We study a space--time block code from a maximal order in the definite quaternion algebra $(-1,-3)_{\Q}$. Its embedding into $\C^{2\times 2}$ yields an Alamouti--Eisenstein code over $\Z[w]$ with full diversity, orthogonality, and non-vanishing determinant. The underlying lattice is isomorphic to $\Z[w]^2$, while the embedded lattice has $A_2\oplus A_2$ geometry, yielding a hexagonal shaping gain. We compare it with the classical Alamouti code over $\Z[i]$ in terms of shaping, constellation-constrained mutual information, and finite-blocklength achievable rates, obtaining an asymptotic energy gain of about $0.79$~dB and a small but positive mutual-information gain. At the same SNR and rate, the Alamouti--Eisenstein design also improves short-packet reliability.

This study explores the optimization of transmission strategies focusing on energy efficiency within a network comprising ground-based beyond-diagonal reconfigurable intelligent surfaces (BD-RIS), a.k.a RIS 2.0, and multiple unmanned aerial vehicles (UAVs). The motivation behind this work stems from the critical need to enhance energy efficiency in next-generation wireless networks, where the integration of UAVs and RIS technologies presents both opportunities and challenges. Specifically, while UAVs offer flexible deployment and improved coverage, their limited battery life and the complex interference environment in multi-user networks necessitate innovative solutions for sustainable operation. Each UAV is designed to serve its corresponding user group, with each group utilizing unique subcarriers to maintain orthogonality and employing a rate-splitting multiple access (RSMA) strategy within each group. The primary objectives of this work are to optimize: 1) the allocation of BD-RIS elements to groups, 2) the phase rotations of BD-RIS, 3) the common rate allocation in RSMA, 4) UAV trajectories, and 5) the design of precoders. To achieve these objectives, we formulate an optimization problem under the framework of mixed-integer nonlinear programming (MINLP), with a focus on maximizing energy efficiency. Our proposed solution combines generalized Benders decomposition (GBD), a manifold-based algorithm, and successive convex approximation (SCA). GBD decomposes the MINLP into primal and master sub-problems, which are iteratively solved. To efficiently address variable coupling in the primal problem, we adopt a block coordinate descent (BCD) method and employ the Riemannian conjugate gradient (RCG) technique for phase rotation. SCA addresses the remaining challenges in the primal problem, while a two-stage approach simplifies the optimization process. Simulations confirm the significant energy efficiency improvements achieved by the proposed method.

In this paper, we investigate the fundamental trade-off between communication and sensing performance ofad hocintegrated sensing and communication (ISAC) wireless networks. Specifically, we consider thatnnodes are randomly located in an extended network with areanand transmit ISAC signals. Under the pure path loss channel gain model and the condition that the transmission power scales according to the communication distance, we fully characterize the optimal scaling law trade-off between throughput and sensing distance by proposing an achievable scheme and proving its converse. Our results can be interpreted as follows: by reducing the throughput by a factor of a function ofn, the sensing range order improves according to the same function ofn, raised to the power of the ratio between the path loss factors in communication and sensing. We prove that the same result also holds true for ISAC networks with random fading, despite the uncertainty on the connectivity and power level created by random fading. In addition, we show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale freely. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff ofad hocISAC networks.

In Contention Resolution Diversity Slotted ALOHA (CRDSA) and Irregular Repetition Slotted ALOHA (IRSA), iterative decoding is typically assumed to be instantaneous, overlooking the decoding latency encountered in practical systems. This paper proposes a Partially Parallel Decoding ($\textsf {PPD}$) framework that offers a tunable trade-off between latency, complexity, and throughput. The proposed framework supports both CRDSA and IRSA with AWGN and Rayleigh fading by incorporating, in each iteration of the decoding process, a scheduling algorithm that selects target packets based on the number of available decoders, along with a slot selection algorithm that identifies a subset of time slots for the equalization step to manage complexity. Simulation results show that in both AWGN and fading environments, the PPD framework achieves performance close to that of sequential decoding while significantly reducing latency—by up to$64\times $for CRDSA and$16\times $for IRSA—with a practical number of decoders.

