This paper considers recovering a continuous angular power spectrum (APS) from the channel covariance. Building on the projection-onto-linear-variety (PLV) algorithm, an affine-projection approach introduced by Miretti \emph{et. al.}, we analyze PLV in a well-defined \emph{weighted} Fourier-domain to emphasize its geometric interpretability. This yields an explicit fixed-dimensional trigonometric-polynomial representation and a closed-form solution via a positive-definite matrix, which directly implies uniqueness. We further establish an exact energy identity that yields the APS reconstruction error and leads to a sharp identifiability/resolution characterization: PLV achieves perfect recovery if and only if the ground-truth APS lies in the identified trigonometric-polynomial subspace; otherwise it returns the minimum-energy APS among all covariance-consistent spectra.

Massive connectivity supports the sporadic access of a vast number of devices without requiring prior permission from the base station (BS). In such scenarios, the BS must perform joint activity detection and channel estimation (JADCE) prior to data reception. Message passing algorithms have emerged as a prominent solution for JADCE under a Bayesian inference framework. The existing message passing algorithms, however, typically rely on some hand-crafted and overly simplistic priors of the wireless channel, leading to significant channel estimation errors and reduced activity detection accuracy. In this paper, we focus on the problem of JADCE in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) grant-free random access network. We propose to incorporate a more accurate channel prior learned by score-based generative models (a.k.a. diffusion models) into message passing, so as to push towards the performance limit of JADCE. Specifically, we develop a novel turbo message passing (TMP) framework that models the entire channel matrix as a super node, rather than factorizing it element-wise. This design enables the seamless integration of score-based generative models as a minimum mean-squared error (MMSE) denoiser. The variance of the denoiser, which is essential in message passing, can also be learned through score-based generative models. Our approach, termed score-based TMP for JADCE (STMP-JADCE), takes full advantages of the powerful generative prior and, meanwhile, benefits from the fast convergence speed of message passing. Numerical simulations show that STMP-JADCE drastically enhances the activity detection and channel estimation performance compared to the state-of-the-art baseline algorithms.

Decentralized federated learning (DFL) is an emerging paradigm to enable edge devices collaboratively training a learning model using a device-to-device (D2D) communication manner without the coordination of a parameter server (PS). Aggregation weights, also known as mixing weights, are crucial in DFL process, and impact the learning efficiency and accuracy. Conventional design relies on a so-called central entity to collect all local information and conduct system optimization to obtain appropriate weights. In this paper, we develop a distributed aggregation weight optimization algorithm to align with the decentralized nature of DFL. We analyze convergence by quantitatively capturing the impact of the aggregation weights over decentralized communication networks. Based on the analysis, we then formulate a learning performance optimization problem by designing the aggregation weights to minimize the derived convergence bound. The optimization problem is further transformed as an eigenvalue optimization problem and solved by our proposed subgradient-based algorithm in a distributed fashion. In our algorithm, edge devices only need local information to obtain the optimal aggregation weights through local (D2D) communications, just like the learning itself. Therefore, the optimization, communication, and learning process can be all conducted in a distributed fashion, which leads to a genuinely distributed DFL system. Numerical results demonstrate the superiority of the proposed algorithm in practical DFL deployment.

Decentralized machine learning (DML) supports collaborative training in large-scale networks with no central server. It is sensitive to the quality and reliability of inter-device communications that result in time-varying and stochastic topologies. This paper studies the impact of unreliable communication on the convergence of DML and establishes a direct connection between the spectral properties of the mixing process and the global performance. We provide rigorous convergence guarantees under random topologies and derive bounds that characterize the impact of the expected mixing matrix's spectral properties on learning. We formulate a spectral optimization problem that minimizes the nontrivial spectral radius of the expected second-order mixing matrix to enhance the convergence rate under probabilistic link failures. To solve this non-smooth spectral problem in a fully decentralized manner, we design an efficient subgradient-based algorithm that integrates Chebyshev-accelerated eigenvector estimation with local update and aggregation weight adjustment, while ensuring symmetry and stochasticity constraints without central coordination. Experiments on a realistic low Earth orbit satellite constellation with time-varying inter-satellite link models and real-world remote sensing data demonstrate the feasibility and effectiveness of our method. The method significantly improves classification accuracy and convergence efficiency compared to existing baselines, validating its applicability in satellite and other decentralized systems.

Spatial multiplexing in far-field (FF) multiple-input multiple-output (MIMO) systems typically relies on the multipath provided by scatterers. Recent studies have revealed that near-field (NF) MIMO systems can exploit spatial multiplexing even in line-of-sight (LoS) propagation. Through singular value decomposition (SVD), an orthogonal basis consisting of the channel’s right singular vectors can be obtained to formulate a beamforming matrix. This matrix maps multiple data streams into orthogonal sub-channels, enabling spatial multiplexing. However, the SVD-based beamforming matrix, composed of entries with varying magnitudes and phases, generally requires a costly full digital array architecture. To achieve spatial multiplexing with low-complexity beamforming, this paper explores the channel’s structural characteristics of NF-LoS MIMO systems. Specifically, we consider a general point-to-point MIMO setup for a pair of uniform linear arrays (ULAs). We first investigate the ability of the transmitter (Tx) array to resolve paths to different Rx antennas under the NF spherical wave model. Based on the derived resolution, we explicitly formulate a quasi-orthogonal (QO) basis consisting of NF beamfocusing vectors, ensuring that the normalized magnitude of any two QO vectors remains sufficiently small to meet practical application requirements. By appropriately arranging these QO vectors into a beamforming matrix, we transform the spatial channel into a beamspace channel, where the beams serve as QO sub-channels. Furthermore, we utilize these QO beams or sub-channels for beamspace modulation (BM), where the Tx dynamically selects beams to deliver information via beam index modulation and via spatial multiplexing. Importantly, the beamformer designed for selecting QO beams, consisting of constant-magnitude entries, can be implemented using only analog phase shifters. Compared with the existing SVD based BM, the proposed scheme simplifies implementation in hybrid array architectures and achieves high spectrum efficiency with reduced computation complexity. We also show the feasibility of the proposed scheme in practical scenarios, including imperfect channel state information (CSI), finite-resolution phase shifters, mixed LoS and none-line-of-sight (NLoS) channels, and uniform planar arrays (UPAs).

This paper studies asynchronous repetition slotted ALOHA (a-ReSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We show that owing to user-asynchrony, the channel state information (CSI) can be acquired even when some users utilize an identical pilot sequence, i.e., pilot collision happens. Further, we develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-ReSA is utilized to solve packet collision. To characterize the performance of a-ReSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Our analysis shows that the ACPR of the user-asynchronous scenario is considerably larger than that of user-synchronous scenarios. Further, we present an asymptotic analysis of the throughput of a-ReSA system. It is demonstrated that the normalized throughput of a-ReSA exceeds that of traditional ReSA by about 20% ~ 80%. We also show that the a-ReSA scheme is more robust than traditional ReSA in imperfect CSI scenarios. Numerical results are verified to agree with the analyzed results.

Time-of-arrival (ToA)-based user localization typically requires precise clock synchronization between base stations and user equipments, making the localization and synchronization problems tightly coupled with each other. This paper considers a distributed multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system and addresses joint multi-user localization and clock synchronization within an integrated sensing and communication (ISAC) framework. Unlike existing works that only consider clock bias, we account for the impacts of both clock bias and clock skew on MIMO-OFDM signals. Specifically, clock bias introduces a constant offset in path delays, whereas clock skew causes a mismatch in the OFDM symbol durations between the transmitter and the receiver, resulting in linearly varying delays across OFDM symbols. We formulate the joint localization and synchronization problem within a Bayesian framework. Based on variational message passing and the sum-product rule, we propose a message passing algorithm, termed Bayesian Localization and Clock Synchronization (BLACS), which jointly estimates the positions, clock parameters, and velocities of multiple users. Simulation results show that accounting for clock skew significantly improves localization accuracy compared to the baseline methods. Moreover, the proposed BLACS algorithm achieves performance close to the Bayesian Cramér–Rao Bound, demonstrating its effectiveness and near-optimality.

Holographic surface based communication technologies are anticipated to play a significant role in the next generation of wireless networks. The existing reconfigurable holographic surface (RHS)-based scheme only utilizes the reconstruction process of the holographic principle for beamforming, where the channel state information (CSI) is needed. However, channel estimation for CSI acquirement is a challenging task in metasurface based communications. In this study, inspired by both the recording and reconstruction processes of holography, we develop a novel holographic communication scheme by introducing recordable and reconfigurable metasurfaces (RRMs), where channel estimation is not needed thanks to the recording process. Then we analyze input-output mutual information and outage probability of the RRM-based communication system and compare it with the existing RHS based system. Our results show that, without channel estimation, the proposed scheme achieves performance comparable to that of the RHS scheme with perfect CSI, suggesting a promising alternative for future wireless communication networks.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output (MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing (MAMP) to estimate the channel state information (CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.

This paper studies the near-field localization problem under dynamic scenarios, which harnesses the non-line-of-sight (NLoS) components, in a reconfigurable intelligent surface (RIS)-aided system equipped with extremely large-scale multi-input multi-output (XL-MIMO). To reduce the complexity of the position estimation, the subarray far-field model is employed to approximate the near-field channel. A factor graph within a Bayesian framework is constructed to detail the probability transition relationship among the relevant variables. Based on the message passing in this factor graph, a near-field localization algorithm is developed to estimate the marginal probability distributions of the UE’s and scatterers’ positions in each time slot. The misspecified Cramér-Rao Lower Bound (MCRLB) is derived to evaluate the performance of the algorithm under the subarray far-field model. To explore the localization potential of the system, a closed-form solution for a low-complexity directional beamforming design and a robust beamforming design based on the gradient descent method (GDM) are further proposed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark schemes, and validate the performance gain of harnessing the NLoS components.

With explosive increase of data at the mobile edge, federated learning (FL) emerges as a promising technique to reduce data transmission costs and privacy leakage risks. Nevertheless, the huge communication overhead for an increasing volume of edge devices still restricts the FL performance. Over-the-air computation (AirComp) is viable for alleviating the communication burden in FL systems. However, there consequently appears a straggler issue restraining the performance of the over-the-air FL (OA-FL) framework, which is even worse especially when devices training a machine learning model are distributed over a relatively large service area. In this paper, we propose an unmanned aerial vehicle (UAV) enabled OA-FL scheme, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. The global aggregation frequency is tunable in the hierarchical aggregation approach, enabling it to balance the resource consumption between communication and learning. Building on this approach, we carry out a gradient-correlation-aware FL performance analysis and jointly optimize the trajectory of UAV-PS, the device selection state, and the aggregation coefficients. An algorithm based on alternating optimization (AO) is developed to solve the formulated problem, where successive convex approximation (SCA) and fractional programming (FP) are utilized for the convexification of the non-convex problem. Numerical simulation results demonstrate the effectiveness of our UAV enabled hierarchical aggregation scheme compared with several existing baselines.

The distortion-perception (DP) tradeoff reveals a fundamental conflict between distortion metrics (e.g., MSE and PSNR) and perceptual quality. Recent research has increasingly concentrated on evaluating denoising algorithms within the DP framework. However, existing algorithms either prioritize perceptual quality by sacrificing acceptable distortion, or focus on minimizing MSE for faithful restoration. When the goal shifts or noisy measurements vary, adapting to different points on the DP plane needs retraining or even re-designing the model. Inspired by recent advances in solving inverse problems using score-based generative models, we explore the potential of flexibly and optimally traversing DP tradeoffs using a single pre-trained score-based model. Specifically, we introduce a variance-scaled reverse diffusion process and theoretically characterize the marginal distribution. We then prove that the proposed sample process is an optimal solution to the DP tradeoff for conditional Gaussian distribution. Experimental results on two-dimensional and image datasets illustrate that a single score network can effectively and flexibly traverse the DP tradeoff for general denoising problems.

This paper studies asynchronous repetition slotted ALOHA (a-RSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-RSA is utilized to solve packet collision. To characterize the performance of a-RSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Further, we present an asymptotic analysis of the throughput of a-RSA system. It is demonstrated that the normalized throughput of a-RSA exceeds that of traditional RSA by about 20% ~ 30%.

This paper addresses the problem of end-to-end (E2E) design of learning and communication in a task-oriented semantic communication system. In particular, we consider a multi-device cooperative edge inference system over a wireless multiple-input multiple-output (MIMO) multiple access channel, where multiple devices transmit extracted features to a server to perform a classification task. We formulate the E2E design of feature encoding, MIMO precoding, and classification as a conditional mutual information maximization problem. However, it is notoriously difficult to design and train an E2E network that can be adaptive to both the task dataset and different channel realizations. Regarding network training, we propose a decoupled pretraining framework that separately trains the feature encoder and the MIMO precoder, with a maximuma posteriori(MAP) classifier employed at the server to generate the inference result. The feature encoder is pretrained exclusively using the task dataset, while the MIMO precoder is pretrained solely based on the channel and noise distributions. Nevertheless, we manage to align the pretraining objectives of each individual component with the E2E learning objective, so as to approach the performance bound of E2E learning. By leveraging the decoupled pretraining results for initialization, the E2E learning can be conducted with minimal training overhead. Regarding network architecture design, we develop two deep unfolded precoding networks that effectively incorporate the domain knowledge of the solution to the decoupled precoding problem. Simulation results on both the CIFAR-10 and ModelNet10 datasets verify that the proposed method achieves significantly higher classification accuracy compared to various baselines.

This paper considers the joint compression of a pair of correlated sources, where the encoder is allowed to access only one of the sources. The objective is to recover both sources under separate distortion constraints for each source while minimizing the rate. This problem generalizes the indirect lossy source coding problem by also requiring the recovery of the observed source. In this paper, we aim to study the nonasymptotic and second-order asymptotic properties of this problem. Specifically, we begin by deriving nonasymptotic achievability and converse bounds valid for general sources and distortion measures. The source dispersion (Gaussian approximation) is then determined through asymptotic analysis of the nonasymptotic bounds. We further examine the case of erased fair coin flips (EFCF) and provide its specific nonasymptotic achievability and converse bounds. Numerical results under the EFCF case demonstrate that our second-order asymptotic approximation closely approximates the optimum rate at appropriately large blocklengths.

Heavy rainfall and earthquake in mountain areas usually trigger numerous landslides. Fast and accurate mapping of landslides is crucial for risk management and emergency rescue. Deep learning-based landslide detection methods can automate identification, but convolutional neural network (CNN) models focus primarily on local features, often missing crucial global context in landslide images. Conversely, Transformer-based models excel at capturing global features but are hindered by high computational complexity. As a result, existing detection models struggle to strike an effective balance between accuracy and efficiency. To address this issue, this article presents a lightweight landslide detection method based on the newly proposed Mamba network. Specifically, a landslide detection model named SegMamba2D with an encoder–decoder structure is proposed. In the encoder, the Mamba network is used to extract multiscale features. A state-space model (SSM) is employed to reduce computational complexity while maintaining accuracy. In the decoder, a multilayer perceptron is used to build a lightweight decoder, ensuring that the model’s overall complexity remains low. The experimental results on both public and new datasets demonstrate that SegMamba2D achieves a superior landslide detection accuracy, with an approximately 2% improvement in$F1$score across various scenarios over conventional models, while significantly reducing computational costs. Additionally, SegMamba2D demonstrates robust generalization performance across diverse research areas. These advancements highlight the model’s potential to enhance accuracy in creating landslide inventories and expedite emergency response times during landslide disasters. The source code is available athttps://github.com/xiaochuan-tang/SegMamba2D

Compressive Robust Principal Component Analysis (CRPCA) naturally arises in various applications as a means to recover a low-rank matrix low-rank matrix$\boldsymbol {L}$and a sparse matrix$\boldsymbol {S}$from compressive measurements. In this paper, we approach the problem from a Bayesian inference perspective. We establish a probabilistic model for the problem and develop an improved turbo message passing (ITMP) algorithm based on the sum-product rule and the appropriate approximations. Additionally, we establish a state evolution framework to characterize the asymptotic behavior of the ITMP algorithm in the large-system limit. By analyzing the established state evolution, we further propose sufficient conditions for the global convergence of our algorithm. Our numerical results validate the theoretical results, demonstrating that the proposed asymptotic framework accurately characterize the dynamical behavior of the ITMP algorithm, and the phase transition curve specified by the sufficient condition agrees well with numerical simulations.

