effect does not hold sufficiently well, this bound may be quite far from the actual achievable rate. In recent developments of massive MIMO, several scenarios where channel hardening is not sufficiently pronounced have emerged. These settings include, for example, the case of small scattering angular spread, yielding highly correlated channel vectors, and the case of cell-free massive MIMO. In this short contribution, we present two new bounds on the achievable ergodic rate that offer a complementary behavior with respect to the classical bound: while the former performs well in the case of channel hardening and/or when the system is interference-limited (notably, in the case of finite number of antennas and conjugate beamforming transmission), the new bounds perform well when the useful signal coefficient does not harden but the channel coherence block length is large with respect to the number of users, and in the case where interference is nearly entirely eliminated by zero-forcing beamforming. Overall, using the most appropriate bound depending on the system operating conditions yields a better understanding of the actual performance of systems where channel hardening may not occur, even in the presence of a very large number of antennas.

The availability of abundant spectrum makes millimeter wave (mm-wave) a prominent candidate technology for the next generation of cellular networks. Highly directional transmission is essential for the exploitation of mm-wave bands to compensate for high propagation loss. The directional transmission, nevertheless, necessitates a specific design for mm-wave initial cell discovery, as conventional omni-directional broadcasting may fail in delivering cell discovery information. To address this issue, this paper provides an analytical framework for mm-wave beamformed cell discovery based on an information-theoretic approach. Design options are compared considering four fundamental and representative broadcasting schemes to evaluate discovery latency and overhead. The schemes are then simulated under realistic system parameters. Analytical and simulation results reveal four key findings: 1) analog/hybrid beamforming performs as well as digital beamforming in terms of cell discovery latency; 2) single-beam exhaustive scan optimizes the latency and, however, leads to the overhead penalty; 3) multi-beam simultaneous scan can significantly reduce the overhead and provide the flexibility to achieve tradeoff between the latency and the overhead; and 4) the latency and the overhead are relatively insensitive to extreme low block error rates.

Millimeter-wave (mm-wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6-GHz band. Communication at mm-waves is, however, quite challenging due to the severe propagation pathloss incurred by conventional isotropic antennas. To cope with this problem, directional beamforming both at the base station (BS) side and at the user equipment (UE) side is necessary in order to establish a strong path conveying enough signal power. Finding such beamforming directions is referred to as beam alignment (BA). This paper presents a new scheme for efficient BA. Our scheme finds a strong propagation path identified by an angle-of-arrival (AoA) and angle-of-departure (AoD) pair, by exploring the AoA-AoD domain through pseudo-random multi-finger beam patterns and constructing an estimate of the resulting second-order statistics (namely, the average received power for each pseudo-random beam configuration). The resulting under-determined system of equations is efficiently solved using non-negative constrained least-squares, yielding naturally a sparse non-negative vector solution whose maximum component identifies the optimal path. As a result, our scheme is highly robust to variations of the channel time dynamics compared with alternative concurrent approaches based on the estimation of the instantaneous channel coefficients, rather than of their second-order statistics. In the proposed scheme, the BS probes the channel in the downlink and trains simultaneously an arbitrarily large number of UEs. Thus, “beam refinement,” with multiple interactive rounds of downlink/uplink transmissions, is not needed. This results in a scalable BA protocol, where the protocol overhead is virtually independent of the number of UEs, since all the UEs run the BA procedure at the same time. Extensive simulation results illustrate that our approach is superior to the state-of-the-art BA schemes proposed in the literature in terms of training overhead in multi-user scenarios and robustness to variations in the channel dynamics.

Massive MIMO is a variant of multiuser MIMO, in which the number of antennas M at the base-station is very large and generally much larger than the number of spatially multiplexed data streams to the users. It turns out that by increasing the number of antennas M at the base-station and as a result increasing the spatial resolution of the array, although the received signal from each user tends to be very high-dim, it lies on a low-dim subspace due to the limited angular spread of the user. This low-dim subspace structure can be exploited to improve estimation of the channel state during the training period. For example, channel vectors of the users can be estimated by sampling only a small subset rather than the whole number of antenna elements, which reduces the number of required RF chains and A/D converters at receiver front end. Moreover, the subspace information can be used to group the users based on the similarity of their subspaces in order to serve them more efficiently. Thus, it is apparent that estimating the signal subspace of the users from low-dim noisy sketches of their channel vectors plays a crucial role in massive MIMO. In this paper, we aim to design such a subspace estimation/tracking algorithm. Our proposed algorithm requires sampling only a small number of antennas in each training period, has a very low computational complexity, and is able to track the sharp transitions in the channel statistics very quickly.

We consider a single-cell scenario involving a single base station (BS) with a massive array serving multi-antenna terminals in the downlink of a mmWave channel. We present a class of multiuser MIMO schemes, which rely on uplink training from the user terminals, and on uplink/downlink channel reciprocity. The BS employs virtual sector-based processing according to which, user-channel estimation and data transmission are performed in parallel over non-overlapping angular sectors. The uplink training schemes we consider are non-orthogonal, that is, we allow multiple users to transmit pilots on the same pilot dimension (thereby potentially interfering with one another). Elementary processing allows each sector to determine the subset of user channels that can be resolved on the sector (effectively pilot contamination free) and, thus, the subset of users that can be served by the sector. This allows resolving multiple users on the same pilot dimension at different sectors, thereby increasing the overall multiplexing gains of the system. Our analysis and simulations reveal that, by using appropriately designed directional training beams at the user terminals, the sector-based transmission schemes we present can yield substantial spatial multiplexing and ergodic user-rates improvements with respect to their orthogonal-training counterparts.

We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying à-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and “noisy network coding” are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish the inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression and decoding and the role of time sharing.

In this paper, we study the recovery of a signal from a collection of unlabeled and possibly noisy measurements via a measurement matrix with random i.i.d. Gaussian components. We call the measurements unlabeled since their order is missing, namely, it is not known a priori which elements of the resulting measurements correspond to which row of the measurement matrix. We focus on the special case of ordered measurements, where only a subset of the measurements is kept and the order of the taken measurements is preserved. We identify a duality between this problem and the traditional Compressed Sensing, where we show that the unknown support (location of the nonzero elements) of a sparse signal in Compressed Sensing corresponds in a natural way to the unknown location of the measurements kept in unlabeled sensing. While in Compressed Sensing it is possible to recover a sparse signal from an under-determined set of linear equations (less equations than the dimension of the signal), successful recovery in unlabeled sensing requires taking more samples than the dimension of the signal. We develop a low-complexity alternating minimization algorithm to recover the target signal from the set of its unlabeled samples. We also study the behavior of the proposed algorithm for different signal dimensions and number of measurements empirically via numerical simulations. The results are a reminiscent of the phasetransition similar to that occurring in Compressed Sensing.

We consider a wireless Device-to-Device (D2D) caching network, where users make arbitrary requests from a library of files and have pre-fetched (cached) information on their devices, subject to a per-node storage capacity constraint. The network is assumed to obey the “protocol model”, widely considered in the wireless network literature. Unlike other related works, which either restrict the communication to single-hop, or assume entire file caching, here we consider both multi-hop transmission and fully general caching strategies, including file subpacketization. We propose a caching strategy based on deterministic assignment of MDS-coded packets of the library files, and a coded multicast delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We show that our approach can achieve the information theoretic outer bound within a multiplicative constant factor in practical parameter regimes.

In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelation function or equivalently the power spectral density (PSD) of the process is available, the Minimum Mean Squared Error (MMSE) predictor is readily obtained. In particular, it is given by a linear operator that depends on autocorrelation of the process as well as the noise power in the observed samples. The prediction becomes, however, quite challenging when the PSD of the process is unknown. In this paper, we propose a blind predictor that does not require the a priori knowledge of the PSD of the process and compare its performance with that of an MMSE predictor that has a full knowledge of the PSD. To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process. We apply the well-known atomic-norm minimization technique to the observed samples to obtain a discrete quantization of the underlying random spectrum, which we use to predict the process. Our simulation results show that this estimator has a good performance comparable with that of the MMSE estimator.

We study the capacity scaling law of a device-to-device (D2D) caching network where n nodes are placed on a regular grid of area n. Each node caches some (coded) bits from a content library and requests a file from the library independently according to the Zipf popularity distribution. We propose a cache-induced hierarchical cooperation scheme which achieves the optimal capacity scaling law under a commonly used “physical model”. When the path loss exponent α <; 3, the capacity scaling law can be significantly better than the throughput scaling laws achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the capacity scaling law for wireless caching networks under the physical model.

In this paper we consider a single-cell downlink scenario where a multiple-antenna base station delivers contents to multiple cache-enabled user terminals. Based on the multicasting opportunities provided by the so-called Coded Caching technique, we investigate three delivery approaches. Our baseline scheme employs the coded caching technique on top of max-min fair multicasting. The second one consists of a joint design of Zero-Forcing (ZF) and coded caching, where the coded chunks are formed in the signal domain (complex field). The third scheme is similar to the second one with the difference that the coded chunks are formed in the data domain (finite field). We derive closed-form rate expressions where our results suggest that the latter two schemes surpass the first one in terms of Degrees of Freedom (DoF). However, at the intermediate SNR regime forming coded chunks in the signal domain results in power loss, and will deteriorate throughput of the second scheme. The main message of our paper is that the schemes performing well in terms of DoF may not be directly appropriate for intermediate SNR regimes, and modified schemes should be employed.