Recently, simultaneous generalized triangular decomposition (SGTD) has emerged as an effective matrix decomposition technique for multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. In this paper, we extend SGTD to a two-user MIMO uplink setting where each user is assisted by a dedicated reconfigurable intelligent surface (RIS), enabling user-specific channel control. Both the conventional diagonal RIS (D-RIS) and the more general beyond-diagonal RIS (BD-RIS) are considered. For both designs, we derive low-SNR and high-SNR approximations of the sum-rate expressions, enabling lightweight optimization problems that are solved using either Riemannian manifold optimization (RMO) or quasi-Newton-based methods. In addition, a low-complexity scheme based on closed-form approximations is also provided. Simulation results and detailed complexity analysis demonstrate that the proposed schemes, which solve lightweight optimization problems, achieve significantly higher sum rates than benchmarks, while the proposed closed-form scheme still outperforms the benchmarks with substantially reduced computational complexity.

This paper revisits both the uplink and downlink two-user multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We present a novel concept of matrix decomposition called simultaneous generalized triangular decomposition (SGTD). The main idea is to simultaneously decompose two matrices into predetermined triangular forms while providing flexibility in designing the ratios of the diagonal elements. For uplink MIMO-NOMA, the proposed SGTD enables interference-free transmission between users by transforming MIMO channels into two triangular forms and successively decoding and canceling data streams. Unlike existing simultaneous diagonalization or triangularization approaches with fixed diagonal values, the proposed method provides flexibility in adjusting the diagonal elements of the triangular matrices. This adaptability makes it particularly advantageous for meeting specific design requirements, such as maximizing the weighted sum rate and ensuring stable transmission across each spatial stream. As an example, an optimized design is provided to maximize the weighted sum rate within the proposed framework. A similar approach is also proposed to develop a new precoding design for downlink MIMO-NOMA. Extensive simulations demonstrate that, for both uplink and downlink scenarios, the proposed scheme outperforms state-of-the-art MIMO-NOMA and orthogonal multiple access (OMA) systems.

This study addresses the critical challenge of quickest mean-change detection (QMCD), which involves promptly detecting changes in the mean of a sequence of independent observations, particularly under the challenging condition where both pre-change and post-change distributions are unknown. A novel detector is proposed to tackle the QMCD problem. It recursively computes the backward sample average and checks whether the result escapes the forward confidence sequence, hence the name BAckward Recursive AVerage escapes Outside Confidence Sequence (BRAVO-CS). Rigorous analysis demonstrates that the proposed BRAVO-CS detector achieves an average detection delay that scales inversely with the magnitude of the mean shift-significantly better than the inverse quadratic scaling of current state-of-the-art approaches when the change is subtle. Furthermore, BRAVO-CS incurs linear time complexity, in stark contrast to the quadratic time complexity of state-of-the-art methods. Extensive simulations further confirm its robustness and efficiency across diverse scenarios, establishing BRAVO-CS as a compelling solution for real-time systems requiring rapid and reliable mean change detection.

In this paper, we investigate the communication and sensing performance tradeoff of ad hoc integrated sensing and communication (ISAC) wireless networks. Specifically, we consider that$n$nodes are randomly located in an extended network with area$n$and transmit ISAC signals. Our goal is to answer the following questions: what is the tradeoff between the throughput and sensing range of an ISAC network and how does it scale with the network size or node numbers? Under the condition that the transmission power scales according to the communication distance, we fully characterize the scaling law tradeoff between throughput and sensing distance by proposing an achievable scheme and proving its converse. Interestingly, our results reveal that by reducing the throughput by a factor of a function of$n$, the sensing range order improves according to the same function of$n$, raised to the power of the ratio between the path loss factors in communication and sensing. We also show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale differently. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff of ad hoc ISAC networks.

A Batch Private Information Retrieval (batch-PIR) scheme allows a client to retrieve multiple data items from a database without revealing them to the storage server(s). Most existing approaches for batch - Pirare based on batch codes, in particular, probabilistic batch codes (PBC) (Angel et al. S&P'18), which incur large storage overheads. In this work, we show that zero storage overhead is achievable for tree-shaped databases. In particular, we develop TreePIR, a novel approach tailored made for private retrieval of the set of nodes along an arbitrary root-to-leaf path in a Merkle tree with no storage redundancy. This type of tree has been widely implemented in many real-world systems such as Amazon DynamoDB, Google's Certificate Transparency, and blockchains. Tree nodes along a root-to-leaf path forms the well-known Merkle proof. TreePIR, which employs a novel tree coloring, outperforms PBC, a fundamental component in state-of-the-art batch-PIR schemes (Angel et al. S&P'18, Mughees-Ren S&P'23, Liu et al. S&P'24), in all metrics, achieving 3 ×lower total storage and 1.5-3 ×lower computation and communication costs. Most notably, TreePIR has 8-160× lower setup time and its polylog-complexity indexing algorithm is 19–160 ×faster than PBC for trees of 210_224leaves.