The attitude of an unmanned aerial vehicle (UAV) is highly related to its motion status, such as velocity and acceleration, and thus needs to be taken into consideration in UAV localization and tracking. In this paper, we study a short-range UAV localization and tracking system, where a UAV flies in the near-field region of an extremely large antenna array (ELAA). The ELAA is arranged to track the UAV by continuously estimating its position and attitude. To accomplish this task, we leverage an array partitioning approach to establish the signal model between the UAV and the ELAA based on the subarray-wise far-field assumption. Then, we characterize the relationship between UAV’s attitude and motion status based on force analysis. Building on the analysis, we formulate a probabilistic UAV tracking problem that jointly estimates the UAV position and attitude in an online fashion. A new message-passing-based algorithm is proposed to solve this problem, which combines attitude and motion status information to enhance tracking performance. We also derive the Bayesian Cramér Rao bound (BCRB) of the problem as a performance benchmark. Numerical results show that the proposed algorithm outperforms other alternatives, and demonstrate that the information fusion of the UAV attitude and motion status can effectively improve the accuracy of the UAV localization and tracking.

This paper investigates the fundamental problem of cascaded channel estimation in reconfigurable intelligent surface (RIS) aided multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. To address the high pilot overhead required by existing methods, we introduce appropriate auxiliary variables to decompose the received signal model into a subcarrier-wise bilinear sub-model and two linear sub-models with respect to the MU-to-RIS and RIS-to-base-station (BS) cascaded channels. The proposed model decomposition maintains the matrix factorization structure of the two cascaded channels and avoids the problem of parameter expansion in exiting methods. In the two linear sub-models, in addition, the cascaded channels are transformed into the delay-angle domain to leverage the joint sparsity. Based on the established models, we consider the problem of simultaneously estimating the MU-to-RIS and RIS-to-BS channel matrices as a bilinear estimation problem. To tackle this problem, we formulate a Bayesian inference framework and develop a hybrid message-passing (HVMP) algorithm to achieve approximate Bayesian inference by leveraging the Bethe method. Notably, the HVMP algorithm infers the two cascaded channels iteratively, where the covariance of each cascaded channel matrix is estimated to characterize the correlation of the matrix elements. Simulation results show that the proposed algorithm achieves accurate channel estimation with low pilot overhead while state-of-the-art baseline schemes exhibit poor performance. Furthermore, the proposed algorithm can approach the estimation oracle bound of the MU-to-RIS (or RIS-to-BS) channel which assumes perfect knowledge of the RIS-to-BS (or MU-to-RIS) channel.

Channel knowledge map (CKM) is viewed as a digital twin of wireless channels, providing location-specific channel knowledge for environment-aware communications. A fundamental problem in CKM-assisted communications is how to construct the CKM efficiently. Current research focuses on interpolating or predicting channel knowledge based on error-free channel knowledge from measured regions, ignoring the extraction of channel knowledge. This paper addresses this gap by unifying the extraction and representation of channel knowledge. We propose a novel CKM construction framework that leverages the received signals of the base station (BS) as online and low-cost data. Specifically, we partition the BS coverage area into spatial grids. The channel knowledge per grid is represented by a set of multi-path powers, delays, and angles, based on the principle of spatial consistency. In extracting these channel parameters, the challenges lie in strong inter-cell interference and non-linear relationships between received signals and channel parameters. To address these issues, we formulate the problem of CKM construction into a problem of Bayesian inference, employing a interference-activity prior model to characterize the path-loss differences of interferers. Under the Bayesian inference framework, we develop a hybrid message-passing algorithm for the interference-cancellation-based CKM construction. Based on the CKM, we obtain the joint frequency-space covariance of the user channel and design a CKM-assisted Bayesian channel estimator. The computational complexity of the channel estimator is substantially reduced by exploiting the CKM-derived covariance structure. Numerical results show that the proposed CKM provides accurate channel parameters at low signal-to-interference-plus-noise ratio (SINR) and that the CKM-assisted channel estimator significantly outperforms state-of-the-art counterparts.

This paper addresses the challenge of blind massive connectivity in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, focusing on improving the efficiency of ultra massive machine-type communications (umMTCs) in sixth-generation (6G) wireless networks. We formulate blind massive connectivity as a Bayesian trilinear factorization problem involving the joint estimation of the angular array response, channel coefficients, and data signals. To solve the problem, we propose a novel algorithm, termed hybrid vector message passing for trilinear factorization (TF-HVMP), under a unified Bayesian inference framework. The TF-HVMP algorithm mitigates the energy leakage effect and provides superior performance in joint active user identification, channel estimation, and signal detection. Furthermore, we utilize the replica method to analyze the asymptotic performance of the trilinear factorization problem. Numerical results show that TF-HVMP closely approaches the replica bound and exhibits significant performance gains over the state-of-the-art algorithms.

To address the limited server coverage of traditional over-the-air federated learning (OA-FL), we propose a new OA-FL framework for MIMO-based cloud radio access network (Cloud-RAN), called MIMO Cloud-RAN OA-FL (MIMOCROF). The proposed MIMOCROF consists of three stages in each training round. The first stage of edge aggregation allows each access point (AP) to collect local updates from edge devices and construct an edge update using MIMO multiple access. In the second stage of global aggregation, the cloud server (CS) aggregates edge updates received from the APs to form a global update through a fronthaul network. In the third stage of model updating and broadcasting, the CS sends the updated global model parameters to the APs, and the latter then broadcast the parameters to their served devices. To effectively exploit inter-AP correlation, we model the global aggregation stage as a lossy distributed source coding (L-DSC) problem. Based on the rate-distortion theory, we further analyze the performance of the MIMOCROF framework. We formulate a communication-learning optimization problem to improve the system performance by considering the inter-AP correlation. To solve this problem, we develop an algorithm by using alternating optimization (AO) and majorization-minimization (MM). Furthermore, we propose a practical L-DSC that exploits inter-AP correlation. Numerical results show that the proposed practical L-DSC effectively utilizes inter-AP correlation and is superior to other baseline schemes in performance.

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramér-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).

In dual-functional radar-communication (DFRC) systems, to achieve desirable performance in both direction-of-arrival (DoA) estimation for sensing targets and wireless communication for user equipments (UEs), the Cramér-Rao bound (CRB) for DoA estimation and the communication rates of UEs should be jointly optimized through beamforming design. However, the CRB function is inherently dependent on prior knowledge of DoA, which may only be obtained through existing estimators that introduce unavoidable estimation errors. Such errors inevitably degrade the optimization performance. To address this issue, we propose novel optimization methodologies for CRB minimization and communication rate guarantees, which effectively reduce the adverse impact of target DoA estimation errors. Specifically, considering a bounded DoA error model and a statistical DoA error model, two optimization problems are formulated to optimize the worst-case CRB and the statistical mean of CRB over the DoA error regions, while ensuring that the communication rates of multiple UEs exceed predefined thresholds. To tackle the first problem, we propose a semidefinite relaxation (SDR)-based iterative entropic regularization (SDR-IER) method, acquiring approximate solutions via alternating outer minimization and inner maximization. For the second problem, we develop a vectorial space analysis (VSA)-based projected stochastic gradient descent (VSA-PSGD) approach, featuring closed-form projections per iteration for single-user cases, and successive convex approximation (SCA)-based projections for multi-user cases. Simulation results demonstrate that our proposed methods exhibit enhanced robustness against target DoA estimation errors, compared with the existing benchmarks that do not take these errors into account.

This paper studies a localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in the near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of both localization accuracy and computational complexity.

To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). By collectively training a unified learning model on edge devices, FL bypasses the need of direct data transmission, thereby addressing problems such as latency issues and privacy concerns inherent in centralized ML. However, in practical deployment FL suffers from low learning efficiency due to the involved straggler issue and huge uplink overhead. In this paper, we develop a UAV-enabled over-the-air asynchronous FL (UAV-AFL) framework to address this problem. This framework significantly enhance the learning efficiency by supporting the UAV as the parameter server (UAV-PS) in collecting data over-the-air and updating model continuously. We conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning efficiency. Based on this analysis, a unified communication-learning problem is formulated to maximize asymptotical learning accuracy by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results reveal valuable insights for the system design and demonstrate that the proposed UAV-AFL scheme achieves substantially improvement in learning efficiency compared with the state-of-the-art approaches.

The communication bottleneck of over-the-air federated learning (OA-FL) lies in aggregating the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradient aggregation by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. Based on the analysis results, we show that the optimal number of multiplexed data streams to minimize the upper bound on the FL learning performance loss is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem for the design of precoding and post-processing matrices to minimize the gradient aggregation error. To solve this problem, we develop an efficient algorithm based on alternating optimization (AO) and Karush-Kuhn-Tucker (KKT) conditions, which effectively mitigates the impact of the gradient aggregation error. Numerical results demonstrate the superb performance of the proposed SCoM approach.

In this paper, we consider a task-oriented communication system for multi-device edge inference over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (feature encoding and classification) and communication (precoding) modules are designed to achieve the same goal of inference accuracy maximization. End-to-end learning of these modules involves both the task dataset and the time-varying wireless channel, which may result in unaffordable training overhead and complexity. Instead, we advocate a modular design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem separated from the learning design. We develop a block coordinate ascent (BCA) algorithm for efficient problem-solving. Moreover, the MCR2objective also serves the loss function of the feature encoding network, thus unifying the objectives of learning and communication. Simulation results demonstrate the superior performance of the proposed method compared to various baselines. As such, our work paves the way for further exploration into the synergistic alignment of learning and communication objectives in task-oriented communication systems. Code is available at https://github.com/chang-cai/TaskCommMCR2.

In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity.

To address limited server coverage in traditional over-the-air federated learning (OA-FL), we introduce the framework of multiple input multiple output (MIMO) cloud radio access network (Cloud-RAN) OA-FL (MIMOCROF). This framework involves a two-step model aggregation method in each training round. Firstly, each base station (BS) aggregates the local updates from its served devices, resulting in an edge update. Secondly, the cloud server (CS) aggregates the edge updates from the BSs. By modeling the second step as a lossy distributed source coding (L-DSC) process, we analyze the performance of MIMOCROF from the perspective of rate-distortion theory, resulting in a unified communication-learning design approach. In the proposed design, we jointly optimize rate resources and beamforming vectors, thereby leveraging the correlation inherent in FL to achieve performance gains. Numerical results show that, by solving the optimization problem, MIMOCROF performs comparably to the error-free bound and significantly outperforms other benchmark schemes.

Decentralized federated learning (DFL) is an emerging paradigm for leveraging the rapidly growing data from wireless devices in a fully distributed manner. However, the deployment of DFL is facing some pivotal challenges, including communication bottlenecks due to extensive inter-device message exchanges and the difficulty for edge devices to achieve consensus. To address these challenges, this paper proposes to employ the over-the-air computation (Aircomp) technique to improve communication efficiency and introduces a mixing matrix mechanism to guarantee consensus. Specifically, we present a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework for addressing the DFL design problem in general ad hoc networks. A rigorous convergence bound is derived to quantitatively capture the impact of mixing matrix and communication error on the system performance. The results show that the communication errors, the spectral gap of the mixing matrix, and the mixing matrix itself have a significant impact on the learning performance. Building on this result, we formulate a joint communication-learning optimization problem to optimize transceiver beamformers and mixing matrix. Numerical experiments demonstrate the substantial performance enhancement achieved by our proposed scheme.

Existing near-field localization algorithms generally face a scalability issue when the number of antennas at the sensor array goes large. To address this issue, this paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user that transmits signals. The user is considered to be in the near-field (Fresnel) region of the BS array. We propose a novel algorithm, named array partitioning based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the user can be approximated as in the far-field region of each subarray. The APLE algorithm determines the user's location by exploiting the differences in the angles of arrival (AoAs) of the sub arrays. Specifically, we establish a probability model of the received signal based on the geometric constraints of the user's location and the observed AoAs. Then, a message-passing algorithm, i.e., the proposed APLE algorithm, is designed for user localization. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to the existing methods. Besides, numerical results demonstrate that the proposed APLE algorithm outperforms the existing baselines in terms of localization accuracy.

In this paper, we consider the recovery of low-rank matrices from noisy observations using spectral denoisers, where the singular values are denoised through an identical scalar smoothing function. We explore the asymptotic mean squared error (AMSE) of these denoisers within a framework where the rank of the matrix to be recovered grows linearly with the matrix size. We demonstrate that, under arbitrary i.i.d. noise and some mild regularity assumptions, the AMSE converges in probability to a deterministic function of the noise power. Our results are applicable to commonly used denoisers, including the best-rank-r denoiser, the singular-value soft-threshold denoiser, and the singular-value hard-threshold denoiser. To the best of our knowledge, this is the first study to establish an analytical expression for the asymptotic MSE under arbitrary i.i.d. noise. The derived analytical expression depends solely on the empirical distribution of the singular values of the low-rank matrix and the specific form of the spectral denoiser employed.

Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine-type communications (mMTCs) for Internet of Things (IoT) applications, where the handshaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this article, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter-wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation, and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging a unitary approximate message passing-based matrix factorization (UAMP-MF) algorithm, we design a message passing-based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme.

Landslide hazards pose a great threat to the local residents and infrastructure in mountain areas. Numerous technologies have been invented to monitor landslides, and large amounts of high-resolution spatio-temporal data are consistently emerging. These data are highly related to the national security. The local governments release legislation to regulate the sharing of these data. However, the existing landslide detection models explicitly or implicitly assume that landslide monitoring and mapping data are directly shared in a centralized server. There is a gap between landslide detection models and landslide data sharing. To bridge this gap, this letter proposes a privacy-preserving machine learning method named federated learning-based landslide detection (FedLD) for landslide detection. First, horizontal federated learning (HFL) is introduced to protect the data privacy of the modeling process of landslide detection, enabling the development of landslide detection models without direct sharing of the original landslide monitoring data. Second, a new marginal contribution (MC) metric is proposed to measure the contribution of the participants of federated landslide detection models and is used to develop a model aggregation algorithm for federated landslide detection. Experimental results demonstrated that FedLD is able to protect the privacy of popular deep learning-based landslide detection models and achieves competitive landslide classification performance. Therefore, federated learning (FL) provides an effective solution for promoting data sharing in landslide detection.

In this letter, we propose a novel method to improve the localization accuracy for an array based satellite system, where we aim at minimizing the Cramér–Rao lower bound (CRLB) of the position by adjusting the attitude of the satellite. The Lie group method is adopted to provide an unconstrained representation of the rotation operation, which avoids using the conventional orthogonal matrix representation. Then the problem can be solved by gradient based methods. The proposed method has lower complexity than the traditional manifold method. Numerical simulations verify the effectiveness of our proposed approach.

Millimeter-wave (mmWave) is highly susceptible to obstacles and requires significant directivity of beams. To address these challenges, a promising solution is to deploy antenna arrays at base stations (BSs) and reconfigurable intelligent surfaces (RISs). Antenna arrays enable 3D beamforming and provide highly directional beams, while RISs create favorable transmission environments. In this paper, we propose an analytical framework to quantify the coverage performance of the RIS-assisted mmWave cellular network with 3D beamforming. Modeling obstacles and RISs with a line Boolean model and BSs with a Poisson point process (PPP), we provide the distance distribution between a UE and its associated RIS by evaluating the impact of the RIS orientation. Moreover, we adopt a 3D sectorized flat-top antenna model to characterize the interference introduced by dynamic directional beams (i.e., mainlobe/sidelobe). Furthermore, we derive the signal-to-interference ratio (SIR) coverage probability, where an equivalent PPP is proposed to enhance the computation efficiency. Numerical results validate the accuracy of our analysis and show that the proposed network achieves significant coverage improvement. We also investigate the impacts of the key parameters on the coverage probability, providing useful insights for deploying RISs and antenna arrays in mmWave cellular networks.

This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average achievable rate is formulated to jointly design the communication scheduling, the RIS’s phase shift, and the three-dimensional (3D) UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design significantly outperforms the existing schemes and provides new insights into the elevation angle and distance trade-off for the UAV-borne RIS communication system.

In task-oriented communications, most existing work designed the physical-layer communication modules and learning based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency between the design objectives may hinder the exploitation of the full benefits of task-oriented communications. In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i.e., feature encoding and classification) and communication (i.e., precoding) modules are designed with the same goal of inference accuracy maximization. Instead of end-to-end learning which involves both the task dataset and wireless channel during training, we advocate a separate design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem. We cast valuable insights into this formulation and develop a block coordinate ascent (BCA) algorithm for efficient problem-solving. Moreover, the MCR2 objective serves the loss function for feature encoding and guides the classification design. Simulation results on the synthetic features explain the mechanism of MCR2 precoding at different SNRs. We also validate on the CIFAR-10 and ModelNet10 datasets that the proposed design achieves a better latency-accuracy tradeoff compared to various baselines with inconsistent learning-communication objectives. As such, our work paves the way for further exploration into the synergistic alignment of learning and communication objectives in task-oriented communication systems.