We study the topological interference management problem with decoded message passing (TIM-MP) using a polyhedral approach. The TIM-MP problem considers partially-connected interference channels with no channel state information except for the network topology (i.e., connectivity graph) at the transmitters, while the decoded messages at the receivers can be routed to other receivers via backhaul links to help cancel interference. With the aid of polyhedral combinatorics, we identify the structural properties of certain classes of network topologies for which orthogonal access achieves the optimal degrees-of-freedom (DoF) region in the information-theoretic sense. We are also able to prove the linear optimality of orthogonal access in terms of symmetric DoF for the networks up to four users with all possible network topologies (218 instances).

Millimeter wave (mm-wave) communication is essential for the next generation cellular networks. To exploit mm-wave frequencies, directional transmissions have to be applied to compensate the high propagation loss. Due to directional transmissions, initial access procedure of mm-wave communication systems needs specific design compared to conventional networks operating at sub-6 GHz. This paper focuses on an important step in the initial access procedure, namely broadcast signaling design for cell discovery. An analysis of such design is conducted based on an information theoretical approach, where four fundamental beam patterns, which cover most of the design options, are compared. Their performances in terms of cell discovery latency and signaling overhead are analyzed. The analysis reveals three key findings: (i) the average cell discovery latency depends only on beam duration and frame length, if the entire beacon interval can be accommodated in one frame; (ii) for low latency, single beam exhaustive scanning provides the best performance, but results in high signaling overhead; (iii) simultaneous multi-beam scanning can significantly reduce the overhead, and provide the flexibility to achieve trade-off between latency and overhead. The analytical results are verified by extensive simulations.

Millimeter wave (mm-wave) frequencies provide orders of magnitude larger spectrum than current cellular allocations and allow usage of high dimensional antenna arrays for exploiting beamforming and spatial multiplexing. This paper addresses the problem of joint scheduling and radio resource allocation optimization in mm-wave heterogeneous networks where mm-wave small cells are densely deployed underlying the conventional homogeneous macro cells. Furthermore, mm-wave small cells operate in time division duplexing mode and share the same spectrum and air-interface for backhaul and access links. The scheme proposed in this paper can significantly enhance network throughput by exploiting space-division multiple access, i.e., allowing non-conflicting flows to be transmitted simultaneously. The optimization problem of maximizing network throughput is formulated as a mixed integer nonlinear programming problem. To find a practical solution, this is decomposed into three steps: concurrent transmission scheduling, time resource allocation, and power allocation. A maximum independent set based algorithm is developed for concurrent transmission scheduling to improve resource utilization efficiency with low computational complexity. Through extensive simulations, we demonstrate that the proposed algorithm achieves significant gain over benchmark schemes in terms of user throughput.

We consider a wireless device-to-device network, where n nodes are uniformly distributed at random over the network area. We let each node caches M files from a library of size m ≥ M. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions, including Zipf distributions with exponent less than one. In the parameter regime of interest, i.e., m=o(nM), we show that a decentralized random caching strategy with uniform probability over the library yields the optimal per-node capacity scaling of Θ(√M/m) for heavy-tailed popularity distributions. This scaling is constant with n , thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is Θ (M/m). The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as n increases.

We consider the canonical shared link caching network formed by a source node, hosting a library of m information messages (files), connected via a noiseless multicast link to n user nodes, each equipped with a cache of size M files. Users request files independently at random according to an a-priori known demand distribution q. A coding scheme for this network consists of two phases: cache placement and delivery. The cache placement is a mapping of the library files onto the user caches that can be optimized as a function of the demand statistics, but is agnostic of the actual demand realization. After the user demands are revealed, during the delivery phase the source sends a codeword (function of the library files, cache placement, and demands) to the users, such that each user retrieves its requested file with arbitrarily high probability. The goal is to minimize the average transmission length of the delivery phase, referred to as rate (expressed in channel symbols per file). In the case of deterministic demands, the optimal min-max rate has been characterized within a constant multiplicative factor, independent of the network parameters. The case of random demands was previously addressed by applying the order-optimal min-max scheme separately within groups of files requested with similar probability. However, no complete characterization of order-optimality was previously provided for random demands under the average rate performance criterion. In this paper, we consider the random demand setting and, for the special yet relevant case of a Zipf demand distribution, we provide a comprehensive characterization of the order-optimal rate for all regimes of the system parameters, as well as an explicit placement and delivery scheme achieving order-optimal rates. We present also numerical results that confirm the superiority of our scheme with respect to previously proposed schemes for the same setting.

Correlation across transmit antennas in multiple-input multiple-output (MIMO) systems has been studied in various scenarios and has been shown to be detrimental or provide benefits depending on the particular system and underlying assumptions. In this paper, we investigate the effect of transmit correlation on the capacity of the Gaussian MIMO broadcast channel, with a particular interest in the large-scale array (or massive MIMO) regime. To this end, we introduce a new type of diversity, referred to as transmit correlation diversity, which captures the fact that the channel vectors of different users may have different channel covariance matrices spanning often nearly mutually orthogonal subspaces. In particular, when taking the cost of downlink training properly into account, transmit correlation diversity can yield significant capacity gains in all regimes of interest. Our analysis shows that the system multiplexing gain can be increased by a factor up to ⌊M/r⌋, where M is the number of antennas and r ≤ M is the common rank of the users channel covariance matrices, with respect to standard schemes that are agnostic of the transmit correlation diversity and treat the channels as if they were isotropically distributed. Thus, this new form of diversity reveals itself as a valuable “new resource” in multiuser communications.

We consider a massive MIMO system based on time division duplexing (TDD) and channel reciprocity, where the base stations (BSs) learn the channel vectors of their users via the pilots transmitted by the users in the uplink (UL). It is well-known that, in the limit of very large number of BS antennas, the system performance is limited by pilot contamination, due to the fact that the same set of orthogonal pilots is reused in multiple cells. In the regime of moderately large number of antennas, another source of degradation is channel interpolation because the pilot signal of each user probes only a limited number of orthogonal frequency division multiplexing (OFDM) subcarriers, and the channel must be interpolated over the other subcarriers, where no pilot symbol is transmitted. In this paper, we propose a low-complexity algorithm that uses the received UL wideband pilot snapshots in an observation window comprising several coherence blocks (CBs) to obtain an estimate of the angle-delay power spread function (PSF) of the received signal. This is generally given by the sum of the angle-delay PSF of the desired user and the angle-delay PSFs of the copilot users, i.e., the users re-using the same pilot dimensions in other cells/sectors. We propose supervised and unsupervised clustering algorithms to decompose the estimated PSF and isolate the part corresponding to the desired user only. We use this decomposition to obtain an estimate of the covariance matrix of the user wideband channel vector, which we exploit to decontaminate the desired user channel estimate by applying minimum mean squared error (MMSE) smoothing filter, i.e., the optimal channel interpolator in the MMSE sense. We also propose an effective low-complexity approximation/implementation of this smoothing filter. We use numerical simulations to assess the performance of our proposed method, and compare it with other recently proposed schemes that use the same idea of separability of users in the angle-delay domain.

Line-of-sight wireless communications can benefit from the simultaneous transmission of multiple independent data streams through the same medium in order to increase system capacity. A common approach is to use conventional spatial multiplexing with spatially separated transmitter/receiver antennae, for which inter-channel crosstalk is reduced by employing multiple-input-multiple-output (MIMO) signal processing at the receivers. Another fairly recent approach to transmitting multiple data streams is to use orbital-angular-momentum (OAM) multiplexing, which employs the orthogonality among OAM beams to minimize inter-channel crosstalk and enable efficient (de)multiplexing. In this paper, we explore the potential of utilizing both of these multiplexing techniques to provide system design flexibility and performance enhancement. We demonstrate a 16 Gbit/s millimeter-wave link using OAM multiplexing combined with conventional spatial multiplexing over a short link distance of 1.8 meters (shorter than Rayleigh distance). Specifically, we implement a spatial multiplexing system with a 2 × 2 antenna aperture architecture, in which each transmitter aperture contains two multiplexed 4 Gbit/s data-carrying OAM beams. A MIMO-based signal processing is used at the receiver to mitigate channel interference. Our experimental results show performance improvements for all channels after MIMO processing, with bit-error rates of each channel below the forward error correction limit of 3.8 × 10-3. We also simulate the capacity for both the 4 × 4 MIMO system and the 2 × 2 MIMO with OAM multiplexing. Our work indicates that OAM multiplexing and conventional spatial multiplexing can be simultaneously utilized to provide design flexibility. The combination of these two approaches can potentially enhance system capacity given a fixed aperture area of the transmitter/receiver (when the link distance is within a few Rayleigh distances).

Recently, a new class of decentralized random coded caching schemes have received increasing interest, as they can achieve order-optimal memory-load tradeoff through decentralized content placement when the file size goes to infinity. However, most of these existing decentralized schemes may not provide enough coded- multicasting opportunities in the practical operating regime where the file size is limited. In this paper, we focus on the finite file size regime and propose a decentralized random coded caching scheme and a partially decentralized sequential coded caching scheme. These two schemes have different requirements on coordination in the content placement phase and can be applied to different scenarios. The content placement of the proposed schemes aims at ensuring abundant coded-multicasting opportunities in the content delivery phase when the file size is finite. We analyze the worst-case (over all possible requests) loads of our schemes and show that the sequential coded caching scheme outperforms the random coded caching scheme in the finite file size regime. Analytical results indicate that, when the file size grows to infinity, the proposed schemes achieve the same memory- load tradeoff as Maddah-Ali-Niesen's decentralized scheme, and hence are also order optimal. Numerical results show that the two proposed schemes outperform Maddah-Ali-Niesen's decentralized scheme when the file size is not very large.