This paper proposes a novel Particle Swarm Optimization-Aided Reinforcement Learning (PSO-RL) framework for efficient beam and power management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) networks. To optimize dynamic beam coordination and resource allocation, we introduce a reinforcement learning (RL)-based approach that integrates value and policy networks, with the policy network enhanced by a target policy network (TPN). By employing PSO, we refine TPN parameters to accelerate convergence and enhance solution exploration. Simulation results validate the effectiveness of the proposed PSO-RL framework, demonstrating significant performance improvements in RISassisted network deployments. The method enhances system capacity by 29.75 % and improves edge user capacity by 36.65 %. These results highlight the potential of PSO-RL for optimizing beam allocation in dynamic urban network environments.

Binary linear block codes (BLBCs) are essential to modern communication, but their diverse structures often require tailor-made decoders, increasing complexity. This work introduces enhanced polar decoding ($\textsf {PD}^{+}$), a universal soft decoding algorithm that transforms any BLBC into a polar-like code compatible with efficient polar code decoders such as successive cancellation list (SCL) decoding. Key innovations in$\textsf {PD}^{+}$include pruning polar kernels, shortening codes, and leveraging a simulated annealing algorithm to optimize transformations. These enable$\textsf {PD}^{+}$to achieve competitive or superior performance to state-of-the-art algorithms like OSD and GRAND across various codes, including extended BCH, extended Golay, and binary quadratic residue codes, with significantly lower complexity. Moreover,$\textsf {PD}^{+}$is designed to be forward-compatible with advancements in polar code decoding techniques and AI-driven search methods, making it a robust and versatile solution for universal BLBC decoding in both present and future systems.

We consider the uplink multiple access of heterogeneous users, e.g., ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) users. Each user has its own reliability requirement and blocklength constraint, and users transmitting longer blocks suffer from heterogeneous interference. On top of that, the decoding of URLLC messages cannot leverage successive interference cancellation (SIC) owing to the stringent latency requirements. This can significantly degrade the spectral efficiency of all URLLC users when the interference is strong. To overcome this issue, we propose a new multiple access scheme employing discrete signaling and treating interference as noise (TIN) decoding, i.e., without SIC. Specifically, to handle heterogeneous interference while maintaining the single-user encoding and decoding complexities, each user uses a single channel code and maps its coded bits onto sub-blocks of symbols, where the underlying constellations can be different. We demonstrate theoretically and numerically that the proposed scheme employing quadrature amplitude modulations and TIN decoding can perform very close to the benchmark scheme based on Gaussian signaling with perfect SIC decoding. Interestingly, we show that the proposed scheme does not need to use all the transmit power budget, but also can sometimes even outperform the benchmark scheme.

Streaming codestake a string of source symbols as input and output a string of coded symbols in real time, which eliminate the queueing delay of traditionalblock codesand are thus especially appealing for delay sensitive applications. This work studies the asymptotics of random linear streaming codes (RLSCs) in the large finite-field-size regime under the i.i.d. symbol erasure channel models. Two important scenarios are analyzed: (i) tradeoff between decoding deadline Δ and probability of errorpeassuming infinite memory α = ∞; and (ii) tradeoff between α andpeassuming infinite Δ = ∞. For each scenario, this work derives the corresponding asymptotic constant ρ, power β and decay rate η that satisfype(x) ∼ ρxβe−ηx. The results of (i) and (ii) are then used to study an important code design problem: Under a given target deadline Δ, what is the memory length α needed for the error probabilitypeto be within a factor ofc> 1 of the best possiblep∗eover α. Further analysis also suggests that regardless thecvalue being considered, the necessary memory length is approximately 3–7% of the target deadline Δ when Δ is large, the actual percentage depending on the channel model and the coding rate. Such a prediction is consistent with existing brute-force-based evaluations.