In this paper, we investigate the problem of tracking the angles of arrival (AoAs) of multiple sources in millimeter wave (mmWave) systems with a limited number of radio frequency (RF) chains. Considering the time-varying nature of the channel, we propose a deep neural network (DNN)-based active learning scheme for adaptive analog beamforming and multi-source AoA tracking. The proposed scheme consists of a DNN-based beamformer and a subspace tracking-based multiple signal classification (MUSIC) estimator. Specifically, the DNN generates the beamformer using soft AoA estimates from the previous time block, and the MUSIC estimator exploits the measured signal by the beamformer to estimate the AoAs in the current time block. The proposed scheme is first applied to the uniform linear array (ULA) scenario, and then extended to the uniform rectangular array (URA) scenario. Particularly, in the URA scenario, to reduce the computational complexity, we modify the reduced-dimension MUSIC (RD-MUSIC) algorithm to a beam-space form. Furthermore, we adopt a partially connected analog beamforming scheme for the large-scale URA scenario to further reduce the hardware costs. We conduct numerical experiments to evaluate the tracking performance of the proposed scheme in the ULA and URA scenarios, and show that the proposed scheme significantly outperforms the existing codebook-based beamformer methods.

The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consumption model (GPECM) for rotary-wing UAVs under the consideration of stochastic wind modeling and 3D force analysis. Based on the GPECM, we study a UAV-enabled downlink communication system, where a rotary-wing UAV flies subject to stochastic wind disturbance and provides communication services for ground users (GUs). We aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing the 3D trajectory and user scheduling among the GUs based on the GPECM. We formulate the problem as a stochastic optimization, which is difficult to solve due to the lack of real-time wind information. To address this issue, we propose an offline-based online adaptive (OBOA) design that is comprised of two phases, namely, an offline phase and an online phase. In the offline phase, we average the wind effect on the UAV by leveraging stochastic programming (SP) based on wind statistics; then, in the online phase, we further optimize the instantaneous velocity to adapt to the real-time wind. Simulation results show that the optimized trajectories of the UAV in both phases can better adapt to changes of the speed and direction of the wind, resulting in a higher EE compared with the wind-unaware scheme. In particular, the proposed OBOA design can be applied in the scenario with dramatic wind changes, and allows the UAV to adjust its velocity dynamically to achieve better performance in terms of EE.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing problem. We propose a novel message-passing algorithm to solve the considered problem, and a progressive approximation method to reduce the computational complexity involved in the message passing. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters, hence mitigating the model mismatch problem. Finally, we analyze the efficacy of the proposed algorithm through the Bayesian Cramér-Rao bound. Numerical results demonstrate the feasibility of the proposed scheme and the superior performance of the proposed EM-based message-passing algorithm.

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner. With the cooperation of edge devices, DFL enables joint training of machine learning model in a device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchanges between neighbor devices to share the learned model. Besides, model consensus becomes increasingly difficult as the number of devices grows because there is no available central server for coordination. To overcome these difficulties, this paper proposes the use of over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveforms in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output (MIMO) over-the-air DFL (OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the impact of aggregation weights and communication error on the MIMO OA-DFL performance inad hocD2D networks. The result shows that the communication error together with the spectral gap of the mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize the transceiver beamformers and the mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm.

In this paper, we build up a new intelligent reflecting surface (IRS) aided multiple-input multiple-output (MIMO) channel model, named the cascaded LoS MIMO channel, that is applicable to both near-field and far-field scenarios. The proposed channel model consists of a transmitter (Tx) and a receiver (Rx) both equipped with uniform linear arrays (ULAs), and an IRS is used to enable communications between the transmitter and the receiver through the line-of-sight (LoS) links seen by the IRS. When modeling the reflection of electromagnetic waves at the IRS, we take into account the curvature of the wavefront on different reflecting elements. Based on the established channel model, we show that an IRS-assisted MIMO channel is able to support spatial multiplexing solely by the cascaded LoS links. We generalize the notion of Rayleigh distance originally coined for the single-hop MIMO channel to full multiplexing region (FMR) for the cascaded LoS MIMO channel, where the FMR is the union of all the Tx-IRS and IRS-Rx distance pairs that enable full multiplexing communication. We derive an inner bound of the FMR under a special passive beamforming (PB) strategy named reflective focusing, and provide the corresponding orientation settings of the antenna arrays that achieve full multiplexing.

In this paper, we study the performance limit of the cascaded line-of-sight (LoS) multiple-input-multiple-output (MIMO) system consisting of a transmitter (Tx) and a receiver (Rx) both equipped with uniform linear arrays (ULAs), and an intelligent reflecting surface (IRS) that enables communications between the Tx and the Rx through the cascaded LoS Tx-IRS-Rx link. We investigate the potential gain of the cascaded LoS MIMO system achieved by the optimization of the array orientations, especially when the Tx and the Rx are in the near-field of the IRS. Under a recently established near-field channel model, we formulate the problem of maximizing the input-output mutual information (MI) of the cascaded LoS MIMO system over active and passive beamforming as well as Tx and Rx array orientations. We give analytical solutions to the problem under asymptotic conditions, such as in the high/low signal-to-noise ratio (SNR) regime or with sufficiently large Tx-IRS and IRS-Rx distances. For non-asymptotic cases, we propose an alternating optimization method to solve the problem. We show that compared with only optimizing active and passive beamforming, the cascaded LoS MIMO system can harvest a significant MI gain from the additional optimization over the array orientations.

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

Integrated sensing and massive connectivity (MC) can potentially provide not only low-cost full-coverage sensing by utilizing the transmit signals of the huge number of devices in the wide areas, but also efficient user activity detection and channel estimation by utilizing the sensed environment/channel information. However, due to the huge number of users, burst traffic, and short packets, random and non-orthogonal multiple access is a natural choice for MC. In this case, efficient receiver design for integrated sensing and massive connectivity is clearly a challenging problem. In this paper, by introducing auxiliary variables properly, we reconstruct the probability model of the system, and then propose an iterative Bayesian receiver structure, consisting of four modules, namely multi-user decomposition (MUD), multipath decomposition (MPD), parameter fusion (PF), and communications and sensing output (CSO). These modules are designed based on message passing, variational inference, sum-product rules, and Bayesian inference, respectively, to compose a novel variational message passing receiver. Numerical results show that the proposed design can obtain high-accuracy positioning and sensing performance as well as improved performance of channel estimation and data communication.

The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. We show that the optimal number of multiplexed data streams to minimize the upper bound is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem of designing precoding and post-processing matrices to minimize the gradient aggregation error, and develop an efficient algorithm to solve the problem. The numerical results indicate the effectiveness of the proposed SCoM approach.

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost of model aggregation, with remarkable results. However, the following two questions remain unanswered: What are the performance limits of model aggregation? How much can FL convergence performance be improved by modifying existing model aggregation schemes? In this paper, we manage to answer these two questions. Specifically, for the first question, we put forth a general model aggregation performance analysis framework based on the rate-distortion theory. We then derive an inner bound of the rate-distortion region of model aggregation. For the second question, we develop an algorithm to search for the minimum achievable aggregation distortion, which, combined with the achievability scheme of the derived inner bound, induces a method to approximate the limits of FL convergence performance numerically. Numerical results demonstrate that the baseline model aggregation schemes still have great potential for further improvement.

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.

This paper investigates the potential of reconfigurable intelligent surface (RIS) for passive information transfer in a RIS-aided multiple-input multiple-output (MIMO) system. We propose a novel simultaneous active and passive information transfer (SAPIT) scheme, where the RIS information is embedded in reflected Tx signals. We introduce the coded modulation technique at the Tx and the RIS. The main challenge of the SAPIT scheme is to simultaneously detect the Tx signals and the RIS phase coefficients at the receiver. To solve this problem, we introduce appropriate auxiliary variables and develop a message-passing algorithm. We further analyze the fundamental performance limit of the proposed SAPIT-MIMO transceiver. Notably, we establish state evolution to predict the receiver performance in large-scale systems and analyze the achievable rates of the Tx and the RIS. Numerical results are provided to verify our analysis.

In this work, we consider a massive random access (RA) scenario, where massive single-antenna users access a base station (BS) equipped with a large number of antennas. If orthogonal RA protocols are employed, massive collisions will occur due to the limited number of orthogonal preambles given the preamble sequence length. To alleviate this problem, we propose an expectation-maximization-based delay-calibrated user activity detection algorithm, and investigate the benefits of oversampling for accurate delay estimation at the BS. The proposed algorithm alternately estimates the delay and detects active users by noting that the collided users have different transmission delays. The user activity detection problem can be formulated as a compressive sensing (CS) problem due to the sporadic activity patterns. We present the multiple measurement vector (MMV) version of Turbo-CS to solve this problem with considering the noise correlations due to oversampling. Moreover, a greedy search-based delay calibration method is proposed for the estimation of the transmission delay. Numerical results demonstrate the superior performance of the proposed algorithm in terms of the probability of misdetection and the normalized mean square error of time delay.

Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing. In this paper, we study a challenging multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple RISs. In particular, we assume that a multi-antenna base station (BS) receives the OFDM symbols from single-antenna users reflected by multiple RISs and tracks the positions of these users. Considering the users’ mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition model to characterize the tracking process, where the geometric constraints between channel parameters and multiuser positions are utilized. We further develop an online message passing algorithm, termed the Bayesian multiuser tracking (BMT) algorithm, to estimate the multiuser positions, the angles-of-arrivals (AoAs) at multiple RISs, and the time delay and the blockage of the LoS path. The Bayesian Cramér Rao bound (BCRB) is derived as the fundamental performance limit of the considered tracking problem. Based on the BCRB, we optimize the passive beamforming (PBF) of the multiple RISs to improve the tracking performance. Simulation results show that the proposed PBF design significantly outperforms the counterpart schemes, and our BMT algorithm can achieve up to centimeter-level tracking accuracy.

To exploit the potential of the reconfigurable intelligent surface (RIS) in supporting integrated sensing and communication (ISAC), this paper proposes a novel joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size. First, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the target detection capability. Then, an optimization problem is formulated to maximize the signal-to-noise ratio (SNR) at the user-equipment (UE) under a minimum detection probability constraint. To solve this non-convex problem, a novel alternative optimization approach is developed. In this approach, the solutions of the communication and sensing beamformers are obtained by our proposed bisection-search based method. The optimal receive combining vector is derived from an equivalent Rayleigh-quotient problem. To optimize the RIS phase shifts, the Charnes-Cooper transformation is conducted to cope with the fractional objective, and a novel convexification process is proposed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor expansion. After the convexification, a successive convex approximation (SCA) based algorithm is designed to yield a suboptimal phase-shift solution. Finally, the overall optimization algorithm is built, followed by detailed analyses on its computational complexity, convergence behavior and problem feasibility condition. Extensive simulations are carried out to testify the analytical properties of the proposed beamforming design, and to reveal two important trade-offs, namely, communication vs. sensing trade-off and UDR vs. sensing-duration trade-off. In comparison with several existing benchmarks, our proposed approach is validated to be superior when detecting targets with practical sizes.

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning (ML) paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost for model aggregation. However, the following question remains unanswered: What is the fundamental trade-off between the communication cost and the FL convergence performance? In this paper, we manage to answer this question. Specifically, we first put forth a general framework for model aggregation performance analysis based on the rate-distortion theory. Under the proposed analysis framework, we derive an inner bound of the rate-distortion region of model aggregation. We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance. We formulate an aggregation distortion minimization problem to improve the FL convergence performance. Two algorithms are developed to solve the above problem. Numerical results on aggregation distortion, convergence performance, and communication cost demonstrate that the baseline model aggregation schemes still have great potential for further improvement.

This paper studies the user activity detection and channel estimation problem in a temporally-correlated massive access system where a very large number of users communicate with a base station sporadically and each user once activated can transmit with a large probability over multiple consecutive frames. We formulate the problem as a dynamic compressed sensing (DCS) problem to exploit both the sparsity and the temporal correlation of user activity. By leveraging the hybrid generalized approximate message passing (HyGAMP) framework, we design a computationally efficient algorithm, HyGAMP-DCS, to solve this problem. In contrast to only exploiting the historical estimations, the proposed algorithm performs bidirectional message passing between the neighboring frames for activity likelihood update to fully exploit the temporally-correlated user activities. Furthermore, we develop an expectation maximization HyGAMP-DCS (EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the estimation procedure when the system statistics are unknown. In particular, we propose to utilize the analysis tool of state evolution to find the appropriate hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate that our proposed algorithms can significantly improve the user activity detection accuracy and reduce the channel estimation error.

In next-generation wireless networks, reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems are foreseeable to support a large number of antennas at the transceiver as well as a large number of reflecting elements at the RIS. To fully unleash the potential of RIS, the phase shifts of RIS elements should be carefully designed, resulting in a high-dimensional non-convex optimization problem that is hard to solve with affordable computational complexity. In this paper, we address this scalability issue by partitioning RIS into sub-surfaces, so as to optimize the phase shifts in sub-surface levels to reduce complexity. Specifically, each sub-surface employs a linear phase variation structure to anomalously reflect the incident signal to a desired direction, and the sizes of sub-surfaces can be adaptively adjusted according to channel conditions. We formulate the achievable rate maximization problem by jointly optimizing the transmit covariance matrix and the RIS phase shifts. Under the RIS partitioning framework, the RIS phase shifts optimization reduces to the manipulation of the sub-surface sizes, the phase gradients of sub-surfaces, as well as the common phase shifts of sub-surfaces. Then, we characterize the asymptotic behavior of the system with an infinitely large number of transceiver antennas and RIS elements. The asymptotic analysis provides useful insights on the understanding of the fundamental performance-complexity tradeoff in RIS partitioning design. We show that in the asymptotic domain, the achievable rate maximization problem has a rather simple form with an explicit physical meaning of optimization variables. We develop an efficient algorithm to find an approximately optimal solution to the asymptotic problem via a one-dimensional (1D) grid search. Moreover, we discuss the insights and impacts of the asymptotic result on finite-size system design. By applying the asymptotic result to a finite-size system with necessary modifications, we show by numerical results that the proposed design achieves a favorable tradeoff between system performance and computational complexity.

In this paper, we consider asynchronous massive connectivity, where massive low-power and low-rate devices with sporadic activity patterns connect to a multi-antenna access point (AP) in an asynchronous manner. Asynchronous transmission minimizes the amount of coordination between the devices and the AP, and thereby simplifies the transmitter design, yet at the cost of a more challenging receiver design. Specifically, asynchronous transmission results in inter-symbol interference (ISI) since the sampling at AP generally cannot match with the symbol intervals of uncoordinated devices. To enable reliable reception, we develop a turbo approximate message passing (TAMP) algorithm that consists of a channel-signal decomposition (CSD) module and a delay learning (DL) module. The CSD carries out sparse matrix factorization to estimate the channels and the ISI corrupted signals of active devices, and the DL is designed to estimate the delay of each active user and resolve the corresponding ISI based on the Bayesian principle. To refine the delay estimation, we further divide the DL module into symbol-level delay learning (SDL) and sub-symbol-level delay learning (sub-SDL) submodules. In particular, the sub-SDL estimates the residue delays (obtained by taking modulo of the symbol interval) and then finely compensates the ISI. Due to the continuity and randomness of time delay, the receive signal constellation consists of lines and curves instead of discrete points, even if the transmit signal constellation is discrete. To reduce the complexity of soft demodulation, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed TAMP algorithm. Particularly, the TAMP algorithm is able to approach the single-user bound with known user delay.

To achieve communication-efficient federated multi-task learning (FMTL), we propose an over-the-air FMTL (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). To overcome the inter-task interference inherent in the non-orthogonal transmission among tasks, we design a novel transmission method called model sparsification and random compression (MSRC) as well as a reception method called modified turbo compressed sensing (M-Turbo-CS). More specifically, at each edge device, the local model updates of all tasks are first sparsified andrandomlycompressed with different random compression matrices for different tasks, before being superimposed and sent over the uplink channel. Then the ES constructes the model aggregations of all the tasks from the channel observation data through a modified version of the turbo compressed sensing (Turbo-CS) algorithm called M-Turbo-CS. We analyze the performance of the proposed OA-FMTL framework with MSRC and M-Turbo-CS. Based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by adjusting the power allocation among the tasks at the edge devices. Numerical simulations show that our proposed OA-FMTL efficiently suppresses the inter-task interference to achieve a learning performance comparable to the inter-task interference free bound at a significantly reduced communication overhead. It is also shown that the proposed inter-task power allocation optimization algorithm further reduces the overall communication overhead by appropriately adjusting the power allocation among the tasks.