Dense large-scale antenna deployments are one of the most promising technologies for delivering very large throughputs per unit area in the downlink (DL) of cellular networks. We consider such a dense deployment involving a distributed system formed by multi-antenna remote radio head (RRH) units connected to the same fronthaul serving a geographical area. Knowledge of the DL channel between each active user and its nearby RRH antennas is most efficiently obtained at the RRHs via reciprocity based training, that is, by estimating a user's channel using uplink (UL) pilots transmitted by the user, and exploiting the UL/DL channel reciprocity. We consider aggressive pilot reuse across an RRH system, whereby a single pilot dimension is simultaneously assigned to multiple active users. We introduce a novel coded pilot approach, which allows each RRH unit to detect pilot collisions, i.e., when more than a single user in its proximity uses the same pilot dimensions. Thanks to the proposed coded pilot approach, pilot contamination can be substantially avoided. As shown, such a strategy can yield densification benefits in the form of increased multiplexing gains per UL pilot dimension with respect to conventional reuse schemes and some recent approaches assigning pseudorandom pilot vectors to the active users.

We consider the problem of clustering N users into K groups such that users in the same group are assigned a common service profile over M commodities. The profile of each group k sets for each commodity m the maximum of the service quality that users in the k-th group are willing to pay. The objective is to find the clustering that maximizes the total service user quality, which corresponds to the revenue of the service provider. This Service Profile Optimization Problem (SPOP) emerges in various applications, as for example the bit-loading in Hybrid Fiber Coax data distribution systems. We propose a Mixed Integer Linear Programming (MILP) model for the problem, that allows to use state-of-the-art MILP solvers as the core tool in an original powerful heuristic. We show complexity and performance gains with respect to previously proposed methods and a direct application of a state of the art MILP solver.

In this paper, we study the capacity and degree-of-freedom (DoF) scaling for the continuous-time amplitude limited AWGN channels in radio frequency (RF) and intensity modulated optical communication (OC) channels. More precisely, we study how the capacity varies in terms of the OFDM block transmission time T, bandwidth W, amplitude A and the noise spectral density N0/2. We first find suitable discrete encoding spaces for both cases, and prove that they are convex sets that have a semi-definite programming (SDP) representation. Using tools from convex geometry, we find lower and upper bounds on the volume of these encoding sets, which we exploit to drive pretty sharp lower and upper bounds on the capacity. We also study a practical Tone-Reservation (TR) encoding algorithm and prove that its performance can be characterized by the statistical width of an appropriate convex set. Recently, it has been observed that in high-dimensional estimation problems under constraints such as those arisen in Compressed Sensing (CS) statistical width plays a crucial role. We discuss some of the implications of the resulting statistical width on the performance of the TR. We also provide numerical simulations to validate these observations.

3GPP/LTE provisions for a Multimedia Broadcast Multicast Service (MBMS), where a common data stream is sent to many users (a multicast group) simultaneously from multiple base stations, transmitting on the same frequency channel, i.e., forming a Single-Frequency Network (SFN). This setting has been extensively treated as a max-min fair beamforming problem, where the beamforming vector is optimized as a function of the instantaneous channel state information in order to maximize the instantaneous (per-slot) common rate over all users. Unfortunately, such common rate vanishes as the number of users grows for fixed number of base station antennas. In this paper we consider the ergodic regime, where coding across multiple slots affected by independent fading is allowed. We formulate the problem as an ergodic compound channel, subject to a per-slot and per-group of antennas power constraint, and we provide an efficient algorithm that approximates the compound capacity to any desired degree of accuracy. Then, in line with the current implementation of MBMS-FSN in 3GPP, we consider also the multicast throughput achievable by a concatenated coding scheme, where inner physical layer coding is applied on a per-slot basis, and outer packet erasure coding is used at the application layer. The optimal strategy in this case is NP-Hard, and we propose a convex relaxation approach with good performance and low complexity.

Motivated by the ability of modern terminals to receive simultaneously from multiple networks (e.g., WLAN and Cellular), we extend the single shared link network with caching at the user nodes to the case of r parallel partially shared links, where users in different classes receive from the server simultaneously and in parallel through different set of links. For this setting, we give an order-optimal rate and (maximal) delay region characterization for the case of r = 2 links with two classes of users, one receiving only from link 1 and the other from both links 1 and 2. We also extend these results to r = 3 with three classes of users, receiving from link 1, from links 1 and 2, and from links 1 and 3, respectively.

We consider the problem of revealing/sharing data in an efficient and secure way via a compact representation. The representation should ensure reliable reconstruction of the desired features/attributes while still preserve privacy of the secret parts of the data. The problem is formulated as a remote lossy source coding with a privacy constraint where the remote source consists of public and secret parts. Inner and outer bounds for the optimal tradeoff region of compression rate, distortion, and privacy leakage rate are given and shown to coincide for some special cases. When specializing the distortion measure to a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for the case of identical side information forms an optimization problem which corresponds to the “secure” version of the so-called information bottleneck.

Topological interference management (TIM) problem studies partially connected interference networks with no channel state information except for the connectivity graph at transmitters. In this paper, we consider a similar problem in the uplink cellular networks while message passing is enabled at receivers (e.g., base stations) in which the decoded messages can be routed to other receivers via backhaul links to help improve overall network performance. For this new problem setting, we try to answer the following two questions: (1) when is orthogonal access optimal? and (2) when does message passing help? From both graph theoretic and index coding perspectives, we are able to offer preliminary answers to those questions by identifying sufficient and/or necessary conditions.

Cache-aided network architectures are emerging as an innovative solution able to harness device memory, a cheap and widely available resource, into bandwidth, so as to meet the predicted dramatic increase of user data traffic generated by on-demand multi-media. In this paper, starting from the previously proposed and widely studied femtocaching network, we consider a partially connected interference network where the femto base stations are equipped with caches and have no access to channel state information beyond the network connectivity (network topology). We aim at characterizing the tradeoff between the cache memory size and the normalized transmission delay for file delivery. We formulate a joint file placement and delivery optimization problem, and propose approaches to compute extreme points of the achievable memory-delay region. Our algorithmic solution consists of decomposing the intractable joint optimization problem into separate subproblems, which are solvable using existing efficient methods.

We consider problems of two-user secret key generation through an intermediate relay. In the untrusted relay setting, the goal is to establish key agreement between the two users at the highest key rate without leaking information about the key to the relay. We characterize inner and outer bounds to the optimal tradeoff between communication and key rates. The inner bound is based on the scheme which involves a combination of binning, network coding, and key aggregation techniques. For the trusted relay setting with a public broadcast link, the optimal communication-key rate tradeoff is provided for a special case where the two sources are available losslessly at the relay.

The enormous success of advanced wireless devices is pushing the demand for higher wireless data rates. The industry is satisfying this increasing demand by densely deploying large numbers of access points (APs). Unfortunately, unicast rates, especially in crowded scenarios, remain very low due to severe interference and time-sharing. However, one may take advantage of the broadcasting nature of wireless transmissions to offer high multicast rates. Motivated by this, we present coordinated broadcasting (Co-BCast), a system which coordinates multiple APs to provide participants of big events with high multicast rates that can support multiple high definition video streams.

Reliable connectivity between vehicles is a requirement for efficient overtaking warning systems. We investigate the 6 GHz propagation channel between oncoming vehicles with five different types of large obstructing vehicles in a poor scattering environment. The presented channel gains emphasize the advantages of different antenna positions and the difference between a straight road and curved road. We conclude that connectivity ranges on a straight road are close to the minimum distance required by a warning system. We furthermore conclude that the channel gain of antennas mounted inside the vehicle can be higher compared to antennas mounted on the roof, which can increase the connectivity range significantly.

As wireless video is the fastest growing form of data traffic, methods for spectrally efficient on-demand wireless video streaming are essential to both service providers and users. A key property of video on-demand is the asynchronous content reuse, such that a few popular files account for a large part of the traffic but are viewed by users at different times. Caching of content on wireless devices in conjunction with device-to-device (D2D) communications allows to exploit this property, and provide a network throughput that is significantly in excess of both the conventional approach of unicasting from cellular base stations and the traditional D2D networks for “regular” data traffic. This paper presents in a tutorial and concise form some recent results on the throughput scaling laws of wireless networks with caching and asynchronous content reuse, contrasting the D2D approach with other alternative approaches such as conventional unicasting, harmonic broadcasting, and a novel coded multicasting approach based on caching in the user devices and network-coded transmission from the cellular base station only. Somehow surprisingly, the D2D scheme with spatial reuse and simple decentralized random caching achieves the same near-optimal throughput scaling law as coded multicasting. Both schemes achieve an unbounded throughput gain (in terms of scaling law) with respect to conventional unicasting and harmonic broadcasting, in the relevant regime where the number of video files in the library is smaller than the total size of the distributed cache capacity in the network. To better understand the relative merits of these competing approaches, we consider a holistic D2D system design incorporating traditional microwave (2 GHz) and millimeter-wave (mm-wave) D2D links; the direct connections to the base station can be used to provide those rare video requests that cannot be found in local caches. We provide extensive simulation results under a variety of system settings and compare our scheme with the systems that exploit transmission from the base station only. We show that, also in realistic conditions and nonasymptotic regimes, the proposed D2D approach offers very significant throughput gains.