In this paper, the heterogeneous distributed quickest change detection (HetDQCD) with 1-bit non-anonymous feedback is studied. The concept of syndromes is introduced and the family of syndrome-based fusion rules is proposed, which encompasses all deterministic fusion rules as special cases. Through the Hasse diagram of syndromes, upper and lower bounds on the second-order performance of expected detection delay as a function of average run length to false alarm are provided. An interesting instance, the weighted voting rule previously proposed in our prior work, is then revisited, for which an efficient pruning method for breadth-first search in the Hasse diagram is proposed to analyze the performance. This in turn assists in the design of the weight threshold in the weighted voting rule. Simulation results corroborate that our analysis is instrumental in identifying a proper design for the weighted voting rule, demonstrating consistent superiority over both the anonymous voting rule and the group selection rule in HetDQCD.

This paper explores differentially-private federated learning (FL) across time-varying databases, delving into a nuanced three-way tradeoff involving age, accuracy, and differential privacy (DP). Emphasizing the potential advantages of scheduling, we propose an optimization problem aimed at meeting DP requirements while minimizing the loss difference between the aggregated model and the model obtained without DP constraints. To harness the benefits of scheduling, we in-troduce an age-dependent upper bound on the loss, leading to the development of an age-aware scheduling design. Simulation results underscore the superior performance of our proposed scheme compared to FL with classic DP, which does not consider scheduling as a design factor. This research contributes insights into the interplay of age, accuracy, and DP in FL, with practical implications for scheduling strategies.

In this paper, by considering the effect of error correcting codes in addition to collision resolution, a novel probabilistic density-evolution analysis of the irregular repetition slotted ALOHA (IRSA) is proposed. Simulation results confirm that the proposed extension analysis can accurately recover the efficiency of the iterative successive interference cancellation (iSIC) scheme for a satellite Internet-of-Things (IoT) system endowed with an error correcting code, and therefore can be used to determine the corresponding optimal degree distributions.

To mitigate the performance degradation caused by channel aging in massive multi-input multi-output (MIMO) systems, channel prediction is investigated in this paper. Based on the existing vector Prony method (VPM) and the Prony-based angular-delay domain (PAD) prediction, two novel channel prediction methods, referred to as the modified VPM (MVPM) and the modified PAD (MPAD), are proposed. In the proposed methods, we decouple the model size from the number of past channel estimates that are involved in the prediction of the future channels, allowing more flexible usage of channel estimates. Simulations demonstrate that when the number of past channel estimates becomes large, the proposed MVPM and MPAD significantly outperform VPM and PAD, respectively. Complexity analysis shows that this improvement in performance comes with a slight increase in computational complexity.

Reconfigurable Intelligent Surfaces (RIS) are perceived as cost-effective entities capable of expanding network coverage and enhancing system performance in 6G networks. Considering an RIS-assisted 6G millimeter network, this paper aims to find low-complexity beam and power allocations that maximize the network throughput. To resolve the complexity of such a mixed-integer non-convex programming problem, an iterative power decremental method is developed to solve the beam and power allocation problem alternatively under the system design constraints, making use of the concept of difference of convex functions and the zeroth-order gradient ascent method. Simulation results verify the superiority of the proposed method and its effectiveness in RIS-assisted millimeter networks.

In the federated training of a deep neural network (DNN), model updates are transmitted from the remote users to the parameter server (PS). In many scenarios of practical relevance, one is interested in reducing the communication overhead to enhance training efficiency. To address this challenge, we introduce$\textsf {CO}_{3}$.$\textsf {CO}_{3}$takes its name from three processing applied which reduce the communication load when transmitting the local DNN gradients from the remote users to the PS. Namely, 1) gradient quantization through floating-point conversion; 2) lossless compression of the quantized gradient; and 3) correction of quantization error. We carefully design each of the steps above to ensure good training performance under a constraint on the communication rate. In particular, in steps 1) and 2), we adopt the assumption that DNN gradients are distributed according to a generalized normal distribution, which is validated numerically in this article. For step 3), we utilize an error feedback with a memory decay mechanism to correct the quantization error introduced in step 1). We argue that the memory decay coefficient –similar to the learning rate—can be optimally tuned to improve convergence. A rigorous convergence analysis of the proposed$\textsf {CO}_{3}$with stochastic gradient descent (SGD) is provided. Moreover, with extensive simulations, we show that$\textsf {CO}_{3}$offers improved performance as compared with existing gradient compression schemes proposed in the literature which employ sketching and nonuniform quantization of the local gradients.