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.

In this paper, we consider a reconfigurable intelligent surface (RIS) aided uplink multiuser multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, where the receiver is assumed to conduct low-complexity iterative detection. We aim to minimize the total transmit power by jointly designing the precoder of the transmitter and the passive beamforming of the RIS. This problem can be tackled from the perspective of information theory. But this information-theoretic approach may involve prohibitively high complexity since the number of rate constraints that specify the capacity region of the uplink multiuser channel is exponential in the number of users. To avoid this difficulty, we formulate the design problem of the iterative receiver under the constraints of a maximal iteration number and target bit error rates of users. To tackle this challenging problem, we propose a groupwise successive interference cancellation (SIC) optimization approach, where the signals of users are decoded and canceled in a group-by-group manner. We present a heuristic user grouping strategy, and resort to the alternating optimization technique to iteratively solve the precoding and passive beamforming sub-problems. Specifically, for the precoding sub-problem, we employ fractional programming to convert it to a convex problem; for the passive beamforming sub-problem, we adopt successive convex approximation to deal with the unit-modulus constraints of the RIS. We show that the proposed groupwise SIC approach has significant advantages in both performance and computational complexity, as compared with the counterpart approaches.

With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme.

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bayesian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

In reconfigurable intelligent surface (RIS) aided communications, how to optimize a large number of RIS reflecting elements in a scalable and efficient manner remains an open challenge. This paper addresses the scalability issue by partitioning RIS into sub-surfaces, so as to optimize the phase shifts in sub-surface levels to reduce complexity. In the proposed design, each sub-surface employs a linear phase variation structure to anomalously reflect the incident signal to a desired direction. We formulate the achievable rate maximization problem by jointly optimizing the transmit covariance matrix and the structured RIS phase shifts. Then, we characterize the asymptotic behavior of the system with an infinitely large number of transceiver antennas and reflecting elements. We develop an efficient algorithm to find the optimal solution to the asymptotic problem via one-dimensional (1D) search. By applying the asymptotic result to a finite-size system with necessary modifications, we show by numerical results that the proposed design achieves a favorable tradeoff between system performance and computational complexity.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing (CS) problem. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters to mitigate the model mismatch problem. Finally, we analyze the efficacy of various CS algorithms through the Bayesian Cramér-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.

With huge amounts of data explosively increasing on the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issue will significantly reduce the learning performance. In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL system, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. Under this UAV-assisted hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL performance analysis. We then formulate a mean squared error (MSE) minimization problem to tune the UAV trajectory and the global aggregation coefficients based on the analysis results. An algorithm based on alternating optimization (AO) and successive convex approximation (SCA) is developed to solve the formulated problem. Simulation results demonstrate the great potential of our UAV-assisted hierarchical aggregation scheme.

In this paper, we develop an asynchronous massive access framework. Besides the multiple-access interference issue, asynchronous transmission induces inter-symbol interference effect of the same user that is determined by the unknown user delay. To solve this problem, we propose an Bayesian receiver where the whole receiver is divided into two modules: the channel-signal decomposition (CSD) module and the delay learning (DL) module. The CSD module demixes the transmit signals of different users by leveraging the bilinear generalized approximate message passing (BiGAMP) algorithm, and the DL module is designed to estimate the time delay of each user based on the Bayesian principle. Additionally, due to the continuity of time delay, the constellation of all possible received user signals consists of lines and curves instead of discrete points, even if the original transmit signals are discrete. To reduce complexity, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed scheme. Particularly, the proposed scheme is able to approach the single-user interference-free bound with known user delay.

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.

In this paper, we consider the cascaded line-of-sight (LoS) multiple-input-multiple-output (MIMO) system, in which an intelligent reflecting surface (IRS) is employed to enable the communication between a multi-antenna transmitter (Tx) and a multi-antenna receiver (Rx) by connecting the line-of-sight (LoS) links seen by the IRS. We formulate an optimization problem to maximize the input-output mutual information (MI) of the system over the passive beamforming and the Tx/Rx array orientations. Analytical solutions to this problem are provided under asymptotic conditions, such as high or low signal-to-noise ratio (SNR) regimes or sufficiently large Tx-IRS and IRS-Rx distances. For general cases, we propose an alternating optimization method to solve the problem. Numerical results validate the effectiveness of the proposed optimization method and our analyses.

In this paper, we build up a new intelligent reflecting surface (IRS) aided multiple-input multiple-output (MIMO) channel model, named the cascaded line-of-sight (LoS) MIMO channel. The proposed channel model consists of a transmitter (Tx) and a receiver (Rx) both equipped with uniform linear arrays (ULAs), and an IRS used to enable communications between the Tx and the Rx through the LoS links seen by the IRS. To model the reflection of electromagnetic waves at the IRS, we take into account the curvature of the wavefront on different reflecting elements (REs), which is distinct from most existing works with the plane-wave assumption. Based on the established model, we study the spatial multiplexing capability of the cascaded LoS MIMO system. We come up with the notion of full multiplexing region (FMR), which is the union of Tx-IRS and IRS-Rx distance pairs that enable full multiplexing communication over the cascaded LoS MIMO system. We also derive an inner bound of the FMR under a specific passive beamforming strategy named reflective focusing.

Although Federated learning (FL) over wireless medium is a promising technology, a large number of concurrent FL tasks, generated by the urgent demand for ubiquitous intelligence, may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in AirComp inevitably brings about the problem of inter-task interference, which poses a new challenge. In this paper, we study over-the-air multi-task FL (OA-MTFL) over the multiple-input multiple-output (MIMO) interference channel. We establish a communication-learning analytical framework for the proposed OA-MTFL scheme by considering the spatial correlation between devices, formulate an optimization problem of designing transceiver beamforming and device selection, and develop an efficient algorithm to solve it. The numerical results demonstrate the outstanding performance of the proposed scheme.

Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid shape prior. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls. To this end, we first employ Convolutional Neural Networks to detect planes and vertical lines between adjacent walls. Meanwhile, estimating the 3D parameters for each plane. Then, a simple yet effective geometric reasoning method is adopted to achieve room layout reconstruction. Furthermore, we optimize the 3D plane parameters to reconstruct a geometrically consistent room layout between planes and lines. The experimental results on public datasets validate the effectiveness and efficiency of our method.

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

In this paper, we study how to efficiently and reliably detect active devices and estimate their channels in a multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) based grant-free non-orthogonal multiple access system to enable massive machine-type communication (mMTC). First, by exploiting the correlation of the channel frequency responses across the OFDM subcarriers, we propose a block-wise linear channel model. Specifically, the continuous OFDM subcarriers are divided into several sub-blocks and a linear function with only two variables (mean and slope) is used to approximate the frequency-selective channel in each sub-block. This significantly reduces the number of variables to be determined in channel estimation, and the sub-block number can be adjusted to reliably compensate the channel frequency-selectivity. Second, we formulate the joint active device detection and channel estimation in the block-wise linear system as a Bayesian inference problem. By exploiting the block-sparsity of the channel matrix, we propose an efficient turbo message passing algorithm to solve the Bayesian inference problem. We then develop the state evolution to predict the performance of the turbo message passing algorithm. We further incorporate machine learning approaches into turbo message passing to learn unknown model parameters. Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.

Reconfigurable intelligent surface (RIS) based reflection modulation (RM) has been considered as a promising information delivery mechanism, and has the potential to realize passive information transfer of a RIS without consuming any additional radio frequency chain and time/frequency/energy resource. The existing on-off RM (ORM) schemes are based on manipulating the “on/off” states of RIS reflection elements, which may lead to the degradation of RIS reflection efficiency. This paper proposes a frequency RM (FRM) method for RIS-aided OFDM systems. The FRM-OFDM scheme modulates the frequency of the incident electromagnetic waves, and the RIS information is embedded in the frequency-hopping states of RIS elements. Unlike the ORM-OFDM scheme, the FRM-OFDM scheme can achieve higher reflection efficiency, since the latter does not turn off any reflection element in RM. We show that, for the RIS phase shift optimization, the multiplicative multiple access channel in the FRM-OFDM system can be converted to an equivalent RIS-aided multiple-input multiple-output channel. Then, we propose an alternating optimization AO) algorithm for sum rate maximization of the FRM-OFDM system. A low-complexity recursive AO algorithm is further developed to avoid direct channel matrix inversion in the AO algorithm with negligible performance degradation. In addition, we design a bilinear message passing (BMP) algorithm for the bilinear recovery of both the user symbols and the RIS data. Numerical simulations verify the efficiency of the designed optimization algorithms for system optimization and the BMP algorithm for signal detection, as well as the superiority of the proposed FRM-OFDM scheme over the existing ORM-OFDM scheme and the RIS-aided OFDM system.

This paper proposes a mobile unmanned aerial vehicle (UAV) jamming scheme under the probabilistic line-ofsight channel model (PLCM) to improve the secrecy of ground wiretap channels, in which a friendly UAV is deployed to cooperatively transmit jamming signals to confuse the suspicious eavesdropper. Our goal is to maximize the average (expected) secrecy rate by jointly optimizing the source transmit power, UAV jamming power and trajectory for a given flight time. Since the expected secrecy rate is highly complicated with respect to the UAV trajectory, we derive a more tractable lower bound for it. Nevertheless, the resulting optimization problem remains a non-convex problem, which is difficult to solve optimally. Therefore, we propose an efficient iterative algorithm to obtain a suboptimal solution to it by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Simulation results show that the joint power and trajectory optimization scheme under the PLCM significantly outperforms various benchmark schemes.

Symbiotic radio (SR) emerges as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi-backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time and frequency resource block. However, the receiver design becomes much more complicated due to the presence of inter-user and inter-BD interference. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Specifically, motivated by the idea of approximate message passing, we develop a computationally efficient iterative algorithm under the Bayesian inference framework to resolve the joint estimation problem. Simulation results demonstrate the effectiveness of the proposed receiver design.

This paper studies semi-blind channel estimation for a reconfigurable intelligent surface (RIS) aided uplink massive multiple-input multiple-output (MIMO) system, in which the base station simultaneously estimates the channel coefficients and detects the partially unknown transmit symbols. We formulate the semi-blind channel estimation task as a trilinear inference problem. Based on the approximate message passing (AMP) principle, we develop a computationally efficient approach, called Trilinear AMP, to calculate the marginal posterior mean estimators of the trilinear inference problem. Simulation results demonstrate the effectiveness of the proposed Trilinear AMP approach.

In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of the model aggregation series. With this temporal probability model, we formulate the model aggregation problem as to infer the desired aggregated update given all the past observations from a Bayesian perspective. We develop a message passing based algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task with low complexity and near-optimal performance. We further establish the state evolution (SE) analysis to characterize the behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the expected loss reduction of the FEEL system under certain standard regularity conditions. In addition, we develop an expectation maximization (EM) strategy to learn the unknown parameters in the Markovian model. We show that the proposed TSA-GA significantly outperforms the state-of-the-art analog compression scheme, and is able to achieve comparable learning performance as the error-free benchmark in terms of final test accuracy.

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted two-way relay network, in which two users exchange information through the base station (BS) with the help of an RIS. By jointly designing the phase shifts at the RIS and beamforming matrix at the BS, our objective is to maximize the minimum signal-to-noise ratio (SNR) of the two users, under the transmit power constraint at the BS. We first consider the single-antenna BS case, and propose two algorithms to design the RIS phase shifts and the BS power amplification parameter, namely the SNR-upper-bound-maximization (SUM) method, and genetic-SNR-maximization (GSM) method. When there are multiple antennas at the BS, the optimization problem can be approximately addressed by successively solving two decoupled subproblems, one to optimize the RIS phase shifts, the other to optimize the BS beamforming matrix. The first subproblem can be solved by using SUM or GSM method, while the second subproblem can be solved by using optimized beamforming or maximum-ratio-beamforming method. The proposed algorithms have been verified through numerical results with computational complexity analysis.

In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction.

This paper investigates a novel unmanned aerial vehicles (UAVs) secure communication system with the assistance of reconfigurable intelligent surfaces (RISs), where a UAV and a ground user communicate with each other, while an eavesdropper tends to wiretap their information. Due to the limited capacity of UAVs, an RIS is applied to further improve the quality of the secure communication. The time division multiple access (TDMA) protocol is applied for the communications between the UAV and the ground user, namely, the downlink (DL) and the uplink (UL) communications. In particular, the channel state information (CSI) of the eavesdropping channels is assumed to be imperfect. We aim to maximize the average worst-case secrecy rate by the robust joint design of the UAV’s trajectory, RIS’s passive beamforming, and transmit power of the legitimate transmitters. However, it is challenging to solve the joint UL/DL optimization problem due to its non-convexity. Therefore, we develop an efficient algorithm based on the alternating optimization (AO) technique. Specifically, the formulated problem is divided into three sub-problems, and the successive convex approximation (SCA),$\mathcal {S}$-Procedure, and semidefinite relaxation (SDR) are applied to tackle these non-convex sub-problems. Numerical results demonstrate that the proposed algorithm can considerably improve the average secrecy rate compared with the benchmark algorithms, and also confirm the robustness of the proposed algorithm.

To exploit massive amounts of data generated at mobile edge networks,federated learning(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high communication latency and privacy issues as compared to centralized ML. To improve the communication efficiency in FL model aggregation,over-the-air computationhas been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model communication. In this paper, we leverage thereconfigurable intelligent surface(RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from O(N2) to O(1), which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users.

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, an intelligent reflecting surface (IRS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station (BS) and power control at the D2D transmitter are adapted to instantaneous effective channel state information (CSI); and the IRS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic rate subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for the CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively with closed-form expressions. Numerical results verify that the two-timescale based optimization performs better than several baselines, and also demonstrate a favorable trade-off between system performance and CSI overhead.

Reconfigurable Intelligent Surface (RIS) is a new and promising technique to solve the energy-efficiency, spectral-efficiency and hardware-cost problem faced by beyond-5G wireless networks. In this paper, we consider a RIS-assisted two-way relay network in which two users exchange information via the base station (BS) with the help of a RIS. By jointly designing the beamforming matrix at the BS and the phase shifts introduced by the RIS, the minimum SNR of the two users is maximized, under the transmit power constraint at the BS. The formulated problem is non-convex and difficult to solve in general. To start with, we first study the single BS antenna case. The design problem is reduced to the problem of how to choose the phase shifts for the RIS, in addition to optimizing the BS power amplification parameter. A Channel-Gain-Maximization (CGM) algorithm is proposed to solve the problem. For the multiple BS antenna case, we decouple the phase shifts and the beamforming matrix by taking an upper bound of the SNR. The way to obtain the RIS phase shifts is similar to the single BS antenna case while the beamforming matrix is obtained by utilizing an existing solution. A Channel-Gain-Maximization Maximal-Ratio-Beamforming (CGM-MRB) algorithm is thus developed. Finally, numerical results are presented to show the effectiveness of the proposed algorithms.

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks. In this paper, we study a RIS aided multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, where the transmitter is equipped with a constant-envelope analog beamformer. We formulate a novel problem to maximize the total received power of all the users by jointly optimizing the beamformer at transmitter and the phase shifts at the RISs, subject to the individual minimum received power constraints of users. We further solve the problem iteratively with a closed-form expression for each step. Numerical results show the performance gain of deploying RIS and the effectiveness of the proposed algorithm.

This paper investigates the passive beamforming and information transfer (PBIT) technique for the large intelligent surface (LIS) aided multiuser multiple-input multiple-output (MuMIMO) systems, where the LIS is deployed to enhance the primary communication via passive beamforming and simultaneously deliver its private data via spatial modulation. For the passive beamforming design, we propose to maximize the sum channel capacity of the LIS-aided Mu-MIMO systems and formulate the maximization problem as a two-step stochastic program. An efficient sample average approximation based iterative algorithm is developed for the passive beamforming design. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the LIS data are multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this problem, we develop a turbo message passing algorithm in which the bilinear estimation problem is divided into two subproblems: one for the estimation of the user signals and the other for the estimation of the LIS data. Extensive simulation results are provided to demonstrate the advantages of our passive beamforming and receiver designs.