We consider a wireless device-to-device (D2D) network where communication is restricted to be single-hop. Users make arbitrary requests from a finite library of files and have pre-cached information on their devices, subject to a per-node storage capacity constraint. A similar problem has already been considered in an infrastructure setting, where all users receive a common multicast (coded) message from a single omniscient server (e.g., a base station having all the files in the library) through a shared bottleneck link. In this paper, we consider a D2D infrastructureless version of the problem. We propose a caching strategy based on deterministic assignment of subpackets of the library files, and a coded delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We also consider a random caching strategy, which is more suitable to a fully decentralized implementation. Under certain conditions, both approaches can achieve the information theoretic outer bound within a constant multiplicative factor. In our previous work, we showed that a caching D2D wireless network with one-hop communication, random caching, and uncoded delivery (direct file transmissions) achieves the same throughput scaling law of the infrastructure-based coded multicasting scheme, in the regime of large number of users and files in the library. This shows that the spatial reuse gain of the D2D network is order-equivalent to the coded multicasting gain of single base station transmission. It is, therefore, natural to ask whether these two gains are cumulative, i.e., if a D2D network with both local communication (spatial reuse) and coded multicasting can provide an improved scaling law. Somewhat counterintuitively, we show that these gains do not cumulate (in terms of throughput scaling law). This fact can be explained by noticing that the coded delivery scheme creates messages that are useful to multiple nodes, such that it benefits from broadcasting to as many nodes as possible, while spatial reuse capitalizes on the fact that the communication is local, such that the same time slot can be reused in space across the network. Unfortunately, these two issues are in contrast with each other.

We consider the problem of identification and authentication based on secret key generation from some usergenerated source data (e.g., a biometric source). The goal is to reliably identify users pre-enrolled in a database as well as authenticate them based on the estimated secret key while preserving the privacy of the enrolled data and of the generated keys. We characterize the optimal tradeoff region of the identification rate, compression rate of the users' source data, information leakage rate, and secret key rate. In particular, we provide a coding strategy based on layered random binning which is shown to be optimal. In addition, we study a related secure identification/authentication problem where an adversary tries to deceive the system using its own data. Here, the optimal tradeoff of the identification rate, compression rate, leakage rate, and exponent of the maximum false acceptance probability is provided. The results reveal a close connection between the optimal secret key rate and the false acceptance exponent of the identification/authentication system.

The compute-and-forward framework permits each receiver in a Gaussian network to directly decode a linear combination of the transmitted messages. The resulting linear combinations can then be employed as an end-to-end communication strategy for relaying, interference alignment, and other applications. Recent efforts have demonstrated the advantages of employing unequal powers at the transmitters and decoding more than one linear combination at each receiver. However, neither of these techniques fit naturally within the original formulation of compute-and-forward. This paper proposes an expanded compute-and-forward framework that incorporates both of these possibilities and permits an intuitive interpretation in terms of signal levels. Within this framework, recent achievability and optimality results are unified and generalized.

In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated.

For single-antenna Gaussian interference channels, we reformulate the problem of determining the generalized degrees of freedom (GDoF) region achievable by treating interference as Gaussian noise (TIN) derived by Geng et al. from a combinatorial optimization perspective. We show that the TIN power control problem can be cast into an assignment problem, such that the globally optimal power allocation variables can be obtained by well-known polynomial time algorithms (e.g., centralized Hungarian method or distributed Auction algorithm). Furthermore, the expression of the TIN-achievable GDoF region (TINA region) can be substantially simplified with the aid of maximum weighted matchings. We also provide conditions under which the TINA region is a convex polytope that relax those by Geng et al. For these new conditions, together with a channel connectivity (i.e., interference topology) condition, we show TIN optimality for a new class of interference networks that is not included, nor includes, the class found by Geng et al. Building on the above insights, we consider the problem of joint link scheduling and power control in wireless networks, which has been widely studied as a basic physical layer mechanism for device-to-device communications. Inspired by the relaxed TIN channel strength condition as well as the assignment-based power allocation, we propose a low-complexity GDoF-based distributed link scheduling and power control mechanism (ITLinQ+) that improves upon the ITLinQ scheme proposed by Naderializadeh and Avestimehr and further improves over the heuristic approach known as FlashLinQ. It is demonstrated by simulation that ITLinQ+ without power control provides significant average network throughput gains over both ITLinQ and FlashLinQ, and yet still maintains the same level of implementation complexity. Furthermore, when ITLinQ+ is augmented by power control, it provides an energy efficiency substantially larger than that of ITLinQ and FlashLinQ, at the cost of additional complexity and some signaling overhead.

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current Dynamic Adaptive Streaming over HTTP (DASH) technology, for which users sequentially request video “chunks” by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users' request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer.

Massive MIMO is one of the most promising approaches for coping with the predicted wireless data traffic explosion. Future deployment scenarios will involve dense heterogeneous networks, comprised of massive MIMO base stations with different powers, numbers of antennas and multiplexing gain capabilities, and possibly highly nonhomogeneous user density (hot-spots). In such dense irregularly deployed networks, it will be important to have mechanisms for associating users to base stations so that the available wireless infrastructure is efficiently used. In this paper, we consider the optimal user-cell association problem for massive MIMO heterogeneous networks and illustrate how massive MIMO can also provide nontrivial advantages at the system level. Unlike previous treatments that rely on integer program problem formulations and their convex relaxations, the user-cell association problem is formulated directly as a convex network utility maximization and solved efficiently by a centralized subgradient algorithm. As we show, the globally optimal solution is physically realizable, in that there exists a sequence of integer-valued associations approaching arbitrarily closely the optimal fractional association. We also consider simple decentralized user-centric association schemes, where each user individually and selfishly connects to the base station with the highest promised throughput. Such user-centric schemes where users make local association decisions in a probabilistic manner can be viewed as games and are known to converge to Nash equilibria. Surprisingly, as we show, under certain conditions, the globally optimal solution is close to these Nash equilibria. Such decentralized approaches are, therefore, attractive not only for their simplicity, but also because they operate near the system social optimum. Our theoretical results are confirmed by extensive simulations with realistic LTE-like network parameters.

We consider the problem of simultaneous on-demand streaming of stored video to multiple users in a multicell wireless network where multiple unicast streaming sessions are run in parallel and share the same frequency band. Each streaming session is formed by the sequential transmission of video “chunks,” such that each chunk arrives into the corresponding user playback buffer within its playback deadline. We formulate the problem as a network utility maximization (NUM) where the objective is to fairly maximize users' video streaming quality of experience (QoE) and then derive an iterative control policy using Lyapunov optimization, which solves the NUM problem up to any level of accuracy and yields an online protocol with control actions at every iteration decomposing into two layers interconnected by the users' request queues : 1) a video streaming adaptation layer reminiscent of dynamic adaptive streaming over HTTP (DASH), implemented at each user node; and 2) a transmission scheduling layer where a max-weight scheduler is implemented at each base station. The proposed chunk request scheme is a pull strategy where every user opportunistically requests video chunks from the neighboring base stations and dynamically adapts the quality of its requests based on the current size of the request queue. For the transmission scheduling component, we first describe the general max-weight scheduler and then particularize it to a wireless network where the base stations have multiuser multiple-input multiple-output (MU-MIMO) beamforming capabilities. We exploit the channel hardening effect of large-dimensional MIMO channels (massive MIMO) and devise a low complexity user selection scheme to solve the underlying combinatorial problem of selecting user subsets for downlink beamforming, which can be easily implemented and run independently at each base station. Furthermore, through simulations, we show that deploying MU-MIMO significantly improves video streaming performance and also that the proposed cross-layer approach is able to serve users more fairly than a baseline scheme representative of current systems running independently designed protocol layers.

In a multiuser MIMO broadcast channel, the rate performance is affected by multiuser interference when the channel state information at the transmitter (CSIT) is imperfect. To tackle the detrimental effects of the multiuser interference, a rate-splitting (RS) approach has been proposed recently, which splits one selected user's message into a common and a private part, and superimposes the common message on top of the private messages. The common message is drawn from a public codebook and decoded by all users. In this paper, we generalize the idea of RS into the large-scale array regime with imperfect CSIT. By further exploiting the channel second-order statistics, we propose a novel and general framework hierarchical-rate-splitting (HRS) that is particularly suited to massive MIMO systems. HRS simultaneously transmits private messages intended to each user and two kinds of common messages that are decoded by all users and by a subset of users, respectively. We analyze the asymptotic sum rate of RS and HRS and optimize the precoders of the common messages. A closed-form power allocation is derived which provides insights into the effects of various system parameters. Finally, numerical results validate the significant sum rate gain of RS and HRS over various baselines.

With growing base-station density and decreasing frequency reuse factor, intercell interference and low cell-edge user rates are becoming serious problems. Legacy solutions like fractional frequency reuse are simple to implement but are suboptimal. In this paper, we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system. In interlaced clustering, several different coverage patterns coexist on disjoint parts of the spectrum. We demonstrate how various previously suggested network architectures can be interpreted as special cases of interlaced clustering. We then characterize the downlink user throughputs at the proportional fairness operating point of the rate region for both of the cases that the transmitter does, or does not, have channel state information. Based on this derivation, we develop a novel algorithm to solve the resource allocation problem for systems with interlaced clustering. Simulations based on practical cell parameters show that interlaced clustering can provide, on an average, a 100% gain on edge user rate without appreciable loss in rates elsewhere. We also verify that this result is robust to irregular deployment of the remote antenna units.

We introduce a new neighbor discovery method for wireless nodes with adaptive antennas, called "directional ZigZag." Adaptive antennas are capable of electronically changing their gain pattern and in particular form beams and steer them in arbitrary directions. Despite improving range, this makes neighbor discovery more difficult. We consider three cases; namely, nodes transmit with beamsteering and receive in omni-directional mode (DTOR), transmit with omni-directional antenna and receive with beam- steering (OTDR), and use beamsteering (directional antennas) for both transmitting and receiving (DTDR). We show that directional ZigZag detects the neighbors with vanishing probability of error. Furthermore, it is the first algorithm that requires a discovery period that scales on linearly with the average number of neighbors. This is proven through establishing a connection between ZigZag and message passing decoding.