The problem of computing batches of matrix multiplications in distributed computing systems with stragglers is studied. Unlike existing works in the literature, the matrices in a batch are assumed to be sparse, and the sparsity levels for matrices in different batches can be different. A novel coding scheme, called generalized sparse code (GSC), is proposed, in which the matrices are partitioned into smaller chunks that are re- grouped and encoded by respective sparse codes. The expected runtime of the proposed GSC scheme is analyzed, based on which a task assignment problem associated with the proposed GSC is formulated and solved. The solution follows the reverse water-filling principle, by which an efficient worker assignment algorithm whose worst-case time complexity equal to the total number of workers can be developed. Simulation results validate the advantage of the proposed GSC over four existing schemes, including entangled polynomial codes (EP), generalized cross-subspace alignment (GCSA), Lagrange coded computing (LCC) codes and factored Luby transform (FLT) codes at all sparsity levels. As a potential application of the proposed GSC, the problem of computing a batch of matrix multiplications with similarity is discussed.

The problem of enabling the coexistence of heterogeneous services, e.g., different ultra-reliable low-latency communications (URLLC) services and/or enhanced mobile broadband (eMBB) services, in the uplink is studied. Each service has its own error probability and blocklength constraints and the longer transmission block suffers from heterogeneous interference. Due to the latency concern, the decoding of URLLC messages cannot leverage successive interference cancellation (SIC) and should always be performed before the decoding of eMBB messages. This can significantly degrade the achievable rates of URLLC users when the interference from other users is strong. To overcome this issue, we propose a new transmission scheme based on discrete signaling and treating interference as noise decoding, i.e., without SIC. Guided by the deterministic model, we provide a systematic way to construct discrete signaling for handling heterogeneous interference effectively. We demonstrate theoretically and numerically that the proposed scheme can perform close to the benchmark scheme based on capacity-achieving Gaussian signaling with the assumption of perfect SIC.

In this paper, we consider the downlink broadcast channel under heterogenous blocklength constraints, where each user experiences different interference statistics across its received symbols. Different from the homogeneous blocklength case, the strong users with short blocklength transmitted symbol blocks usually cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) owing to their stringent latency requirements. Even if SIC is feasible, it may not be perfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme with discrete signaling and single-user decoding, i.e., without SIC. In addition, we derive the finite blocklength achievable rate and use it for guiding the design of channel coding and modulations. Both achievable rate and error probability simulation show that the proposed scheme can operate close to the benchmark scheme which assumes capacity-achieving signaling and perfect SIC.

The generative adversarial network (GAN) aims to approximate an unknown distribution via a parameterized neural network (NN). While GANs have been widely applied in reinforcement and semi-supervised learning as well as computer vision tasks, selecting their parameters often needs an exhaustive search and only a few selection methods can be proved to be theoretically optimal. One of the most promising GAN variants is the Wasserstein GAN (WGAN). Prior work on optimal parameters for WGAN is limited to the linear-quadratic-Gaussian (LQG) setting, where the NN is linear and the data is Gaussian. In this paper, we focus on the characterization of optimal WGAN parameters beyond the LQG setting. We derive closed-form optimal parameters for one-dimensional WGANs with non-linear sigmoid and ReLU activation functions. Extensions to high-dimensional WGANs are also discussed. Empirical studies show that our closed-form WGAN parameters have good convergence behavior with data under both Gaussian and Laplace distributions.

The heterogeneous distributed quickest change detection (HetDQCD) problem with 1-bit feedback is studied, in which a fusion center monitors an abrupt change through a bunch of heterogeneous sensors via anonymous 1-bit feedbacks. Two fusion rules, one-shot and voting rules, are considered. We analyze the performance in terms of the worst-case expected detection delay and the average run length to false alarm for the two fusion rules. Our analysis unveils the mixed impact of involving more sensors into the decision and enables us to find near optimal choices of parameters in the two schemes. Notably, it is shown that, in contrast to the homogeneous setting, the first alarm rule may no longer lead to the best performance among one-shot schemes. The non-anonymous setting is then investigated where a novel weighted voting rule is proposed that assigns different weights to votes from different types of sensors. Simulation results show that the proposed scheme is able to outperform all the above schemes and the mixture CUSUM scheme for the anonymous HetDQCD, hinting at the price of anonymity.