This paper considers massive connectivity in asynchronous systems, where a large number of devices sporadically send data to the base station (BS) with imperfect synchronization. Grant-free random access is considered and each device is assigned with a unique but not necessarily orthogonal pilot sequence for identification and channel estimation. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. We first adopt a transmission model where a guard interval is inserted between pilot and data in order to eliminate the potential cross pilot-data interference between asynchronous devices. By exploiting the feature of the asynchronous massive connectivity, we formulate a sparse signal recovery problem with hierarchical sparsity on the user activity and the time delays. We propose the Learned approximate message passing (LAMP) network that combines deep learning in the AMP framework to solve the problem. This neural network benefits from parameter learning ability of deep learning and low computation complexity of the AMP algorithm. Simulation results demonstrate that the LAMP network can perform much better than the AMP algorithm with no prior knowledge of the system statistics. Its performance is also insensitive to the maximal delay spread of the asynchronous users.

In this paper, we study the channel acquisition problem in a reconfigurable intelligent surface (RIS) assisted multiuser multiple-input multiple-output (MIMO) system, where an RIS with fully passive phase-shift elements is deployed to assist the MIMO communication. The state-of-the-art channel acquisition approach in such a system estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels.

In the overlay D2D networks, multiple D2D pairs coexist with full frequency reuse resulting in complicated interference. Traditional centralized power control methods require instantaneous interference information and so are difficult to implement in a D2D network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core idea of the scheme is described as follows. First, we design linear filters with various sizes termed IFEs to extract the local interference patterns from the outdated interference information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, and so as to provide more effective power allocation strategies. Then, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network (RNN). The acquisition of the channel correlation can greatly improve the efficiency and the accuracy of our network according to our simulation results. It is worth noting that an input reduction process is also designed to reduce the space complexity from O(N2) to O(1) which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in a RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.

This paper investigates the passive beamforming and information transfer (PBIT) technique for multiuser multiple-input multiple-output (Mu-MIMO) systems with the aid of reconfigurable intelligent surfaces (RISs), where the RISs enhance the primary communication via passive beamforming (P-BF) and at the same time deliver additional information by the on-off reflecting modulation (in which the RIS information is carried by the on/off state of each reflecting element). For the P-BF design, we propose to maximize the achievable user sum rate of the RIS-aided Mu-MIMO channel and formulate the problem as a two-step stochastic program. A sample average approximation (SAA) based iterative algorithm is developed for the efficient P-BF design of the considered scheme. To strike a balance between complexity and performance, we further propose a simplified P-BF algorithm by approximating the stochastic program as a deterministic alternating optimization problem. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the RIS information is multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this bilinear estimation problem, we develop a turbo message passing (TMP) algorithm in which the factor graph associated with the problem is divided into two modules: one for the estimation of the user signals and the other for the estimation of the on-off state of each RIS element. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the Mu-MIMO PBIT scheme from single-RIS to multi-RIS, by leveraging the similarity between the single-RIS and multi-RIS system models. Extensive simulation results are provided to demonstrate the advantages of our P-BF and receiver designs.

In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime.

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.

In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design. Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.

Massive machine-type communication is one of the most important scenarios for future wireless communications. Due to its huge number of potential users and its bursty packet arrivals, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides a possible solution, and at the same time poses a challenge for efficient receiver design. In this paper, we propose a joint user identification, channel estimation, and signal detection (JUICESD) scheme based on message passing principles to solve the problem. By introducing two types of auxiliary variables, we divide the whole iterative receiver into two modules: one is a linear module leveraging the existing approximate message passing (AMP) algorithm for its low complexity and asymptotic optimality; the other is a non-linear module decoupled for different users. We further discuss the message passing in the non-linear module and between the two modules. Instead of using the conventional Gaussian approximation, we propose to use a structured Gaussian mixture approximation in message updates. With these ideas, an efficient iterative algorithm is developed and analyzed. Numerical results show that the proposed scheme achieves a significant performance improvement over the existing alternatives. Moreover, the complexity of our scheme is linear with the number of users, which is especially suitable for machine type communication with massive devices.

In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) massive MIMO downlink systems, where instantaneous channel state information (CSI) is unavailable at the base station (BS). We propose to extract the spatial information (i.e., the angle parameters and the large-scale fading coefficients) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is provided. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. By maximizing the resultant sum-rate, we develop a low-complexity beam selection scheme. Numerical results demonstrate that our proposed algorithm has a significant improvement compared to the existing statistical beamforming (SBF) approaches.

Fog computing (FC) has the potential to enable computation-intensive applications for the next generation wireless networks. In parallel with the development of FC, nonorthogonal multiple access (NOMA) has been recognized as a promising solution to improve the spectrum efficiency. In this paper, a NOMA-based FC system is considered, where multiple task nodes perform task scheduling via NOMA to a helper node, the helper node with abundant computation resource is required to compute the computation task from the task nodes. We formulate a joint task scheduling, computational resource allocation, and power allocation problem with an objective to minimize the sum cost (i.e., delay and energy consumptions for all task nodes) realizing energy-delay tradeoff. It is challenging to obtain an optimal policy for such a combinatorial optimization problem. To this end, we propose an online learning-based optimization framework to tackle this problem. Simulation results show that the proposed scheme significantly reduces the sum cost compared to the baselines.

In this paper, we study blind signal detection by exploiting the hidden sparsity of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the channel sparsity by representing the angular-domain channel with a uniform angle-sampling grid. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the energy leakage problem. In contrast to this approach, we deploy a sparse channel representation with a fixed general sampling grid. Based on that, we formulate the blind signal detection problem as an affine matrix factorization task and develop a novel message passing algorithm to estimate the channel and the user signals simultaneously. Unlike the existing approach, the proposed algorithm is applicable to general antenna arrays. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach by avoiding the leakage of energy.

This paper considers the transceiver design for uplink massive multi-input multi-output (MIMO) systems with channel sparsity. Recent progress has shown that sparsity learning-based blind signal detection is able to retrieve the channel and data by using message passing based noisy matrix factorization. We propose a semi-blind signal detection scheme in which a short pilot sequence is introduced to each user packet and the knowledge of pilots is integrated into the message passing algorithm for noisy matrix factorization. We show that our semi-blind signal detection scheme substantially outperforms the state-of-the-art blind detection and training-based schemes in the short-pilot regime.

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iterative processing structure, which consists of a linear estimator, a demodulation-and-decoding module, and a clustering module. In the clustering module, we employ modified expectation-maximization (EM)-based algorithms to estimate the interference under the knowledge of the modulation constraint (MC) of the interference. We show that this modified EM algorithm-based receiver outperforms the original EM-based receiver, since the former is able to generate a more accurate clustering result by reducing the dimension of the parameter space. We further improve the performance of the iterative receiver by introducing the extrinsic information technique, with the resulting algorithm referred to as the extrinsic modulation constrained EM (Ext-MC-EM) algorithm. We show that the Ext-MC-EM algorithm-based receiver considerably outperforms the counterpart iterative receivers, including the MC-EM algorithm.

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.

In this paper, we study time-varying orthogonal frequency division multiplexing (OFDM) systems, and propose a joint channel-and-signal estimation receiver based on turbo message passing (TMP) to efficiently suppress inter-carrier interference (ICI). We establish a factor graph representation of the problem and divide the whole factor graph into two parts, one for channel estimation and the other for signal detection. For the first part, we use Gaussian message passing (GMP) for channel estimation; for the second part, a discrete state space (DSS) model is employed to describe the transition of signal states, and a forward-backward algorithm is adopted for message passing over the transition trellis in signal detection. The resulting algorithm is referred to as DSS-GMP. The complexity of DSS-GMP quickly becomes the bottleneck as the increase of the signal constellation size and the ICI width. To address this issue, we further develop a continuous-state-space (CSS) model based turbo message passing algorithm, where the messages of modulated signals are approximated as continuous Gaussian messages. Numerical results demonstrate that the TMP based scheme significantly outperforms the state-of-the-art schemes.

Compute-and-forward (CF) harnesses interference in wireless communications by exploiting structured coding. The key idea of CF is to compute integer combinations of code words from multiple source nodes, rather than to decode individual code words by treating others as noise. Compute-compress-and-forward (CCF) can further enhance the network performance by introducing compression operations at receivers. In this paper, we develop a more general compression framework, termed generalized CCF (GCCF), where the compression function involves the selection of message segments over finite fields. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian-Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. We formulate a sum-rate maximization problem and develop an approximate algorithm to solve the problem. Numerical results are presented to demonstrate the performance superiority of GCCF over CCF and other schemes.

This paper studies the transceiver design of the Gaussian two-pair two-way relay channel (TWRC), where two pairs of users exchange information through a common relay in a pairwise manner. Our main contribution is to show that the capacity of the Gaussian two-pair TWRC is achievable to within$\frac{1}{ 2}$bit for arbitrary channel conditions. For the outer bound, we derive a genie-aided bound of the Gaussian two-pair TWRC, which is tighter than the cut-set bound. For the inner bound, we develop a hybrid coding scheme involving Gaussian random coding, nested lattice coding, superposition coding, and network-coded decoding. We further present a message-reassembling strategy to decouple the coding design for the user-to-relay and relay-to-user links, so as to provide flexibility to fully exploit the channel randomness. We show that judicious power allocation at the users and at the relay is necessary to approach the channel capacity under various channel conditions.

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

Banded linear systems arise in many communication scenarios, e.g., those involving inter-carrier interference and inter-symbol interference. Motivated by recent advances in deep learning, we propose to design a high-accuracy low-complexity signal detector for banded linear systems based on convolutional neural networks (CNNs). We develop a novel CNN-based detector by utilizing the banded structure of the channel matrix. Specifically, the proposed CNN-based detector consists of three modules: the input preprocessing module; the CNN module; and the output postprocessing module. With such an architecture, the proposed CNN-based detector is adaptive to different system sizes, and can overcome the curse of dimensionality, which is a ubiquitous challenge in deep learning. Through extensive numerical experiments, we demonstrate that the proposed CNN-based detector outperforms conventional deep neural networks and existing model-based detectors in both accuracy and computational time. Moreover, we show that the CNN is flexible for systems with large sizes or wide bands. We also show that the proposed CNN-based detector can be easily extended to near-banded systems such as doubly selective orthogonal frequency division multiplexing (OFDM) systems and 2-D magnetic recording (TDMR) systems, in which the channel matrices do not have a strictly banded structure.

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.

Linear minimum mean square error (LMMSE) estimation based turbo detection has been extensively studied for coded linear systems since the seminal work of Wang and Poor (WP). The WP algorithm operates iteratively between a linear detector (LD) and a nonlinear detector (NLD): the LD suppresses the interference based on LMMSE filtering, and the NLD decodes the data by treating the output of the LD as an observation from an additive white Gaussian noise (AWGN) channel. In WP, the messages exchanged between LD and NLD are required to beextrinsic. For the NLD, the extrinsic message comes from the constraint imposed on feedforward error correction (FEC) codes. Therefore, WP does not work in an un-coded linear system. Recently, we proposed an orthogonal approximate message passing (OAMP) algorithm, which only requires the input/output error terms of LD and NLD to beorthogonal. We conjectured that for un-coded linear systems that involve certain large random matrices, the dynamics of OAMP can be accurately characterized by state evolution (SE). In this paper, we consider a coded linear system and develop an extrinsic message aided OAMP (EMA-OAMP) algorithm. Similar to the un-coded case, EMA-OAMP relaxes the requirements on output messages to be orthogonal instead of extrinsic. We derive an SE procedure to characterize the performance of OAMP in coded systems. We conjecture that this SE procedure is accurate, which is verified by simulation results. Under this conjecture, we show that EMA-OAMP can outperform WP under certain standard assumptions for iterative decoding. Extensive simulations results are provided to verify the advantages of OAMP in coded MIMO systems.

In this work, we study the non-time-slotted massive-device multiple access (MaDMA) problem where massive user devices transmit sporadic data to a multi-antenna base station (BS). We develop a structured sparsity learning based multiuser detection (SSL-MUD) scheme. By exploiting the structured sparsity naturally embedded in user signals, our SSL-MUD scheme is able to blindly detect the user packets without any prior knowledge of the user activity state (UAS) and the channel state information (CSI), and hence significantly reduces the transmission overhead. For the blind signal detection at the BS, we put forth the turbo bilinear generalized approximate message passing (Turbo-BiG-AMP) algorithm. Simulation results demonstrate that the Turbo-BiG- AMP algorithm significantly outperforms the existing compressed sensing based approach and achieves a performance comparable to that of the oracle linear minimum mean-square error (Oracle- LMMSE) algorithm (which assumes perfect knowledge of UAS and CSI at the BS).

Motivated by the recent advances in deep learning, we propose to design high-accuracy low-complexity signal detectors for banded linear systems based on deep neural networks (DNNs). We first design a fully connected DNN for signal detection. Then, to deal with the curse of dimensionality, we propose a novel convolutional neural network (CNN) based on the banded structure of the channel matrix. From simulations, we observe that the proposed CNN outperforms the fully connected DNN in both accuracy and computational time. Moreover, CNN is more robust for the extension to channel matrices with a large size or a wide band. We also run extensive numerical experiments to show that both fully connected DNN and CNN perform much better than existing detectors with comparable complexity.

We study the optimal degrees of freedom (DoF) region of three-user asymmetric multiple-input multiple- output (MIMO) Y channel by considering a hybrid data exchange model. In the hybrid data exchange, we consider both the pairwise data exchange and full data exchange. To derive the optimal DoF region, we analyze the DoF region from both the converse and the achievability aspects. For the converse part, the tight DoF region outer bound is derived using cut-set theorem and genie aided approach. Further, a novel and systematic approach is proposed to analyze the achievability of DoF region. In proving the optimality of achievability, we propose to design distinct patterns to pack the overall transmit data over the channel. We find that the obtained achievable DoF coincides with derived DoF region outer bound.

In ambient backscatter communication (AmBC), it is a challenging task to recover the tag information at the reader due to the difficulty in obtaining the relevant channel state information (CSI). In this paper, we translate the signal detection problem into a clustering problem, for which two known labels are transmitted from the tag as the prior knowledge to assist clustering initialization and signal detection. By exploiting the received signals directly, two clustering-inspired detection methods are proposed, one is called clustering with labeled signals (CLS), and the other is referred to as clustering with labeled and unlabeled signals (CLUS). Both methods are developed based on the proposed modulation-constrained (MC) Gaussian mixture model (GMM). Finally, extensive simulation results show that the proposed methods only have small gaps compared with the optimal detection with perfect CSI.

This paper is concerned with the affine rank minimization (ARM) problem for low-rank matrix recovery purposes. Inspired by the recently proposed Turbo-CS algorithm in the field of compressed sensing, we propose a turbo-type algorithm for ARM, termed Turbo-ARM (TARM). For matrix recovery problems with a large class of random measurement matrices, the performance of TARM can be analyzed via the state evolution framework. Our numerical results show that TARM achieves state-of-the-art reconstruction performance, and our results are further confirmed by state evolution analysis.

In this paper, we consider the design of efficient channel estimation algorithms for downlink massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. By exploiting the channel sparsity, compressed sensing can be used to reduce the pilot overhead in channel identification. Among various compressed sensing algorithms, turbo compressed sensing (Turbo-CS) provides a generic framework for sparse signal recovery with low computational complexity and good performance. In this paper, we propose a structured Turbo-CS (STCS) algorithm to efficiently handle the sparsity of the MIMO-OFDM channel in various transform domains, including the angular domain, the frequency domain, as well as the delay domain. We show that the performance of the proposed algorithm can be characterized by state evolution. We also show that the proposed algorithm can achieve considerable performance gain, as compared with its counterparts.

This letter is concerned with the symmetric nonnegative matrix factorization in the presence of heavy-tailed outliers. We address this problem under a formulation involving some robust loss functions, instead of the standard squared-error loss. To handle the original computationally intractable problem, we present an efficient block iteratively reweighted algorithmic framework with provable convergence guarantee. Each iteration of the proposed method is obtained by minimizing a fourth-order nonnegative polynomial optimization with a closed-form solution. Simulation results illustrate that the proposed algorithm attains a significant performance improvement over existing benchmark methods in heavy-tailed noise environment.