We consider a wireless device-to-device (D2D) network in which the nodes are uniformly distributed at random over the network area and can cache information from a library of possible messages (files). Each node requests a file in the library independently at random, according to a given popularity distribution, and downloads from other nodes having the requested file in their local cache via multihop transmission. Under the classical “protocol model” of wireless ad hoc networks, we characterize the optimal throughput scaling law by presenting a feasible scheme formed by a decentralized caching policy for the parameter regimes of interest and a local multihop transmission protocol. The scaling law optimality of the proposed strategy is shown by deriving a new throughput upper bound. Surprisingly, we show that decentralized uniform random caching yields optimal scaling in most of the system interesting regimes. We also observe that caching improves the throughput scaling law of classical ad hoc networks, and that multihop improves the previously derived scaling law of caching wireless networks under one-hop transmission.

Coded multicasting has been shown to improve the caching performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into a number of packets that grows exponentially with the number of users [1]. In this paper, we first extend the analysis of the order-optimal multiple-groupcast coded multicasting scheme in [2] to the case of heterogeneous cache sizes and demand distributions, providing an achievable scheme and an upper bound on the optimal performance when the number of packets goes to infinity. We then show that the scheme achieving this upper bound can very quickly loose its promising multiplicative caching gain for finite content packetization. To overcome this limitation, we design a novel polynomial-time algorithm based on greedy local graph-coloring that, while keeping the same content packetization, recovers a significant part of the multiplicative caching gain. Our results show that the achievable schemes proposed to date to quantify the fundamental limiting performance, must be properly designed for practical regimes of finite content packetization.

Low signal strength and high interference lead to significantly reduced data-rates at the cell edge. With rising demand for spectrum and systems moving closer to universal frequency reuse, the problem has become more pronounced. Techniques like Fractional Frequency Reuse boost the edgeuser performance at the cost of the spectral efficiency and are therefore sub-optimal. In this paper we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system, and explore a multicell Distributed Antenna System as a particular example. In interlaced clustering, different parts of the spectrum use coverage patterns that are spatially shifted (by less than a cell size) replicas of each other. An information theoretic analysis is presented to characterize the proportional fairness boundary point of the achievable rate region. In the process, the joint resource allocation problem is formulated and shown to be convex. As opposed to using interior point methods which are relatively slower, the current paper proposes a novel gradient-search algorithm to solve the resource allocation problem. It is demonstrated that fractional frequency reuse can in fact be represented as a special (albeit sub-optimal) case of interlaced clustering. Simulation results show that interlaced clustering can boost the edge-user rates by a factor of 2 with negligible degradation of rates in the cell interior. Results also show that interlaced clustering outperforms the edge-user rates achieved with fractional frequency reuse by a factor of 1.5. The theoretical results are validated by comparing performance to a practical proportional fairness scheduler.

In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multipleinput multiple-output (MIMO) systems in the presence of an active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform towards the eavesdropper, thus increasing its received signal power. We derive an asymptotic achievable secrecy rate for matched filter precoding and artificial noise (AN) generation at the transmitter when the number of transmit antennas goes to infinity. For the achievability scheme at hand, we obtain the optimal power allocation policy for the transmit signal and the AN in closed form. For the case of correlated fading channels, we show that the impact of the active eavesdropper can be completely removed if the transmit correlation matrices of the users and the eavesdropper are orthogonal. Inspired by this result, we propose a precoder null space design exploiting the low rank property of the transmit correlation matrices of massive MIMO channels, which can significantly degrade the eavesdropping capabilities of the active eavesdropper.

We study a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such channel, quantize-map-and-forward (QMF) (or noisy network coding (NNC)) achieves the cut-set upper bound within a constant gap where the gap grows linearly with the number of relay stages K. However, this gap may not be negligible for the systems with multihop transmissions (i.e., a wireless backhaul operating at higher frequencies). We have recently attained an improved result to the capacity scaling where the gap grows logarithmically as logK, by using an optimal quantization at relays and by exploiting relays' messages (decoded in the previous time slot) as side-information. In this paper, we further improve the performance of this network by presenting a mixed scheme where each relay can perform either decode-and-forward (DF) or QMF with possibly rate-splitting. We derive an achievable rate and show that the proposed scheme outperforms the optimized QMF. Furthermore, we demonstrate that this performance improvement increases with K.

We consider problems of authentication using secret key generation under a privacy constraint on the enrolled source data. An adversary who has access to the stored description and correlated side information tries to deceive the authentication as well as learn about the source. We characterize the optimal tradeoff between the compression rate of the stored description, the leakage rate of the source data, and the exponent of the adversary's maximum false acceptance probability. The related problem of secret key generation with a privacy constraint is also studied where the optimal tradeoff between the compression rate, leakage rate, and secret key rate is characterized. It reveals a connection between the optimal secret key rate and security of the authentication system.

In this work, we consider an interference channel model in which K receivers cooperatively attempt to decode their intended messages locally by processing and sharing information through limited capacity backhaul links. In contrast to distributed antenna architectures that have been proposed in the literature, where data processing is utterly performed in a centralized fashion, the model considered in this paper aims to capture the essence of decentralized (over the cloud) processing, allowing for a more general class of interference management strategies. Focusing on the three-user case, we characterize the fundamental tradeoff between the achievable communication rates and the corresponding backhaul cooperation rate, in terms of degrees of freedom (DoF). Surprisingly, we show that the optimum communication-cooperation tradeoff remains the same when we move from two-user to three-user interference channels. In the absence of cooperation, this is due to interference alignment, which keeps the fraction of communication dimensions wasted for interference unchanged. When backhaul cooperation is available, we develop a new idea that we call cooperation alignment, which guarantees that the average (per user) backhaul load remains the same as we increase the number of users.

For single-antenna Gaussian interference channels, we re-formulate the problem of determining the Generalized Degrees of Freedom (GDoF) region achievable by treating interference as noise (TIN) with proper power control from a combinatorial optimization perspective. We show that the TIN power control problem can be cast into an assignment problem, such that the globally optimal power allocation variables can be obtained by well-known polynomial time algorithms. Furthermore, the expression of the TIN-achievable GDoF region can be substantially simplified with the aid of maximum weighted matchings. In addition, we provide conditions under which the TIN-achievable GDoF region is a convex polytope that relax those in [1]. For these new conditions, together with a channel connectivity (i.e., interference topology) condition, we can prove GDoF optimality for a new class of interference networks that is not included, nor includes, the class found in [1].

We introduce a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such network, quantize-map-and-forward (QMF) (or noisy network coding (NNC)) can achieve the cut-set upper bound within a constant gap where the gap grows linearly with the number of relay stages K. However, this gap may not be negligible for the systems with multihop transmissions (e.g., a power-limited wireless backhaul system operating at high frequencies). In this paper, we obtain an improved result to the capacity scaling where the gap grows logarithmically as log (K). This is achieved by using an optimal quantization at relays and by exploiting relays' messages (decoded in the previous time slot) as side-information at the destination. We further improve the performance of this network by presenting a mixed strategy where each relay can perform either decode-and-forward (DF) or QMF with possibly rate-splitting.

We consider a wireless device-to-device (D2D) network where n nodes are uniformly distributed at random over the network area. We let each node with storage capacity M cache files from a library of size m. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions including the Zipf distribution with Zipf exponent less than one. Surprisingly, in the parameter regimes of interest, we show that decentralized random caching uniformly across the library yields optimal per-node capacity scaling of Θ(√M/m), which outperforms the single-hop caching networks whose capacity scales as Θ (M/m) [1], [2].

The capacity of caching networks has received considerable attention in the past few years. A particularly studied setting is the shared link caching network, in which a single source with access to a file library communicates with multiple users, each having the capability to store segments (packets) of the library files, over a shared multicast link. Each user requests one file from the library according to a common demand distribution and the server sends a coded multicast message to satisfy all users at once. The problem consists of finding the smallest possible average codeword length to satisfy such requests. In this paper, we consider the generalization to the case where each user places L ≥ 1 independent requests according to the same common demand distribution. We propose an achievable scheme based on random vector (packetized) caching placement and multiple groupcast index coding, shown to be order-optimal in the asymptotic regime in which the number of packets per file B goes to infinity. We then show that the scalar (B = 1) version of the proposed scheme can still preserve order-optimality when the number of per-user requests L is large enough. Our results provide the first order-optimal characterization of the shared link caching network with multiple random requests, revealing the key effects of L on the performance of caching-aided coded multicast schemes.

Urban intersections constitute an important safety-critical scenario for vehicle-to-vehicle communication. Based on measurements, this paper presents detailed investigations of the radio wave propagation processes in a typical urban intersection. We focus on one time instance of the propagation process and set up hypothesis for locations of the scattering objects. Multipath components (MPCs) are identified and an MPC tracking algorithm is applied. The number of MPCs and the lifetime of MPCs for this communication scenario are analyzed. It is concluded that the lifetime of an MPC is heavily dependent on its relative power, with typical values in the range 0.05-0.3 s corresponding to 0.5-3 m travel distance of the vehicles.