Streaming codes eliminate the queueing delay and are an appealing candidate for low latency communications. This work studies the tradeoff between error probability peand decoding deadline ∆ of infinite-memory random linear streaming codes (RLSCs) over i.i.d. symbol erasure channels (SECs). The contributions include (i) Proving pe(∆) ∼ ρ∆−1.5e−η∆. The asymptotic power term ∆−1.5of RLSCs is a strict improvement over the ∆−0.5term of random linear block codes; (ii) Deriving a pair of upper and lower bounds on the asymptotic constant ρ, which are tight (i.e., identical) for one specific class of SECs; (iii) For any c > 1 and any decoding deadline ∆, the c-optimal memory length $\alpha _c^{\ast}(\Delta )$ is defined as the minimal memory length α needed for the resulting peto be within a factor of c of the best possible $p_e^{\ast}$ under any α, an important piece of information for practical implementation. This work studies and derives new properties of $\alpha _c^{\ast}(\Delta )$ based on the newly developed asymptotics.

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

The problem of designing downlink transmission schemes for supporting heterogeneous ultra-reliable low-latency communications (URLLC) and/or with other types of services is investigated. We consider the broadcast channel, where the base station sends superimposed signals to multiple users. Under heterogeneous blocklength constraints, strong users who are URLLC users cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) due to stringent latency requirements, in contrast to the conventional infinite blocklength cases. Even if SIC is feasible, SIC may be imperfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme withdiscrete signalingandsingle-user decoding (SUD), i.e., without SIC. We carefully design the discrete input distributions to enable efficient SUD by exploiting the structural interference. Furthermore, we derive the second-order achievable rate under heterogenous blocklength and error probability constraints and use it to guide the design of channel coding and modulations. It is shown that in terms of achievable rate under short blocklength, the proposed scheme with regular quadrature amplitude modulations and SUD can operateextremely closeto the benchmark schemes that assume perfect SIC with Gaussian signaling.

The problem of fully distributed sequential change detection (FDSCD) is studied, where a bunch of distributed sensors collaboratively detect the occurrence of an abrupt event without the help of a fusion center. Due to the distributed nature, each sensor determines whether the event has occurred according to their own observations and messages received from their neighbors through noiseless links with finite capacity. In this paper, a novel algorithm is proposed for FDSCD that views every sensor as a fusion center and mimics the behavior of the cumulative sum (CUSUM) algorithm that is optimal in the presence of fusion center. One crucial factor in the proposed algorithm is the weighting factors that reduce the influence of stale messages circulated in the network. Design of these weighting factors based on genetic algorithms is provided. Quantizer design is also studied to meet the finite capacity constraint. Two designs are proposed where the first one numerically maximizes the total output Kullback-Leibler divergence and the second one again exploits the genetic algorithms. Extensive simulations are conducted whose results show that the proposed algorithm outperforms the current state-of-art in terms of the tradeoff between expected delay and average run length to a false alarm.

Age of information (AoI) is an effective performance metric measuring the freshness of information and is particularly suitable for applications involving status update. In this paper, using the age violation probability as the metric, scheduling for heterogeneous multi-source systems is studied. Two queueing disciplines, namely the infinite packet queueing discipline and the single packet queueing discipline, are considered for scheduling packets within each source. A generalized round-robin (GRR) scheduling policy is then proposed to schedule the sources. Bounds on the exponential decay rate of the age violation probability for the proposed GRR scheduling policy under each queueing discipline are rigorously analyzed. Simulation results are provided, which show that the proposed GRR scheduling policy can efficiently serve many sources with heterogeneous arrivals and that our bounds can capture the true decay rate quite accurately. When specialized to the homogeneous source setting, the analysis concretizes the common belief that the single packet queueing discipline has a better AoI performance than the infinite packet queueing discipline. Moreover, simulations on this special case reveals that under the proposed scheduling policy, the two disciplines would have similar asymptotic performance when the inter-arrival time is much larger than the total transmission time.

Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems.Coded computingis a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on- demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept calledtransition wastethat quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). Finally, we establish a novel solution based on finite geometry achievingzerotransition wastes given that the number of active machines varies within a fixed range.

In this paper, the problem of optimal gradient lossless compression in Deep Neural Network (DNN) training is considered. Gradient compression is relevant in many distributed DNN training scenarios, including the recently popular federated learning (FL) scenario in which each remote users are connected to the parameter server (PS) through a noiseless but rate limited channel. In distributed DNN training, if the underlying gradient distribution is available, classical lossless compression approaches can be used to reduce the number of bits required for communicating the gradient entries. Mean field analysis has suggested that gradient updates can be considered as independent random variables, while Laplace approximation can be used to argue that gradient has a distribution approximating the normal (Norm) distribution in some regimes. In this paper we argue that, for some networks of practical interest, the gradient entries can be well modelled as having a generalized normal (GenNorm) distribution. We provide numerical evaluations to validate that the hypothesis GenNorm modelling provides a more accurate prediction of the DNN gradient tail distribution. Additionally, this modeling choice provides concrete improvement in terms of lossless compression of the gradients when applying classical fix-to-variable lossless coding algorithms, such as Huffman coding, to the quantized gradient updates. This latter results indeed provides an effective compression strategy with low memory and computational complexity that has great practical relevance in distributed DNN training scenarios.