We study the degrees of freedom (DoFs) of a multiple-input multiple-output K-pair two-way relay channel with delayed channel state information (CSI) with J distributed relays. In the considered model, we assume that the users are equipped with M antennas and the relay with N antennas. We propose two schemes, where the signaling design can be carried out either in each individual time slot or across multiple time slots with and without knowledge of CSI. The scheme involves a joint design of user beamforming matrices, relay beamforming matrices, and user postprocessing matrices, so as to meet interference neutralization and rank conditions. We show that the optimal DoF of N2K per user can be reached for N ≥ 1 for an arbitrary number of user pairs when J = 1. This implies that delayed CSI does not compromise the DoF performance of the considered model when N ≥ 1.

In this paper, we study the degrees of freedom (DoF) region for the multiple-input multiple-output two-way X relay channel with asymmetric antenna setting. In this channel model, there are two groups of source nodes, each group contains two source nodes, every source node in one group exchanges independent messages with every source node in another group via a common relay node, and each node has a different number of antennas. First, we derive an outer bound of the DoF region by using the cut-set theorem and the genie-message approach. Then, we obtain an inner bound of the DoF region by proposing a new transmission scheme that collectively utilizes antenna deactivation, pairwise signal alignment, cyclic signal alignment, and generalized signal alignment techniques. In the case of symmetric data exchange, our inner bound coincides with the outer bound, and thus, our proposed transmission strategy is optimal. We also obtain the optimal sum DoF for the special case, where the source nodes within a same group are equipped with the same number of antennas. This paper provides new insights for the study of more complicated relay networks.

In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case, where perfect CSI is available. In this paper, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix. We show that by exploiting the channel sparsity, our proposed scheme can achieve a degree of freedom (DoF) very close to the ideal case, provided that the channel is sufficiently sparse. Specifically, the achievable DoF has a fractional gap of only 1/T from the ideal DoF, where T is the channel coherence time. This is a remarkable advance for understanding the performance limit of the massive MIMO system. We further show that the performance advantage of our proposed scheme in the asymptotic SNR regime carries over to the practical SNR regime. Numerical results demonstrate that our proposed scheme significantly outperforms its counterpart schemes in the practical SNR regime under various system configurations.

In this paper, we endeavor to seek a fundamental understanding of the throughput limit for a training-based uplink multiuser multiple-input multiple-output (MIMO) system. In a multiuser MIMO system, users are geographically separated and experience significantly different large-scale fading. As large-scale fading varies order of magnitude more slowly than small-scale fading, the system resource consumed for acquiring large-scale fading coefficients is usually marginal as compared with that for small-scale fading coefficients. Such a difference between large-scale and small-scale fadings, however, has not been appropriately handled in the existing approaches for training-based multiuser MIMO systems. In this paper, we ignore the overhead for large-scale fading acquisition by assuming that the large-scale fading coefficients are known a priori, whereas the small-scale fading coefficients are estimated via training. Our target is to maximize the mutual information lower bound (MILB) of a training-based uplink multiuser MIMO system over various system parameters, including the training length, the training signals, the number of users, and the power allocation between training and data transmission. As the exact solution is difficult, we establish upper and lower bounds of the MILB. Specifically, we use the majorization theory to establish an upper bound of MILB. We then propose a lower bound of the MILB by deactivating a portion of users with the smallest large-scale fading gains and assigning orthogonal training sequences to the rest of the users. We show that this simple lower bound is asymptotically optimal for MILB maximization in the high signal-to-noise ratio (SNR) regime. We also show that the upper and lower bounds are reasonably tight in the finite SNR regime under various system configurations. Furthermore, we derive the optimal training design for MILB maximization in uplink multiuser MIMO with uniform large-scale fading. Numerical results are presented to verify our analysis.

In this paper, we consider the design of compression functions for compute-and-forward (CF) based relaying. We develop a general compressing framework, termed generalized compute-compress-and-forward (GCCF), where the compression function involves multiple quantization- and-modulo lattice operations. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian-Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. Numerical results are presented to demonstrate the performance superiority of GCCF over other schemes.

This paper studies the transceiver design of the Gaussian two-pair two-way relay channel (TWRC), where two pairs of users exchange information through a common relay in a pairwise manner. Our main contribution is to show that our scheme can achieve the capacity of the two-pair TWRC to within 1/2 bit per user. In the proof, we develop a hybrid coding scheme involving Gaussian random coding, nested lattice coding, superposition coding, and network-coded decoding. Further, we present a message-reassembling strategy to decouple the coding design for the user-to-relay and relay-to-user links, so as to provide more flexibility to fully exploit the channel randomness. We also show that judicious power allocation among the superimposed codeword components at the relay is needed to approach the channel capacity.

In this paper, we investigate the impact of channel sparsity on the fundamental limits of massive multiple-input multiple-output (MIMO) systems with K single-antenna transmit terminals and an N-antenna receiver, where "massive" means N >> K >> 1, and "sparsity" means that a large portion of the channel coefficients are zeros. We propose a novel blind detection scheme that simultaneously estimates the channel and data through factorizing the received signal matrix. We show that the proposed scheme can achieve a degrees of freedom (DoF) arbitrarily close to K(1-1/T) with T being the channel coherence time, provided that N is sufficiently large and the channel is sufficiently sparse. This achievable DoF has a fractional gap of only 1/T from the ideal DoF of K, which is a remarkable advance for understanding the performance limit of the massive MIMO system. Furthermore, we consider the algorithm design for the proposed blind detection scheme. Numerical results demonstrate that the proposed blind detection scheme significantly outperforms its counterpart schemes in the practical SNR regime.

In this paper, we study the cache-aided cloud radio access network (C-RAN) with wireless fronthaul, where multiple cache-enabled users are served by multiple cache-enabled transmitters that are connected to a cloud processor through a wireless fronthaul link. We put forth a caching-and-delivery scheme that combines network-coded fronthaul transmission with cache-aided interference management. By broadcasting network-coded messages, the cloud processor provides additional information of the requested files to the transmitters, so as to reduce the edge delivery time. Based on our scheme, an achievable normalized delivery time (NDT) is derived with respect to the cache sizes and the fronthaul capacity.

The massive integration of electric vehicles (EVs) will pose great challenges to the stability and efficiency of both the conventional power networks and transportation systems. The recently emerging smart grid technology, which integrates advanced communication, control, and charging infrastructures, provides promising solutions to tackle these challenges. In this paper, we consider a smart-grid enabled EV network with heterogeneous charging facilities of different charging costs and capabilities, e.g., allowing EV to sell energy back to the grid. In this case, an EV user needs to decide which path to take, and where and how much to charge/discharge its battery at charging stations in the chosen path such that its journey can be accomplished with the minimum monetary cost. From the system operator's perspective, we study a joint optimization of the routing selection and charging schedules to maximize the overall consumer surplus of a set of EVs. To reduce the computational complexity of the system operator and signaling exchange, we propose a distributed scheme such that each user can maximize its own profit, and the system operator can also achieve the maximum consumer surplus through limited signaling exchange with the EV users. Our simulation shows that the proposed algorithm could efficiently save energy cost of the users and improve the usage of renewable energy in the power network.

We consider efficient communications over the multiple-input multiple-output (MIMO) multiway distributed relay channel (MDRC) with full data exchange, where each user, equipped with multiple antennas, broadcasts its message to all the other users via the help of a number of distributive relays. We propose a physical-layer network coding (PNC) based scheme involving linear precoding for channel alignment, nested lattice coding for PNC, and lattice-based precoding for interference mitigation. We show that distributed relaying achieves the same sum-rate as cooperative relaying in the high SNR regime in most scenarios, which implies that the proposed scheme with distributed relays is more suitable for practical systems than the schemes with cooperative relays.

In this paper, we study the degrees of freedom (DoF) of the symmetric multi-relay multiple-input multiple-output Y channel, where three user nodes, each with M antennas, communicate via K geographically separated relay nodes, each with N antennas. For this model, we establish a general DoF achievability framework based on linear precoding and post-processing methods. The framework poses a nonlinear problem with respect to user precoders, user post-processors, and relay precoders. To solve this problem, we adopt an uplink-downlink asymmetric strategy, where the user precoders are designed for signal alignment and the user post-processors are used for interference neutralization. With the user precoder and post-processor designs fixed as such, the original problem then reduces to a problem of relay precoder design. To address the solvability of the system, we propose a general method for solving matrix equations. Together with the techniques of antenna disablement and symbol extension, an achievable DoF of the considered model is derived for an arbitrary setup of (K, M, N). We show that for K ≥ 2, the optimal DoF is achieved for (M/N) ∈ [0, max{(√(3K/3)), 1}) ∪ [((3K + (9K2- 12K)1/2)/6), ∞). We also show that the uplink-downlink asymmetric design proposed in this paper considerably outperforms the conventional approach based on uplink-downlink symmetry.

Cloud radio access network (C-RAN) is a promising architecture for unprecedented capacity enhancement in next-generation wireless networks thanks to the centralization and virtualization of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. First, this paper endeavors to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message updating schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we generalize the RGMP algorithm to a blockwise RGMP (B-RGMP) algorithm, which allows parallel implementation. The average computation time of B-RGMP remains constant when the network size increases.

We consider training-based channel estimation for a cloud radio access network (CRAN), in which a large amount of remote radio heads and users are randomly scattered over the service area. In this model, assigning orthogonal training sequences to all users will incur a substantial overhead to the overall network, and is even impossible when the number of users is large. Therefore, in this paper, we introduce the notion of local orthogonality, under which the training sequence of a user is orthogonal to those of the other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of ln K, where K is the number of users covered by a CRAN. This implies that the proposed training design yields a scalable solution to sustain the need of largescale cooperation in CRANs.

In this paper, we study the optimal degrees of freedom (DoF) region for the two-pair MIMO two-way relay channel (TWRC) with asymmetric antenna setting, where two pairs of users exchange information with the help of a common relay. First, we derive an outer bound of the DoF region by using the cut-set theorem and the genie-message approach. Then, we propose a new transmission scheme to achieve the outer bound of the DoF region. Due to the asymmetric data exchange, where the two users in each pair can communicate a different number of data streams, we not only need to form the network-coded symbols but also need to process the additional asymmetric data streams at the relay. This is realized through the joint design of relay compression matrix and source precoding matrices. From the optimal DoF region of this channel, we show that in the asymmetric antenna setting, some antennas at certain source nodes are redundant and cannot contribute to enlarge the DoF region.

In this paper, we study wireless power transfer in a multiuser multiple-input single-output (MISO) system, where a base station equipped with N antennas wirelessly transfers power to distributed single-antenna users. To reduce the implementation cost, we propose a constant-envelope analog beamforming scheme to simultaneously transfer power to multiple users which requires only a single radio frequency (RF) chain at the multi-antenna transmitter. We show that the proposed constant-envelope beamforming design only incurs about 1 dB power loss under homogeneous and independent Rayleigh fading, as compared with the optimal variable-envelope digital beamforming design that however requires N RF chains, one for each transmit antenna.

We consider training-based channel estimation for cloud radio access networks (CRANs), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over a certain service area. In this model, assigning orthogonal training sequences to all users, if possible, will cause a substantial overhead to the overall network. Instead, we introduce the notion of local orthogonality, in which the training sequence of a user is required to be orthogonal to the training sequences of other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of ln K, where K is the number of users covered by the CRAN. Therefore, the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs.

In Cloud Radio Access Networks (C-RANs), the high computational complexity of signal processing becomes unaffordable due to the large number of remote radio heads (RRHs) and users. This paper proposes a randomized Gaussian message-passing (RGMP) algorithm to reduce the complexity of uplink signal processing in C-RANs. Specifically, we first propose to use Gaussian message passing to reduce the computational complexity. In C-RANs, RRHs only need to detect signals from nearby users as the signals from distant users are very weak and can be ignored. Thus, in message-passing algorithms, messages only need to be exchanged among nearby RRHs and users. This leads to a linear computational complexity with the number of RRHs and users. Then, to improve the convergence of message passing, we propose to exchange messages in a random order instead of exchanging them simultaneously or in a fixed order. Numerical results show that the proposed RGMP algorithm has better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, which allows us to derive some convergence conditions for the RGMP algorithm. Besides analysis, we also compare the convergence rate of RGMP with existing low-complexity algorithms through extensive simulations.

In this paper, we endeavour to maximize the throughput of training-based multiuser multiple-input multiple-output (MIMO) systems. In a multiuser MIMO system, users are geographically separated. So, the near-far effect plays an indispensable role in channel fading. The existing optimal training design for conventional MIMO does not take the near-far effect into account, and thus is not applicable to a multiuser MIMO system. In this work, we use the majorization theory as a basic tool to study the tradeoff between the channel estimation quality and the information throughput. We establish tight upper and lower bounds of the throughput. Due to the near-far effect, the optimal training design for throughput maximization is to deactivate a portion of users with the weakest channels in transmission. This observation shed light on the practical design of training-based multiuser MIMO systems.

We study the degrees of freedom (DoF) of a symmetric multi-relay multiple-input multiple-output (MIMO) Y channel, where three users, each equipped with M antennas, exchange messages via multiple geographically separated relay nodes, each with N antennas. We formulate a general DoF achievability problem assuming the use of linear precoding and post-processing. To solve this problem, we present a new uplink-downlink asymmetric strategy where the user precoders are designed for signal alignment and the user post-processors are used for interference neutralization. Based on that, we derive an achievable DoF of the considered model for an arbitrary antenna setup. The optimality of the derived DoF is established under certain antenna configurations. Also, we show that our design considerably outperforms the conventional uplink-downlink symmetric design.

We study the degrees of freedom (DoFs) of the multiple input multiple output (MIMO) multipair two-way distributed relay channel (mTDRC), where$K$pairs of users, each equipped with$M$antennas, exchange messages in a pairwise manner with the help of two geographically separated relay nodes, each with$N$antennas. We establish a general framework for the DoF analysis by combining the ideas of signal space alignment and interference neutralization. The proposed framework involves a joint design of user transmit beamformers, relay precoders, and user receive beamformers. Novel signal alignment techniques are proposed to reduce the number of linearly independent constraints for interference neutralization. Based on that, the original joint transceiver and relay design problem boils down to a problem solely on the design of the relay precoders. This problem is then solved utilizing recent development on the solvability of linear matrix equations. As a result, we derive an achievable DoF for the MIMO mTDRC with an arbitrary configuration of$(K,M,N)$. For$K=2$, the optimal DoF of the considered network is derived for$({M}/{N})\in (0, {1}/{2}) \cup (1,\infty )$by showing that the obtained achievable DoF meets a DoF upper bound. This result beats the state of the art by establishing the optimal DoF of the considered network in an extra range of$({M}/{N})\in (1,2)$. In addition, the achievable DoF obtained for$K=2$is much higher than the existing result in the range of$({M}/{N})\in ({11}/{16},1)$. Furthermore, we show that the optimal DoF of the considered network with$K\geq 3$is derived for$({M}/{N})\in (0, ({2K+\sqrt {2K}})/({4K^{2}-2K})) \cup ({3}/{2},\infty )$.

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieving an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To tackle this challenge, we establish a unified theoretical framework for dynamic clustering by exploiting the near-sparsity of large C-RAN channel matrices. Based on this framework, we propose a dynamic nested clustering (DNC) algorithm that greatly improves the system scalability in terms of baseband-processing and channel-estimation complexity. With the proposed DNC algorithm, we show that the computational complexity (i.e., the computation time with serial processing) for the optimal linear detector is significantly reduced from O(N3) to O(N2), where N is the number of remote radio heads (RRHs) in the C-RAN. Moreover, the proposed DNC algorithm is also amenable to parallel processing, which further reduces the computation time to O(N42/23).

Cloud radio access network (C-RAN) emerges as a promising solution to sustain the mobile data explosion with low cost and high energy efficiency. The centralized base band processing of C-RAN facilitates coordinated signal processing at the cloud server, which can potentially lead to huge capacity gain. However, full-scale coordination in a large-scale system inevitably results in high computational complexity that limits the scalability of the system. To address this issue, this paper proposes a scalable uplink signal processing algorithm based on message passing. By exploiting near-sparsity of large C- RAN channel matrices, we derive a sparse message- passing algorithm that reduces the computational complexity of signal detection to be linear with the number of RRHs and users. This implies that the average computational complexity per user does not grow with the network size, and hence the system is scalable. In addition, we discuss the convergence of the sparse message-passing algorithm and propose a block-wise message-passing algorithm that significantly improves the probability of convergence.

In this paper, we investigate joint base station (BS) activation and beamforming design for coordinated downlink transmissions in a cellular heterogeneous network (HetNet). We formulate the total power minimization problem as a mixed integer program. Using the Benders' partitioning method and the convex duality theory, we show that the global optimal solution can then be computed by solving a sequence of relaxed master programs and the associated subproblems with affordable complexity.