In this paper, we study the downlink performance of a heterogeneous cellular network (HetNet) where both macro and small cells share the same spectrum and hence interfere with each other. We assume that the users are concentrated at certain areas in the cell, i.e., they form hotspots. While some of the hotspots are assumed to have a small cell in their vicinity, the others are directly served by the macrocell. Due to a relatively small area of each hotspot, the users lying in a particular hotspot appear to be almost co-located to the macrocells, which are typically deployed at some elevation. We assume a large number of antennas at the macrocell relative to the number of users simultaneously served. In this “massive MIMO” regime, the channel vectors become highly directional. We exploit this directionality in the channel vectors to obtain spatial blanking, i.e., concentrating transmission energy only in certain directions while creating transmission opportunities for the small cells lying in the other directions. In addition to this inherent interference suppression, we also develop three low-complexity interference coordination strategies: turn off small cells based on the amount of cross-tier interference they receive or cause to the scheduled macrocell hotspots; schedule hotspots such that treating interference as noise is approximately optimal for the resulting Gaussian interference channel; and offload some of the macrocell hotspots to nearby small cells to improve throughput fairness across all hotspots. For all these schemes, we study the relative merits and demerits of uniform deployment of small cells vs. deploying more small cells towards the cell center or the cell edge.

We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a “divide and conquer” approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the “smooth streaming regime,” i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times.

We study a number of two-user interference networks with multiple-antenna transmitters/receivers (MIMO), transmitter side information in the form of linear combinations (over an appropriate finite-field) of the information messages, and two-hop relaying. We start with a cognitive interference channel (CIC) where one of the transmitters (noncognitive) has knowledge of a rank-1 linear combination of the two information messages, while the other transmitter (cognitive) has access to a rank-2 linear combination of the same messages. This is referred to as the network-coded CIC, since such linear combination may be the consequence of some random linear network coding scheme implemented in the backbone wired network. For such channel we develop an achievable region based on a few novel concepts: precoded compute-and-forward (PCoF) with channel integer alignment (CIA), combined with standard dirty-paper coding. We also develop a capacity region outer bound and find the symmetric generalized degrees of freedom (GDoF) region of the network-coded CIC. Through the GDoF characterization, we show that knowing mixed data (linear combinations of the information messages) provides a unbounded spectral efficiency gain over the classical CIC counterpart, if the ratio (in decibel) of signal-to-noise (SNR) to interference-to-noise is larger than certain threshold. Then, we consider a Gaussian relay network having the two-user MIMO IC as the main building block. We use PCoF with CIA to convert the MIMO IC into a deterministic finite-field IC. Then, we use a linear precoding scheme over the finite-field to eliminate interference in the finite-field domain. Using this unified approach, we derive the symmetric sum rate of the two-user MIMO IC with coordination, cognition, and two-hops. We also provide finite-SNR results (not just DoF) which show that the proposed coding schemes are competitive against state-of-the-art interference avoidance scheme based on orthogonal access, for standard randomly generated Rayleigh fading channels.

We consider virtual full-duplex relaying by means of half-duplex relays. In this configuration, each relay stage in a multihop relaying network is formed by at least two relays, used alternatively in transmit and receive modes, such that while one relay transmits its signal to the next stage, the other relay receives a signal from the previous stage. With such a pipelined scheme, the source is active and sends a new information message in each time slot. We consider the achievable rates for various coding schemes and compare them with a cut-set upper bound, which is tight in certain conditions. We show that both lattice-based compute-and-forward (CoF) and quantize-map-and-forward (QMF) yield attractive performance and implementation. In particular, QMF in this context does not require long messages and joint (non-unique) decoding, if the quantization mean-square distortion at the relays is chosen appropriately. In addition, in the multihop case the gap of QMF from the cut-set upper bound grows logarithmically with the number of stages, and not linearly as in the case of noise level quantization. Furthermore, we show that CoF is particularly attractive in the case of multihop relaying, when the channel gains have fluctuations not larger than 3 dB, yielding a rate that does not depend on the number of relaying stages. In particular, we argue that such architecture may be useful for a wireless backhaul with line-of-sight propagation between the relays.

We consider a device-to-device wireless network where n users are densely deployed in a squared planar region and communicate with each other without the help of a wired infrastructure. For this network, we examine the three-phase hierarchical cooperation scheme originally proposed by Ozgur, Leveque, and Tse, and the two-phase improved hierarchical cooperation scheme successively proposed by Ozgur and Leveque based on the concept of network multiple access. Exploiting recent results on the optimality of treating interference as noise in Gaussian interference channels, we optimize the achievable average per-link rate and not just its scaling law (as a function of n). In addition, we provide further improvements on both the previously proposed hierarchical cooperation schemes by a more efficient use of time-division multiple access and spatial reuse. Because of our explicit achievable rate expressions, we are able to compare the hierarchical cooperation scheme with multihop routing (i.e., decode-and-forward relaying), where the latter can be regarded as the current practice of device-to-device infrastructureless wireless networks. Our results show that the improved and optimized hierarchical cooperation schemes yield very significant rate gains over multihop routing in realistic conditions of channel propagation exponents, signal-to-noise ratio, and number of users. This sheds light on the long-standing question about the real advantage of hierarchical cooperation scheme over multihop routing beyond the well-known scaling laws analysis. In contrast, we also show that our rate optimization is nontrivial, since when hierarchical cooperation is applied with off-the-shelf choice of the system parameters, no significant rate gain with respect to multihop routing is achieved. We also show that for large pathloss exponent (e.g., α = 7), the sum rate is a nearly linear function of the number of users n in the range of networks of practical size (e.g., n ≤ 105). This also sheds light on a long-standing dispute on the effective achievability of linear sum rate scaling with hierarchical cooperation. Finally, we notice that the achievable sum rate for large α is much larger than for small α (e.g., α = 4). This suggests that the hierarchical cooperation scheme may be a very effective approach for networks operating at millimeter-waves, where the pathloss exponent is generally large.

We consider a wireless device-to-device (D2D) network where the nodes have precached information from a library of available files. Nodes request files at random. If the requested file is not in the on-board cache, then it is downloaded from some neighboring node via one-hop local communication. An outage event occurs when a requested file is not found in the neighborhood of the requesting node, or if the network admission control policy decides not to serve the request. We characterize the optimal throughput-outage tradeoff in terms of tight scaling laws for various regimes of the system parameters, when both the number of nodes and the number of files in the library grow to infinity. Our analysis is based on Gupta and Kumar protocol model for the underlying D2D wireless network, widely used in the literature on capacity scaling laws of wireless networks without caching. Our results show that the combination of D2D spectrum reuse and caching at the user nodes yields a per-user throughput independent of the number of users, for any fixed outage probability in (0, 1). This implies that the D2D caching network is scalable: even though the number of users increases, each user achieves constant throughput. This behavior is very different from the classical Gupta and Kumar result on ad hoc wireless networks, for which the per-user throughput vanishes as the number of users increases. Furthermore, we show that the user throughput is directly proportional to the fraction of cached information over the whole file library size. Therefore, we can conclude that D2D caching networks can turn memory into bandwidth (i.e., doubling the on-board cache memory on the user devices yields a 100% increase of the user throughout).

Interference alignment promises that, in Gaussian interference channels, each link can support half of a degree of freedom (DoF) per pair of transmit-receive antennas. However, in general, this result requires to precode the data bearing signals over a signal space of asymptotically large diversity, e.g., over an infinite number of dimensions for time-frequency varying fading channels, or over an infinite number of rationally independent signal levels, in the case of time-frequency invariant channels. In this paper, we consider a wireless cellular system scenario where the promised optimal DoFs are achieved with linear precoding in one-shot (i.e., over a single time-frequency slot). We focus on the uplink of a symmetric cellular system, where each cell is split into three sectors with orthogonal intrasector multiple access. In our model, interference is local, i.e., it is due to transmitters in neighboring cells only. We consider a noniterative local cooperation scheme where base stations pass to their neighbors their decoded messages such that interference from already decoded messages can be canceled. Therefore, for a given decoding order, the interference between sectors is described by a directed locally connected graph. The problem consists of maximizing the per-sector DoFs over all possible decoding orders and precoding schemes. In particular, we provide a decoding order and a one-shot interference alignment scheme able to achieve optimal per-sector DoFs, up to an additive gap due to boundary effects, that vanishes as the size of the network becomes large. Then, we extend our treatment by considering the case of intersector interference with joint processing of the three sector at each cell site. In order to avoid signaling schemes relying on the strength of interference, we further introduce the notion of topologically robust schemes, which are able to guarantee a minimum rate (or DoFs) irrespectively of the strength of the interfering links. Toward this end, we design a different decoding order and alignment scheme, which is topologically robust and still achieves the same optimum DoFs. Finally, we provide a new scheme for the downlink, based on local base station cooperation, where base stations pass to their neighbors a quantized version of their dirty-paper coded signals. For the proposed downlink scheme, we can prove a DoFs duality result showing that, for an appropriate choice of the precoding order and of the alignment beamforming vectors, it can achieve the same per-sector DoFs of the corresponding uplink schemes.

Although interference alignment (IA) can theoretically achieve the optimal degrees of freedom (DoFs) in the K-user Gaussian interference channel, its direct application comes at the prohibitive cost of precoding over exponentially many signaling dimensions. On the other hand, it is known that practical one-shot IA precoding (i.e., linear schemes without symbol expansion) provides a vanishing DoFs gain in large fully connected networks with generic channel coefficients. In our previous work, we introduced the concept of cellular IA for a network topology induced by hexagonal cells with sectors and nearest-neighbor interference. Assuming that neighboring sectors can exchange decoded messages (and not received signal samples) in the uplink, we showed that linear one-shot IA precoding over M transmit/ receive antennas can achieve the optimal M/2 DoFs per user. In this paper, we extend this framework to networks with omnidirectional (non-sectorized) cells and consider a limited practical scenario where users have 2 antennas, and base-stations have 2, 3, or 4 antennas. We provide linear one-shot IA schemes for the 2 × 2, 2 × 3, and 2 × 4 cases, and show the achievability of 3/4, 1, and 7/6 DoFs per user, respectively. DoFs converses for one-shot schemes require the solution of a discrete optimization problem over a number of variables that grows with the network size. We develop a new approach to transform such optimization problem into a tractable linear program with significantly fewer variables. This approach is used to show that 3/4 DoFs per user are indeed optimal for one-shot schemes over large (extended) cellular network with 2 × 2 links.