Channel dispersion quantifies the convergence speed of coding rate to channel capacity under different latency constraints. Under the setting of packet erasure channels (PECs) with Bernoulli packet arrivals, this work characterizes the channel dispersions of random linear streaming codes (RLSCs) and MDS block codes, respectively. New techniques are developed to quantify the channel dispersion of sequential (non-block-based) coding, the first in the literature. The channel dispersion expressions are then used to compare the levels of error protection between RLSCs and MDS block codes. The results show that if and only if the target error probability peis smaller than a threshold (≈0.1774), RLSCs offer strictly stronger error protection than MDS block codes, which is on top of the already significant 50% latency savings of RLSCs that eliminate the queueing delay completely.

Streaming codestake a string of source symbols as input and output a string of coded symbols in real time, which eliminate the queueing delay of traditionalblock codesand are thus especially appealing for delay sensitive applications. Existing works on streaming code performance either focused on the asymptotic error-exponent analyses, or on the optimal code construction underdeterministic adversarial channel models. In contrast, this work analyzes the exact error probability ofrandom linear streaming codes(RLSCs) in the large field size regime over the stochastic i.i.d. symbol erasure channel model. A closed-form expression of the error probability of large-field-size RLSCs is derived under, simultaneously, the finite memory length and decoding deadline constraints. The result is then used to examine the intricate tradeoff between memory length (complexity), decoding deadline (delay), code rate (throughput), and error probability (reliability). Numerical evaluation shows that under the same code rate and error probability requirements, the end-to-end delay of RLSCs is 40–48% of that of the optimal block codes (i.e., MDS codes). This implies that switching from block codes to streaming codes not only eliminates the queueing delay completely (which accounts for the initial 50% of the delay reduction) but also improves the reliability (which accounts for the additional 2–10% delay reduction).

The problem of computing the modulo-sum of independent messages over a finite-field erasure multiple access channel is investigated. The focus is on the role of delayed state feedback for function computation over state dependent multiple access channels. For the two-user case, a set of new outer bounds on the non-feedback computation capacity region are developed, which strictly improve the state of the art by Khistiet al., 2013. As a result, a previously unsettled question is answered in the affirmative: delayed state feedback strictly increases computation capacity for the two-user erasure multiple access channel universally. The proof leverages the subset entropy inequalities by Madiman and Tetali, 2010, Jianget al., 2014, and submodularity of conditional entropies. For the achievability part of the non-feedback case, an achievable computation rate region is derived by generalizing the proposed schemes in Khistiet al., 2013. Beyond the two-user case, the$K$-user case is also investigated, with emphasis on the large system regime, where$K$is the number of users. For the non-feedback case, we propose a grouping scheme which has higher computation rate than that of the conventional “compute-and-forward” (CF) scheme. Furthermore, with a growing number of users, the proposed grouping scheme strictly outperforms the conventional “decode-and-forward (DF)” scheme when the erasure probability is smaller than$1-e^{\frac {1}{e}}\approx 0.3078$. This is in contrast to the two-user case where the currently best known achievability (Khistiet al., 2013) coincides with the better one betweenDFandCF. For the case with delayed state feedback, a new hybrid-ARQ-type scheme is proposed, and in the large system regime, it achieves a computation rate scaling like$\Omega \left({\frac {1}{\log (K)}}\right)$, much higher than the scaling$\Theta \left({\frac {1}{K}}\right)$achieved by the grouping scheme without feedback.