In this paper two-path relay channels with direct link between the source and the destination are considered. We present a decode-and-forward cooperative transmission scheme based on nested lattice codes. The proposed scheme performs complete inter-relay interference (IRI) cancellation at the relays as well as successive decoding at the destination, which efficiently combines the signals from the source and the relays. We derive the achievable rates of the proposed scheme, and show that our scheme considerably outperforms the existing schemes in the literature.

We investigate the degrees of freedom (DoF) and secrecy DoF for a general framework of multiway relay networks named wireless MIMO switching, where a number of users exchange information via a common relay. Each round of data exchange consists of one uplink transmission from users to relay and one downlink transmission from relay back to users. The data exchange model is unicast, i.e., every user transmits one message and intends to receive one message from one other user. We categorize unicast patterns using the notion of orbit borrowed from abstract algebra. Roughly speaking, an orbit is a minimum subset of users such that data exchange is closed within this subset. We analyze the achievable DoF of wireless MIMO switching with various numbers of orbits. Particularly, the DoF capacity for unicast with one and two orbits are established. Furthermore, we study communication secrecy with an untrusted relay in wireless MIMO switching. We present an achievable secrecy sum rate and the corresponding achievable secrecy DoF by assuming a non-regenerative relay. Then, we focus on unicast patterns with one and two orbits, and show that this achievable lower bound is actually the secrecy DoF capacity based on a novel genie-aided technique. Our results build a bridge between the DoF and the secrecy DoF in multiway relaying. The methodology of the proof can be generally applied to analyze the secrecy DoF in other relay networks.

We study the degrees of freedom (DoF) of a distributed multi-input multi-output (MIMO) multiway relay channel (mRC) where two pairs of users, each equipped with M antennas, exchange messages in a pairwise manner with the help of two separated relay nodes, each with N antennas. We establish a general framework to derive achievable DoF based on linear signal processing. We show that, to achieve a certain DoF, a joint design of the user transmit beamforming, relay precoding, and user receive beamforming is required. We propose novel signal alignment techniques to reduce the number of linearly independent equations required for interference neutralization. Then, the original joint transceiver and relay design problem boils down to a problem solely on the design of the relay precoders. Based on recent developments on solving linear matrix systems, we determine the solution to the relay design problem and derive the corresponding achievable DoF. Our analysis reveals that the DoF of the considered network is achieved for equation, which is broader than the existing results by covering an extra range of M over N ∈ (1, 2).

Compute-and-forward (CF) can harness interference in a multi-hop relay network by allowing a relay to decode and forward a combination of source messages. However, the total forwarding rate of the relays belonging to one hop may far exceed the total information rate of the sources, which implies information redundancy and spectral inefficiency. To tackle this problem, we propose a novel relaying strategy termed compute-compress-and-forward (CCF). Compared with CF, our proposed CCF scheme includes an extra compressing stage in which the computed combinations of the relays are compressed to reduce forwarding rates. We design the compressing function and develop a successive recovering algorithm to recover source messages at a destination. Numerical results are presented to demonstrate the performance advantage of CCF over CF.

This paper considers the pilot contamination problem in multi-cell massive MIMO systems. We propose a novel cell-specific uplink training scheme along with a downlink pilot contamination elimination precoding (PCEP) scheme. In the uplink training phase, users in one cell apply the same pilot sequence, while orthogonal pilot sequences are employed in different cells. With time-division duplex operation, base stations (BSs) estimate downlink channels in uplink training phases. For downlink data transmissions, the PCEP scheme is introduced to cancel the intra-cell interference caused by pilot contamination. Analytical studies show that the downlink received signal-to-interference-plus-noise ratios (SINRs) of all users approach infinity simultaneously as the number of BS antennas tends to infinity. We also analytically investigate the performance of the proposed schemes in practical scenarios with a finite number of BS antennas. Simulation results demonstrate significant improvement in system performance of the proposed schemes over the existing methods.

In this paper, we consider a symmetric multiple-input-multiple-output (MIMO) multiway relay channel (mRC) with L clusters and K users per cluster operating in clustered pairwise data exchange. Each user is equipped with M antennas, and the relay is equipped with N antennas. The degrees of freedom (DoF) of the MIMO mRC has recently attracted much research interest. The DoF results under certain configurations of (L,K,M,N) have been reported. However, the DoF capacity of the MIMO mRC with an arbitrary network configuration is, in general, far from being well understood. In this regard, the main contribution of this paper is to propose a systematic signal alignment approach to jointly design the beamforming matrices at the users and the relay. Based on that, an achievable DoF is derived for the MIMO mRC with an arbitrary network configuration of (L,K,M,N). Our analysis revealed that the derived achievable DoF is piecewise linear in M and N alternately. Moreover, we showed that the DoF capacity can be achieved for M/N ∈ [1/LK(K-1) + 1/2, ∞) and M/N ∈ (0, Lq/LK(Lq-1)], where q = 2 ⌈K/2⌉.We further derived the asymptotic DoF as K or L → ∞. The DoF analysis in this paper can provide insights on the practical design of efficient communication mechanisms over multiterminal MIMO relay networks.

Energy harvesting is an emerging solution to prolong the lifetime of energy-constrained nodes in wireless networks. This letter develops a novel distributed energy beamforming scheme for realizing simultaneous wireless information and power transfer in the two-way relay channel (TWRC), where two source nodes exchange information via an energy-harvesting relay node. We investigate the optimal transceiver design to maximize the achievable sum-rate of the TWRC. We also propose a low-complexity power-splitting based suboptimal scheme with closed-form solution. Numerical results demonstrate significant performance improvement of the proposed schemes over the conventional power-splitting based scheme.

In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm is shown to be consistent with that derived using the replica method. Numerical results demonstrate that the proposed algorithm outperforms the well-known approximate message passing (AMP) algorithm when a partial DFT sensing matrix is involved.

This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we show in this paper that the channel matrices can be greatly sparsified without substantially compromising the system capacity. Through rigorous analysis, we derive a simple threshold-based channel matrix sparsification approach. Based on this approach, for reasonably large networks, the non-zero entries in the channel matrix can be reduced to a very low percentage (say 0.13% ∼ 2%) by compromising only 5% of SINR. This means each RRH only needs to obtain the CSI of a small number of closest users, resulting in a significant reduction in the channel estimation overhead. On the other hand, the high sparsity of the channel matrix allows us to design detection algorithms that are scalable in the sense that the average computational complexity per user does not grow with the network size.

This paper considers transceiver design for the cellular two-way relay channel (cTWRC), where a multi-antenna base station (BS) exchanges information with multiple single-antenna mobile stations via a multi-antenna relay station. We propose a novel network coding scheme to approach the sum capacity of the cTWRC. Specifically, a new non-linear lattice-based precoding technique is proposed at the BS to pre-compensate the inter-stream interference, in order to allow efficient interference-free lattice decoding at the relay. We derive sufficient conditions for the proposed scheme to asymptotically achieve the sum capacity of the cTWRC in the high signal-to-noise ratio (SNR) regime. Numerical results show that the proposed scheme outperforms the existing schemes and is able to asymptotically achieve the cut-set bound of the cellular relay networks.

In this paper, we investigate the degrees of freedom (DoF) of the half-duplex multiple-input multiple-output (MIMO) multi-way relay channel (MWRC) with full data exchange, where each user wants to learn all the messages from the other users in the channel. We observe that, unlike the case of pairwise data exchange, the uplink and downlink traffic loads are asymmetric when full data exchange is considered. This asymmetry implies that unequal uplink/downlink time allocation, which is allowed in a half-duplex system, can improve the DoF of the MIMO MWRC. Based on that, we derive the DoF capacity of the half-duplex MIMO MWRC with full data exchange. We show that, as compared to the equal uplink/downlink time allocation, the optimized uplink/downlink time allocation achieves a significant DoF gain.

We study achievable degrees of freedom (DoF) of a multi-input multi-output (MIMO) multiway relay channel (mRC) where K users, each equipped with M antennas, exchange messages in a pairwise manner with the help of a single TV-antenna relay node. A novel and systematic way of designing beamforming vectors and matrices at the user and at the relay is proposed to realize signal alignment and to implement physical-layer network coding (PNC). It is shown that, for the considered mRC with K = 3 users, the proposed beamforming design achieves the DoF capacity for any (M, N) setups. For the scenarios with K > 3, we show that the proposed scheme can be improved by disabling a portion of relay antennas so as to align signals more efficiently. Our analysis reveals that the obtained achievable DoF is always piecewise linear, and is bounded either by the number of user antennas M or by the number of relay antennas N. Asymptotic DoF as K → ∞ is also derived based on the proposed signal alignment scheme.

We consider large-scale virtual multiple-input multiple-output (MIMO) systems, in which a large number of user terminals communicate with a large number of cooperative bases stations (BSs). We focus on a training-based scheme and investigate the throughput maximization over various system parameters including pilot symbols, the time allocation coefficient a, the power allocation coefficient γ, and the user number K. Our main contribution is to derive simple throughput expressions by utilizing the random matrix theory, based on which closed-form optimal solutions of (K, γ, α) are obtained. We show that, for a large but finite coherent time T, the optimal K for throughput optimization satisfies K > T/2 and converges to T/2 as the signal-to-noise ratio goes to infinity.

We consider a parallel multiple-input multiple-output (MIMO) relay network, in which a source node communicates with a destination node assisted by multiple parallel relays. In such a network, it is costly to acquire global channel state information (CSI) at every relay node. In this regard, we assume local CSI, i.e., each node in the network knows perfect CSI of the links from and to this node, but only has some statistical information of the other links. We propose an amplify-and-forward relaying strategy, termed doubly Hermitian precoding to efficiently exploit the potential benefit of local CSI. We show that the proposed relaying strategy is asymptotically capacity-approaching as the number of relays tends to infinity. Numerical results demonstrate that the proposed scheme performs close to the performance upper bound obtained by assuming global CSI.

In this paper, we study the point-to-point E-band millimeter wave wireless channel with uniform linear antenna arrays (ULAs) deployed at both link ends and present an analytical approach to characterize the channel behavior. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. The asymptotic behavior and the effective multiplexing distance (EMD) of the E-band channel are then investigated, where the latter is defined as the end-to-end distance at which the channel can support a certain number of spatially independent streams at finite signal-to-noise ratios (SNRs). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. This finding provides useful insights into the design of practical multi-gigabits wireless communication systems over E-band.

This work investigates the broadcast channel with the knowledge of noncausal interference at the transmitter and side information at the receivers. The system studied involves one transmitter sending private information to two receivers. We first obtain an achievable rate region of this system by extending Gelfand and Pinsker's (GP's) random binning method, originally designed for point-to-point communications. We then apply the result to Gaussian scalar and vector channels, and consider the design of the auxiliary random variable used in GP's approach. Asymptotic analysis shows that the proposed scheme in both scalar and vector channels is asymptotically capacity-achieving at high signal-to-noise ratio (SNR). We further investigate the system optimization in the finite SNR regime. For the scalar channel, we derive the optimal auxiliary factor that maximizes the weighed sum-rate. For the vector channel, two kinds of suboptimal designing methods for the auxiliary matrix are proposed. Numerical results show that the proposed scheme can achieve a sum-rate close to the cut-set bound in the entire SNR regime, and significantly outperforms the schemes that do not exploit the knowledge of noncausal interference known at the transmitter.

We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and orthogonal frequency-division multiplexing systems. We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. Area theorems are established to characterize the behavior of the iterative receiver. We show that, for unconstrained signaling, the proposed single-code scheme with linear precoding and iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of the channel state information at the transmitter. We further show that, for constrained signaling, our proposed single-code scheme considerably outperforms the conventional multicode parallel transmission scheme based on singular value decomposition and water-filling power allocation. Numerical results are provided to verify our analysis.

This paper studies the fundamental characteristics of point-to-point E-band channels with uniform linear antenna arrays (ULAs) deployed at both the transmitter and receiver. We model the channels as line-of-sight (LoS) multiple-input multiple-output (MIMO) ones and focus on the channel eigenvalue characterization when theRayleigh distance criterioncannot be fulfilled due to limited physical sizes of the transmitter and receiver. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. Asymptotic analyses are then developed when the antenna numbers at the transmitter and receiver or the distance between them goes to infinity. Based on these analytical results, the maximum eigenvalue and theeffective multiplexing distance(EMD) of the E-band channel are investigated, where EMD is defined as the end-to-end distance at which the channel can support a certain number of simultaneous spatial streams at a given signal-to-noise ratio (SNR). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. Numerical results are provided to validate the analyses.

Recently, much research interest has been focused on the design of efficient communication mechanisms for multiple-input-multiple-output (MIMO) multiway relay channels (mRCs). In this paper, we investigate achievable degrees of freedom (DoFs) of the MIMO mRC with L clusters and K users per cluster, where each user is equipped with M antennas and the relay with N antennas. Our analysis is focused on a new data exchange model, termed clustered full data exchange, i.e., each user in a cluster wants to learn the messages of all the other users in the same cluster. Novel signal alignment techniques are developed to jointly and systematically construct the beamforming matrices at the users and the relay for efficient implementation of physical-layer network coding. Based on this, we derive an achievable DoF of the MIMO mRC with an arbitrary network configuration of L and K, as well as with an arbitrary antenna configuration of M and N. We show that our proposed scheme achieves the DoF capacity when M/N ≤ 1/(LK - 1) and M/N ≥ ((K - 1)L + 1)/KL. The DoF results derived in this paper can serve as fundamental benchmarks in evaluating the performance of practical communication systems over MIMO mRCs and provide guidance and insights into the design of wireless relay networks.

In this paper, we develop a novel iterative equalization algorithm for energy-spreading-transform (EST) based multiple-input multiple-output (MIMO) systems. We show that the proposed scheme significantly outperforms the existing non-linear MIMO equalizers in various system setups. We further investigate the precoder design based on the signal-to-interference-plus-noise-ratio (SINR) variance evolution technique, so as to exploit the available channel state information at the transmitter (CSIT). We derive the optimal precoding directions, and show that the precoder optimization then boils down to a simple power allocation problem that is solvable using convex programming. Numerical results demonstrate that the optimized precoder can achieve a significant power gain, as compared with the non-optimized scheme.

This work investigates the maximum sum-rate of multi-pair MIMO two-way relay channels (TWRCs), in which a relay is responsible for forwarding information between multiple pairs of users. In this system, each pair of users forms a TWRC, and a user exchanges information only with its counterpart in the same TWRC. We focus on the multi-access channel (MAC) phase of the two-pair TWRCs. We first put forth a new interpretation of the space-division (SD) method based on the classical concept of principal angles in linear algebra. We argue that the signal spaces of the two users in a TWRC can be divided into the physical-layer network coding (PNC) signal subspace and the complete decoding (CD) signal subspace according to the principal angles between the two signal spaces of different user pairs. Based on the principal-angle framework, we then propose an extended SD method to mitigate the interference among the PNC signals and CD signals. We further derive the optimal decoding strategy and optimal precoder design principles that maximize the sum-rate of the MAC phase. The associated optimization problem, however, is non-convex. We therefore propose a suboptimal solution for precoder design and analyze the asymptotic rate gap benchmarked against the cut-set bound. Significant performance improvements have been observed for the proposed hybrid PNC-CD system compared with pure complete decoding and pure PNC decoding.

We present a novel physical layer encryption (PLE) scheme that randomizes the radio signals using a discrete Fourier transform (DFT) based encryption algorithm. For any baseband signaling method, we show that perfect secrecy is asymptotically achievable with the proposed DFT-based encryption method when the signal block length (N) approaches infinity. For practical systems with finite N, we also show that the proposed encryption method can transmit at a secrecy rate close to the main channel's achievable data rate. In this sense, transmission privacy is achieved without compromising the capability of the communication channel. Besides, the proposed encryption method can hide the transmission data rate and is immune to all existing upper-layer attacks. The performance advantages of the proposed DFT-based encryption method is verified through comparisons against other existing PLE methods.

This paper is concerned with precoder design for multiple-input multiple-output (MIMO) systems with iterative equalization. We first consider the case of no channel state information at the transmitter (CSIT). Based on evolution analysis, we derive the optimized precoder that minimizes the bit error rate (BER) of the system. We show that, with the optimized precoder, the linear precoding and iterative equalization scheme can achieve a genie-aided performance upper bound at high signal-to-noise ratio (SNR). We further consider the precoder design with perfect CSIT. We show that the precoder design problem reduces to a convex power-allocation problem that can be efficiently solved using standard convex programming tools. Numerical results are provided to demonstrate the performance advantages of the proposed scheme over its counterparts.