This paper investigates the maximal channel coding rate achievable at a given blocklength n and error probability ϵ, when the codewords are subjected to a long-term (i.e., averaged-over-all-codeword) power constraint. The second-order term in the large-n expansion of the maximal channel coding rate is characterized both for additive white Gaussian noise (AWGN) channels and for quasi-static fading channels with perfect channel state information available at both the transmitter and the receiver. It is shown that in both the cases, the second-order term is proportional to (n-1ln n)1/2. For the quasi-static fading case, this second-order term is achieved by truncated channel inversion, namely, by concatenating a dispersion-optimal code for an AWGN channel subject to a short-term power constraint, with a power controller that inverts the channel whenever the fading gain is above a certain threshold. Easy-to-evaluate approximations of the maximal channel coding rate are developed for both the AWGN and the quasi-static fading case.

Recently, the way people consume video content has been undergoing a dramatic change. Plain TV sets, that have been the center of home entertainment for a long time, are losing ground to hybrid TVs, PCs, game consoles, and, more recently, mobile devices such as tablets and smartphones. The new predominant paradigm is: watch what I want, when I want, and where I want. The challenges of this shift are manifold. On the one hand, broadcast technologies such as DVB-T/C/S need to be extended or replaced by mechanisms supporting asynchronous viewing, such as IPTV and video streaming over best-effort networks, while remaining scalable to millions of users. On the other hand, the dramatic increase of wireless data traffic begins to stretch the capabilities of the existing wireless infrastructure to its limits. Finally, there is a challenge to video streaming technologies to cope with a high heterogeneity of end-user devices and dynamically changing network conditions, in particular in wireless and mobile networks. In the present work, our goal is to design an efficient system that supports a high number of unicast streaming sessions in a dense wireless access network. We address this goal by jointly considering the two problems of wireless transmission scheduling and video quality adaptation, using techniques inspired by the robustness and simplicity of proportional-integral-derivative (PID) controllers. We show that the control-theoretic approach allows to efficiently utilize available wireless resources, providing high quality of experience (QoE) to a large number of users.

This paper studies the scalability of a wireless backhaul network modeled as a random extended network with multiantenna base stations (BSs), where the number of antennas per BS is allowed to scale as a function of the network size. The antenna scaling is justified by the current trend toward the use of higher carrier frequencies, which allows packing a large number of antennas in small form factors. The main goal is to study the per-BS antenna requirement that ensures scalability of this network, i.e., its ability to deliver nonvanishing rate to each source-destination pair. We first derive an information theoretic upper bound on the capacity of this network under a general propagation model, which provides a lower bound on the per-BS antenna requirement. Then, we characterize the scalability requirements for two competing strategies of interest: 1) long hop: each source-destination pair minimizes the number of hops by sacrificing multiplexing gain while achieving full beamforming (power) gain over each hop; and 2) short hop: each source-destination pair communicates through a series of short hops, each achieving full multiplexing gain. While long hop may seem more intuitive in the context of massive multiple-input-multiple-output transmission, we show that the short hop strategy is significantly more efficient in terms of per-BS antenna requirement for throughput scalability. As a part of the proof, we construct a scalable short hop strategy and show that it does not violate any fundamental limits on the spatial degrees of freedom.

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on the one-ring model and the Laplacian model. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence block length, though at higher computational complexity.

In this paper, we consider massive multiple-input-multiple-output systems for both downlink and uplink scenarios, where three radio units connected via one digital unit support multiple user equipments at the cell-boundary through the same radio resource, i.e., the same time-frequency slot. For downlink transmitter options, the study considers zero forcing (ZF) and maximum ratio transmission (MRT), whereas for uplink receiver options, it considers ZF and maximum ratio combining (MRC). For the sum rate of each of these, we derive simple closed-form formulas. In the simple but practically relevant case where uniform power is allocated to all downlink data streams, we observe that, for the downlink, vector normalization is better for ZF whereas matrix normalization is better for MRT. For a given antenna and user configuration, we also analytically derive the signal-to-noise-ratio level below which MRC should be used instead of ZF. Numerical simulations confirm our analytical results.

This paper reports an experimental demonstration of a 16 Gbit/s millimeter-wave communication link using MIMO processing of 2 OAM modes on each of two transmitter/receiver antenna apertures. Two groups of multiplexed OAM beams, each containing OAM beams of ℓ =1 and +3 are generated and transmitted through two transmitter apertures respectively. The two transmitter apertures are separated with a certain distance such that the two groups of OAM beams are spatially overlapping at the receiver aperture plane. Each channel carries 1-GBaud 16-QAM signals at the same carrier frequency of 28 GHz. Our experimental results show that MIMO equalization processing can help mitigate the interferences from the other OAM channels and the BER performance of each channel improves significantly after MIMO processing. Our results indicate that OAM multiplexing and traditional spatial multiplexing combined with MIMO processing can be compatible and complementary with each other.

We derive an information theoretic upper bound on the capacity of a wireless backhaul network modeled as a classical random extended network, except that we assume the number of antennas at each base station (BS) also scales up as an arbitrary function of network size. The antenna scaling is justified because of the increasing maturity of higher transmission frequencies which enables us to pack large number of antennas in small form factors. The main technical arguments are based on the generalization of geometric exponential stripping technique of [1] to channel matrices with complex-valued channel gains. An important consequence of our result is a lower bound on the number of antennas per BS required for network scalability.

This paper considers an extended wireless network with multi-antenna nodes in line-of-sight (LoS) propagation environment. Assuming that the number of antennas at each node can be scaled as some arbitrary function of the number of nodes, we study the scalability of this network, i.e., its ability to deliver non-zero rate to each source-destination pair. Since the rank of the LoS multiple-input multiple-output (MIMO) channel starts collapsing with the increasing separation between the transmitter and the receiver, we consider two competing transmission strategies: (i) long hop: each source-destination pair minimizes the number of hops by sacrificing multiplexing gain and ideally achieving full power gain over each hop, and (ii) short hop: each source-destination pair communicates through a series of short hops each achieving full multiplexing gain. By characterizing the number of antennas required to achieve scalability in both the cases, we show that the antenna requirement is significantly less for the short hop case. These results have key applications in the design of wireless backhaul for cellular networks, where the possibility of having massive MIMO links is becoming a reality due to the increasing maturity of higher transmission frequencies, e.g., 28 and 38 GHz.

We optimize the hierarchical cooperation protocol of Ozgur, Leveque and Tse, which is supposed to yield almost linear scaling of the capacity of a dense wireless network with the number of users n. Exploiting recent results on the optimality of “treating interference as noise” in Gaussian interference channels, we are able to optimize the achievable average perlink rate and not just its scaling law. Our optimized hierarchical cooperation protocol significantly outperforms the originally proposed scheme. On the negative side, we show that even for very large n, the rate scaling is far from linear, and the optimal number of stages t is less than 4, instead of t → ∞ as required for almost linear scaling. Combining our results and the fact that, beyond a certain user density, the network capacity is fundamentally limited by Maxwell laws, as shown by Francheschetti, Migliore and Minero, we argue that there is indeed no intermediate regime of linear scaling for dense networks in practice.

Interference alignment (IA) promises that, in Gaussian interference channels, each link can support half of a degree of freedom (DoF) per pair of transmit-receive antennas. However, in general, this result requires to precode the data bearing signals over a signal space of asymptotically large diversity, e.g., over an infinite number of dimensions in time-frequency for time-frequency varying fading channels. Here, we propose a communication scenario in wireless cellular systems where the promised optimal DoFs are achieved with linear precoding in one-shot (coding over a single time-frequency slot). We focus on uplink cellular systems, where each cell is split into three sectors and assume that interference is generated locally between transmitters and receivers of neighboring cells. We consider a message-passing network architecture, in which nearby sectors can exchange already decoded messages and propose an alignment solution that can achieve the optimal DoFs. To avoid signaling schemes relying on the strength of interference, we further introduce the notion of topologically robust schemes, which are able to guarantee a minimum rate (or degrees of freedom) no matter if the interference link are strong or weak. Towards this end, we design an alignment scheme which is topologically robust and still achieves the same optimum DoFs.

We introduce a new neighbor discovery method called “ZigZag”. We present two versions of ZigZag, namely, deterministic and random. We show a close connection between performance of ZigZag and message passing decoding of low density parity check codes over an erasure channel.

We consider a MIMO (linear Gaussian) channel where the inputs are turned on and off at random, and the outputs are sampled at random with probability p. In particular, for a given probability of “on” input q (input sparsity), we consider a scenario where the transmitter wishes to send information to a family of possible receivers characterized by different random sampling rates p ∈ [0,1]. For this setting, we focus on the broadcast approach, i.e., a coding technique where the transmitter sends information encoded into superposition layers, such that the number of decoded layers depends on the receiver sampling rate p. We obtain a method for calculating the power allocation across the layers for given statistics of the MIMO channel matrix in order to maximize the system weighted sum rate for arbitrary non-negative weighting function w(p). In particular, we provide analytical solutions both for iid and Haar distributed MIMO channel matrices. The latter case accounts also for DFT matrices (see [1]), with application to sparse spectrum signals with random sub-Nyquist sampling.

This paper investigates the maximal channel coding rate achievable at a given blocklength n and error probability ε, when the codewords are subject to a long-term (i.e., averaged-over-all-codeword) power constraint. The second-order term in the large-n expansion of the maximal channel coding rate is characterized both for AWGN channels and for quasi-static fading channels with perfect channel state information at the transmitter and the receiver. It is shown that in both cases the second-order term is proportional to √(log n)/n.

Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in somerealisticpropagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as inmeasuredoutdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.

Several works have developed vector-linear maximum-distance separable (MDS) storage codes that minimize the total communication cost required to repair a single coded symbol after an erasure, referred to as repair bandwidth (BW). Vector codes allow communicating fewer sub-symbols per node, instead of the entire content. This allows non trivial savings in repair BW. In sharp contrast, classic codes, like Reed-Solomon (RS), used in current storage systems, are deemed to suffer from naive repair, i.e. downloading the entire stored message to repair one failed node. This mainly happens because they are scalar-linear. In this work, we present a simple framework that treats scalar codes as vector-linear. In some cases, this allows significant savings in repair BW. We show that vectorized scalar codes exhibit properties that simplify the design of repair schemes. Our framework can be seen as a finite field analogue of real interference alignment. Using our simplified framework, we design a scheme that we call clique-repair which provably identifies the best linear repair strategy for any scalar 2-parity MDS code, under some conditions on the sub-field chosen for vectorization. We specify optimal repair schemes for specific (5,3)- and (6,4)-Reed-Solomon (RS) codes. Further, we present a repair strategy for the RS code currently deployed in the Facebook Analytics Hadoop cluster that leads to 20% of repair BW savings over naive repair which is the repair scheme currently used for this code.

Orthogonal Frequency Division Multiplexing (OFDM) is a modulation scheme that is widely used in wired and wireless communication systems. While OFDM is ideally suited to deal with frequency selective channels and AWGN, its performance may be dramatically impacted by the presence of impulse noise. In fact, very strong noise impulses in the time domain might result in the erasure of whole OFDM blocks of symbols at the receiver. Impulse noise can be mitigated by considering it as a sparse signal in time, and using recently developed algorithms for sparse signal reconstruction. We propose an algorithm that utilizes the guard band null subcarriers for the impulse noise estimation and cancellation. Instead of relying on \ell_1 minimization as done in some popular general-purpose compressive sensing schemes, the proposed method jointly exploits the specific structure of this problem and the available a priori information for sparse signal recovery. The computational complexity of the proposed algorithm is very competitive with respect to sparse signal reconstruction schemes based on \ell_1 minimization. The proposed method is compared with respect to other state-of-the-art methods in terms of achievable rates for an OFDM system with impulse noise and AWGN.

We present a framework to study linear deterministic interference networks over finite fields. Unlike the popular linear deterministic models introduced to study Gaussian networks, we consider networks where the channel coefficients are general scalars over some extension held F(pm) (scalar mth extension held models), m x m diagonal matrices over Fp(m-symbol extension ground held models), and m x m general nonsingular matrices (multiple-input and multiple-output (MIMO) ground held models). We use the companion matrix representation of the extension held to convert mth extension scalar models into MIMO ground held models, where the channel matrices have special algebraic structure. For such models, we consider the 2×2×2 topology (two-hop two-flow) and the three-user interference network topology. We derive achievability results and feasibility conditions for certain schemes based on the precoding-based network alignment (PBNA) approach, where intermediate nodes use random linear network coding (i.e., propagate random linear combinations of their incoming messages) and nontrivial precoding/decoding is performed only at the network edges, at the sources and destinations. Furthermore, we apply this approach to the scalar 2 × 2 × 2 complex Gaussian interference channel with fixed channel coefficients and show two competitive schemes outperforming other known approaches at any SNR, where we combine finite field linear precoding/decoding with lattice coding and the compute and forward approach at the signal level. As a side result, we also show significant advantages of vector linear network coding both in terms of feasibility probability (with random coding coefficients) and in terms of coding latency, with respect to standard scalar linear network coding, in PBNA schemes.

The minimum energy, and, more generally, the minimum cost, to transmit 1 bit of information was recently derived for bursty communication when the information is available infrequently at random times at the transmitter. This result assumes that the receiver is always in the listening mode and samples all channel outputs until it makes a decision. Since sampling is in practice one of the receiver's most energy consuming functions, a natural question is to evaluate capacity per unit cost when the receiver is sampling constrained. This paper investigates such a setting where the receiver can sample only a given fraction ρ ∈ (0, 1] of the channel outputs. It is shown that regardless of ρ > 0, the asynchronous capacity per unit cost is the same as under full sampling, i.e., when ρ = 1. Moreover, a sparse output sampling does not even impact decoding delay-the elapsed time between when information is available and when it is decoded. Hence, surprisingly, it suffices to sample an arbitrarily small fraction of the channel outputs and yet achieve the same (asymptotic) performance as under full output sampling.

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the downlink channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.

We present the first deterministic measurement matrix construction with an order-optimal number of rows for sparse signal reconstruction. This improves the measurements required in prior constructions and addresses a known open problem in the theory of sparse signal recovery. Our construction uses adjacency matrices of bipartite graphs that have large girth. The main result is that girth (the length of the shortest cycle in the graph) can be used as a certificate that a measurement matrix can recover almost all sparse signals. Specifically, our matrices guarantee recovery “for-each” sparse signal under basis pursuit. Our techniques are coding theoretic and rely on a recent connection of compressed sensing to LP relaxations for channel decoding.

We study the three-user interference channel where each transmitter has local feedback of the signal from its targeted receiver. We show that in the important case where the channel coefficients are static, exact alignment can be achieved over two time slots using linear schemes. This is in contrast with the interference channel where no feedback is utilized, where it seems that either an infinite number of channel extensions or infinite precision is required for exact alignment. We also demonstrate, via simulations, that our scheme outperforms time-sharing even at finite SNR.

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive video streaming over small cell networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions for a small-cell network.

We study a two-user cognitive interference channel (CIC) where one of the transmitters (primary) has knowledge of a linear combination (over an appropriate finite-field) of the two information messages. We refer to this channel model as Network-Coded CIC, since the linear combination may be the result of some linear network coding scheme implemented in the backbone wired network. In this paper, we characterize the generalized degrees of freedom (GDoF) for the Gaussian Network-Coded CIC. For achievability, we use the novel Precoded Compute-and-Forward (PCoF) and Dirty Paper Coding (DPC), based on nested lattice codes. Through the GDoF characterization, we show that knowing “mixed data” (a linear combination of the information messages) provides an unbounded spectral efficiency gain over the classical CIC counterpart, if the ratio (in dB) of signal-to-noise (SNR) to interference-to-noise (INR) is larger than ceratin threshold. For example, when SNR = INR, the Network-Coded cognition yields a 100% gain over the classical Gaussian CIC.

We consider the 2 × 2 × 2 multiple-input multiple-output interference channel where two source-destination pairs wish to communicate with the aid of two intermediate relays. In this paper we present a novel lattice strategy called Precoded Compute-and-Forward (PCoF) with Channel Integer Alignment (CIA). This scheme consists of two phases: 1) Using the CoF framework based on CIA we convert the Gaussian network into a deterministic finite-field network. 2) Using linear precoding (over finite-field) we eliminate the end-to-end interference in the finite-field domain. Further, we exploit the algebraic structure of lattices to enhance the performance at finite SNR, such that beyond a degree of freedom result (also achievable by other means). We can also show that PCoF with CIA outperforms timesharing in a range of reasonably moderate SNR, with increasing gain as SNR increases.

We consider a wireless device-to-device (D2D) network where the nodes have cached information from a library of possible files. Inspired by the current trend in the standardization of the D2D mode for 4th generation wireless networks, we restrict to one-hop communication: each node places a request to a file in the library, and downloads from some other node which has the requested file in its cache through a direct communication link, without going through a base station. We describe the physical layer communication through a simple “protocol-model”, based on interference avoidance (independent set scheduling). For this network we define the outage-throughput tradeoff problem and characterize the optimal scaling laws for various regimes where both the number of nodes and the files in the library grow to infinity.

In this paper, we propose a novel framework, integer-forcing interference alignment, that can simultaneously exploit both signal-space and signal-scale alignment. We consider receivers that can decode integer-linear combinations of desired and interfering streams and then solve for their desired symbols. This is possible by using appropriate lattice codes at the transmitters and can be applied to the class of wireless communication systems that use linear beamforming. At the core of our architecture lies the compute-and-forward framework, which we extend here to encompass asymmetric power allocations. We evaluate the performance of our scheme in the context of the three-user interference channel through simulation results.

The minimum energy, and, more generally, the minimum input cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver can sample at no cost all channel outputs. Suppose now there is a cost associated to output sampling and that the receiver is constrained to observe only a fraction ρ ϵ (0, 1] of all channel outputs. What is the input cost penalty due to sparse output sampling? Remarkably, there is no penalty: regardless of ρ > 0 the asynchronous capacity per unit cost is the same as under full sampling, i.e., when ρ = 1. Moreover, there is no penalty in terms of decoding delay with respect to full sampling. This latter result relies on the possibility to sample adaptively; the next sample is a function of past samples. When sampling is non-adaptive it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/ρ. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.

We study the problem of neighbor discovery in which each node desires to detect nodes within a single hop. Each node is assigned a unique signature known by all other nodes. The problem can be considered as a compressed sensing problem. We propose a explicit-non-random-construction for the signatures. Further, we suggest the basis pursuit to detect the neighbors and offer a guarantee for its performance. Specifically, we show that the average number of errors can be made arbitrary small as the number of nodes in the network grows. Our result does not depend on the density of the network, i.e., how the average number of neighbors scales with respect to the total number of nodes.