We propose a novel space-division based network-coding scheme for multiple-input multiple-output (MIMO) two-way relay channels (TWRCs), in which two multi-antenna users exchange information via a multi-antenna relay. In the proposed scheme, the overall signal space at the relay is divided into two subspaces. In one subspace, the spatial streams of the two users have nearly orthogonal directions, and are completely decoded at the relay. In the other subspace, the signal directions of the two users are nearly parallel, and linear functions of the spatial streams are computed at the relay, following the principle of physical-layer network coding (PNC). Based on the recovered messages and message-functions, the relay generates and forwards network-coded messages to the two users. We show that, at high signal-to-noise ratio (SNR), the proposed scheme with optimized precoding achieves the asymptotic sum-rate capacity of MIMO TWRCs within 1/2 log(5/4) ≈ 0.161 bit per user-antenna, for any antenna configuration and any channel realization.

This paper addresses relay design for a wireless multiple-input-multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multi-antenna relay linearly precodes the received (uplink) signals from multiple users before forwarding the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on sum-rate maximization criteria is typically non-convex and difficult to solve. The main contribution of this paper is that we show the sum-rate maximization problem can be converted to an equivalent weighted sum-MSE minimization problem and can therefore be solved using an iterative algorithm proposed in our previous work. Asymptotic analysis reveals that, with properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high and low signal-to-noise-ratio (SNR) regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.

In this paper, we study the equalization problem for linearly precoded multiple-input multiple-output (MIMO) systems. We develop novel iterative equalization algorithms based on message-passing principles. We establish an evolution technique to analyze the performance of the proposed iterative equalization algorithms. We show by numerical results that the simulated performance of the proposed linear precoding and iterative equalization scheme agrees well with the evolution analysis, and that the proposed scheme can significantly outperform the existing schemes. It is worth noting that the performance advantage of the proposed scheme is achieved without exploiting any knowledge of channel state information at the transmitter (CSIT).

In this paper, we consider a bidirectional cellular relay network with distributed relays where a single base station exchanges information with multiple independent users through multiple single-antenna relays. We design the transceivers at the base station, the relays, and the users. The related optimization problems are generally non-convex and difficult to solve. In this paper, we propose a unified framework to design the transceiver algorithms based on two criteria, i.e. weighted sum MSE minimization and sum rate maximization. Specifically, we show that the sum rate maximization problem can be converted into an iterative weighted sum MSE minimization problem. Low-complexity iterative algorithms are developed for both weighted sum MSE minimization and sum rate maximization optimization problems. However, the convergence points of the proposed iterative algorithms are sensitive to the initial conditions, especially in the high signal-to-noise ratio (SNR) regime. For this reason, we further derive the high-SNR asymptotically optimal solutions and use them as the initials for the proposed iterative algorithms. Simulation results show that the proposed scheme can approximately double the system throughput, compared to the conventional four-stage transmission schemes.

We consider a distributed multiple-input multiple-output (MIMO) system in which multiple transmitters cooperatively serve a common receiver. It is usually very costly to acquire full channel state information at the transmitter (CSIT) in such a scenario, especially for large-scale antenna systems. In this paper, we assume individual CSIT (I-CSIT), i.e., each transmitter has perfect CSI of its own link but only slow fading factors of the others. A linear Hermitian precoding technique is proposed to enhance the system performance. The optimality of the proposed precoding technique is analyzed. Numerical results demonstrate that the performance loss incurred by the I-CSIT assumption is negligible as compared to the full-CSIT case.

We investigate efficient communication over multiple-input multiple-output (MIMO) two-way relay channels (TWRCs), where two multi-antenna users exchange information via a multi-antenna relay. We propose a new MIMO physical-layer network coding (PNC) scheme that includes novel eigen-direction alignment (EDA) precoding. The proposed EDA precoding efficiently aligns the two-user's eigen-modes into the same set of orthogonal directions, and multiple independent PNC streams are implemented over the aligned eigen-modes. We derive an achievable rate-pair of the proposed scheme, for given EDA precoding parameters, over a MIMO TWRC. To maximize the achievable rate-region, we formulate a design criterion for the EDA precoding parameters, and present solutions to the formulation. Closed-form bounds on the sum-rates of the designed EDA-PNC schemes are derived. Numerical results show that there is only a small gap between the achievable rate of the proposed scheme and the capacity upper bound of the MIMO TWRC. It is shown that the proposed scheme can significantly outperforms existing schemes in the literature.

This paper addresses joint transceiver and relay design for a wireless multiple-input multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multiantenna relay linearly precodes the received (uplink) signals from multiple users and forwards the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically nonconvex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can, therefore, be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high- and low-signal-to-noise-ratio regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.

We propose a novel space-division-based network-coding scheme for multiple-input multiple-output (MIMO) two-way relay channels (TWRCs), in which two multiantenna users exchange information via a multiantenna relay. In the proposed scheme, the overall signal space at the relay is divided into two subspaces. In one subspace, the spatial streams of the two users have nearly orthogonal directions and are completely decoded at the relay. In the other subspace, the signal directions of the two users are nearly parallel, and linear functions of the spatial streams are computed at the relay, following the principle of physical-layer network coding. Based on the recovered messages and message-functions, the relay generates and forwards network-coded messages to the two users. We show that, at high signal-to-noise ratio, the proposed scheme achieves the asymptotic sum-rate capacity of the MIMO TWRC within [ 1/ 2]log(5/4) ≈ 0.161 bits per user-antenna, for any antenna configuration and any channel realization. We perform large-system analysis to derive the average sum-rate of the proposed scheme over Rayleigh-fading MIMO TWRCs. We show that the average asymptotic sum-rate gap to the capacity is at most 0.053 bits per relay-antenna. It is demonstrated that the proposed scheme significantly outperforms the existing schemes.

In this paper, we consider a distributed MIMO communication network in which multiple transmitters cooperatively send common messages to a single receiver. In this scenario, it is usually costly to acquire full channel state information at the transmitters (CSIT), i.e., every transmitter perfectly knows the overall channel state information (CSI) of the network. Hence, we assume individual CSIT (I-CSIT), i.e., each transmitter only knows its own CSI. We propose a novel precoding technique, named Hermitian precoding, to enhance the system performance under the constraint of I-CSIT. We show that the proposed scheme can perform close to the system capacity with full CSIT. This reveals that the amount of CSI required at the transmitters can be significantly reduced without considerably compromising performance.

We propose a new reduced-dimension (RD) eigen-direction alignment (EDA) precoder for MIMO two-way relay channels (TWRCs) with nR>; nT, where nRdenotes the number of antennas at the relay, and nTis that at each of the two users. The RD-EDA precoder can efficiently create aligned eigen-modes for the two users, enabling independent streams of physical-layer network coding. We investigate the design of the RD-EDA and develop a simple suboptimal solution. It is shown that the proposed RD-EDA scheme performs close to the sum-capacity upper bound of the MIMO TWRC. Moreover, it is shown that the proposed scheme can significantly outperform other existing schemes in the literature.

We investigate efficient communications over MIMO two-way relay channels (TWRCs) of nTR, where nTdenotes the number of antennas at each user and nRdenotes that at the relay. We propose a new reduced-dimension (RD) cooperative precoding scheme. In the proposed scheme, the two users cooperatively create nTaligned eigen-modes, supporting nTstreams of physical-layer network coding. We investigate the design of the RD cooperative precoder and derive an asymptotically optimal solution. We analytically show that, in the worst case, the proposed scheme is within ½ bit per transmit antenna of the asymptotic sum-capacity of the MIMO TWRC. For fading MIMO TWRCs with i.i.d. Gaussian coefficients, we derive a closed-form expression of the average sum-rate of the proposed scheme using large system analysis. Our analytical result shows that, for a large system with nT/nR= ½, the proposed scheme is less than 0.16 bit per transmit antenna away from the capacity. Furthermore, this gap reduces as nT/nRincreases, and vanishes as nT/nRtends to 1. It is demonstrated that the proposed scheme can significantly outperform other existing schemes in the literature.

We propose a new joint channel coding and physical layer network coding (CPNC) scheme for multiple-input multiple-output (MIMO) two-way relay channels (TWRCs). At the heart of the scheme lies a key technique referred to as eigen-direction alignment (EDA) precoding. This technique efficiently creates multiple aligned parallel channels which facilitates the deployment of multi-stream CPNC. Our analysis shows that the achievable rate of the scheme can approach the capacity upper bound in the median to high signal-to-noise (SNR) region when nT> nR, where nTand nRdenote the number of antennas of each user and that of relay, respectively. The gap to the capacity upper bound diminishes as nT/nRincreases. Numerical results demonstrate that the proposed scheme significantly outperform other well-known schemes in the literature.

We propose a space-time coding scheme for efficient transmission over multiple-input multiple-output (MIMO) channels without channel state information at the transmitter (CSIT). The proposed scheme involves linear precoding (LP) at the transmitter and iterative linear minimum mean-square error (LMMSE) detection at the receiver. We develop a procedure to jointly optimize the forward-error-control (FEC) coding and LP, taking into consideration of the iterative detection process. Our analysis shows that the proposed scheme can perform close to the outage capacity of MIMO channels.

In this paper, we consider the performance analysis of multiple-input multiple-output (MIMO) inter-symbol interference (ISI) systems involving linear-precoding (LP) and iterative linear minimum mean-square error (ILMMSE) detection. The main contribution of this paper is an area theorem to evaluate the achievable rate of the proposed LP-ILMMSE scheme. Based on this area theorem, we further optimize the linear precoder to maximize the achievable rate. Numerical results are provided to verify our analysis.

Asymptotic analysis of the dual-diagonal (DD) linear-minimum-mean-square-error (LMMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems is presented. We prove that the DD-LMMSE estimator is asymptotically optimal among all DD estimators as the number of subcarriers tends to infinity. Based on the asymptotic analysis, we propose an evolution tool to predict the performance of iterative OFDM systems. Numerical results are presented to verify our analysis.

In this paper, we present a joint forward-error-correction (FEC) coding and linear precoding scheme for multiple-input multiple-output (MIMO) and inter-symbol interference (ISI) channels with imperfect channel state information at the transmitter (CSIT). We first study the performance of ideally coded systems. We focus on an average power gain (APG) method that can achieve capacity in the two extreme cases of no CSIT and perfect CSIT. In the more general case, the performance of the APG method improves progressively with the CSIT quality. We then consider the implementation of this APG method in a practically coded system. We propose a unified scheme involving beamforming, water-filling, and diversity coding. The core of the new scheme is a joint FEC coding and linear precoding strategy at the transmitter and an iterative detection process at the receiver. Simulation results demonstrate that the proposed scheme can achieve significant performance gain by efficiently utilizing the available CSIT.

We propose a dual diagonal linear-minimum-mean-square-error (DD-LMMSE) channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems involving joint channel estimation and signal detection. Computational complexity and mean-square-error (MSE) analysis are presented to evaluate the efficiency of the proposed algorithm. Compared with the optimal LMMSE method, the proposed DD-LMMSE method can achieve significant complexity reduction without compromising much performance.

This paper is concerned with a simple binary erasure-recovery coding scheme that falls into the family of so-called semi-random low-density-parity-check (SR-LDPC) codes. Based on a constrained random-scrambling technique, the proposed coding scheme is systematic, rateless, and capacity-achieving. We provide simulation examples comparing the new scheme with the well-known Luby transform (LT) and raptor codes. It is shown that the new scheme has advantages in complexity and performance over its counterparts especially in channels with a relatively low erasure rate.

We extend the area property to more general linear channel models (including inter-symbol-interference (ISI) and multiple-input multiple-output (MIMO) systems) with arbitrary input constellations. We show that the theoretical limit of a channel (i.e., the mutual information) can be achieved under some assumptions. Several engineering techniques are examined to ensure these assumptions. It is shown that the required assumptions can be approximately materialized using superposition coded modulation (SCM) and a preceding technique.

We propose a family of binary erasure codes, namely, quasi-systematic doped LT (QS-DLT) codes that are almost systematic, universal, and asymptotically capacity-achieving with encoding and decoding complexity O(K log(1/epsiv)), where K is the information length, and e is the overhead. Finite-length analysis is carried out to study the error-floor behavior of our proposed codes. Numerical results verify that our proposed codes provide a low-complexity alternative to systematic Raptor codes with comparable performance.

We study superposition coded modulation (SCM) with iterative linear minimum-mean-square-error (LMMSE) detection. We show that SCM offers an attractive solution for highly complicated transmission environments with severe interference. We analyze the impact of signaling schemes on the performance of iterative LMMSE detection. We prove that among all possible signaling methods, SCM maximizes the output signal-to-noise/ interference ratio (SNIR) in the LMMSE estimates during iterative detection. Numerical examples are used to demonstrate that SCM outperforms other signaling methods when iterative LMMSE detection is applied to multi-user/multi-antenna/multipath channels.

We propose a family of binary erasure codes, namely, quasi-systematic doped Luby-Transform (QS-DLT) codes, that are rate-less, almost systematic, and universally capacity-achieving without the prior knowledge of channel erasure rate. The encoding and decoding complexities of QS-DLT codes are O(Klog(1/epsiv)), where K is the information length, and epsiv is the overhead. Stopping-set analysis is carried out to study the error-floor behavior of QS-DLT codes. Analysis and numerical results demonstrate that QS-DLT codes provide a low-complexity alternative to systematic Raptor codes with comparable performance.

In this paper, we study the iterative detection problem for a coded system with multi-ary modulation. We show that, with iterative linear minimum-mean-square-error (LMMSE) detection, superposition coded modulation (SCM) can provide performance superior to that with other traditional signaling schemes used in trellis coded modulation (TCM) and bit-interleaved coded modulation (BICM). This finding provides a useful guideline for system design considering inter-symbol interference (ISI) and other forms of interference. Simulation results are provided to illustrate the efficiency of the iterative LMMSE detection with different signaling schemes.

This paper is concerned with the low-cost iterative equalization/detection principles for coded linear systems with cyclic prefixes. Turbo frequency-domain-equalization (FDE) is applied to systems that may contain the joint effect of multiple-access interference (MAI), cross-antenna interference (CAI) and inter-symbol interference (ISI). We develop an SNR-variance evolution technique for the performance evaluation of the proposed systems. Numerical results in various channel environments demonstrate excellent agreement between the predicted and simulated system performance.

We propose a low-cost detection strategy for multi-user multiple-input-multiple-output (MIMO) systems with inter-symbol interference (ISI). The cyclic prefix (CP) technique is assumed. The proposed detection algorithm is derived in a very concise manner based on some elegant properties of circulant matrices. We show that multi-user detection and equalization can be carried out jointly and efficiently.

We investigate spectrum shaping based on cyclic filtering for coded single-carrier transmission over intersymbol interference (ISI) channels. An optimized precoder is derived for coded single-carrier systems using an iterative linear-minimum-mean-square-error (LMMSE) frequency-domain-equalization (FDE) receiver. Numerical results show that this precoder can provide considerable spectrum-shaping gain.

We introduce a family of rateless codes, namely the doped accumulate LT (DALT) codes, that are capacity-approaching on a binary erasure channel (BEC) with bounded encoding and decoding complexity. DALT codes can be either systematic or non-systematic. Non-systematic DALT codes are very similar to raptor codes, except that joint optimization on the full coding graph can be applied to DALT codes, and thus they can be optimized to have better asymptotic performance than raptor codes. Systematic DALT codes, with the proposed protocol, exhibit better performance and lower complexity than their non-systematic counterparts.

This paper is concerned with the iterative detection principles for coded linear systems with cyclic prefixes. We derive a matrix-form low-cost fast Fourier transform (FFT) based iterative LMMSE-APP detector and propose an evolution technique for the performance evaluation of the proposed detector. Numerical results show a good match between simulation and evolution prediction.

This paper is concerned with signal estimation over general multiple-input-multiple-output (MIMO) channels based on a jointly Gaussian (JG) approach. We show that the proposed method is equivalent to the LMMSE approach, but is conceptually more concise and computationally more efficient. A 2×2 multi-antenna system with multipath effect is considered as an example to illustrate the advantages of the JG method. Also, a simple SNR-bounding evolution tool, similar to EXIT chart technique, is developed for fast assessment of the JG method. Compared with the optimal MAP algorithm, the JG method can achieve almost the same performance with greatly reduced complexity.