This paper investigates the role of dimensionality reduction in efficient communication and differential privacy (DP) of the local datasets at the remote users for over-the-air computation (AirComp)-based federated learning (FL) model. More precisely, we consider the FL setting in which clients are prompted to train a machine learning model by simultaneous channel-aware and limited communications with a parameter server (PS) over a Gaussian multiple-access channel (GMAC), so that transmissions sum coherently at the PS globally aware of the channel coefficients. For this setting, an algorithm is proposed based on applying (i) federated stochastic gradient descent (FedSGD) for training the minimum of a given loss function based on the local gradients, (ii) Johnson-Lindenstrauss (JL) random projection for reducing the dimension of the local updates and (iii) artificial noise to further aid user's privacy. For this scheme, our results show that the local DP (LDP) performance is mainly improved due to injecting noise of greater variance on each dimension while keeping the sensitivity of the projected vectors unchanged. This is while the convergence rate is slowed down compared to the case without dimensionality reduction. As the performance outweighs for the slower convergence, the trade-off between privacy and convergence is higher but is shown to lessen in high-dimensional regime yielding almost the same trade-off with much less communication cost.

Motivated by the applications for low-delay communication networks, the finite-length analysis, or channel dispersion identification, of the multi-user channel is very important. Recent studies also incorporate the effects of feedback in point-to-point and common-message broadcast channels (BCs). However, with private messages and feedback, finite-length results for BCs are much more scarce. Though it is known that feedback can strictly enlarge the capacity, the ultimate feedback capacity regions remain unknown for even some classical channels including Gaussian BCs. In this work, we study the two-user broadcast packet erasure channel (PEC) with causal feedback, which is one of the cleanest feedback capacity results and the capacity region can be achieved by elegant linear network coding (LNC). We first derive a new finite-length outer bound for any LNCs and then accompanying inner bound by analyzing a three-phase LNC. For the outer-bound, we adopt a linear-space-based framework, which can successfully find the LNC capacity. However, naively applying this method in finite-length regime will result in a loose outer bound. Thus a new bounding technique based on carefully labelling each time slot according to the type of LNC transmitted is proposed. Simulation results show that the sum-rate gap between our inner and outer bounds is within 0.02 bits/channel use. Asymptotic analysis also shows that our bounds bracket the channel dispersion of LNC feedback capacity for broadcast PEC to within a factor of Q-l (E/2)/Q-l (E).

Streaming codes take a string of source symbols as input and output a string of coded symbols in real time, which effectively eliminate the queueing delay and are regarded as a promising scheme for low latency communications. Aiming at quantifying the fundamental latency performance of random linear streaming codes (RLSCs) over i.i.d. symbol erasure channels, this work derives the exact error probability under, simultaneously, the finite memory length and finite decoding deadline constraints. The result is then used to examine the tradeoff among memory length (complexity), decoding deadline (delay), and error probability (reliability) of RLSCs for the first time in the literature. Two critical observations are made: (i) Too much memory can adversely impact the performance under a finite decoding deadline constraint, a surprising finding not captured by the traditional wisdom that large memory length monotonically improves the performance in the asymptotic regime; (ii) The end-to-end delay of the RLSC is roughly 50% of that of the MDS block code when under identical code rate and error probability requirements. This implies that switching from block codes to RLSCs not only eliminates the queueing delay (thus 50%) but also has little negative impact on the error probability.

With the recent emergence of many low-latency applications over wireless networks, the need for accurate finite-length analysis of channel coding over multi-user wireless channels is ever increasing. This paper focuses exclusively on the two-user broadcast packet erasure channel (PEC) with causal feedback, for which existing results show that various linear network coding (LNC) schemes can attain the broadcast capacity region when the block length approaches infinity. Instead of the asymptotic capacity-based analysis, this work derives the exact value of the LNC-based broadcast channel dispersion. Our approach is based on a new explicit characterization of the optimal LNC scheme under any arbitrarily given finite block length. The results show that among all existing asymptotically capacity-achieving LNC schemes, one (class) of them is provably finite-length optimal. By analyzing its second-order asymptotic, we have thus derived the exact (optimal) LNC broadcast channel dispersion, which closes the gap of the state-of-the-art inner and outer bounds previously derived in Lin et al. ISIT 2021

In this paper, the problem of using uncoordinated multiple access (UMA) to serve a massive amount of heterogeneous users is investigated. Leveraging the heterogeneity, we propose a novel UMA protocol, called iterative collision resolution for slotted ALOHA (IRSA) with non-orthogonal multiple access (NOMA), to improve the conventional IRSA. In addition to the inter-slot successive interference cancellation (SIC) technique used in existing IRSA-based schemes, the proposed protocol further employs the intra-slot SIC technique that enables collision resolution for certain configurations of collided packets. A novel multi-dimensional density evolution is then proposed to analyze and to optimize the proposed protocol. Simulation results show that the proposed IRSA with NOMA protocol can efficiently exploit the heterogeneity among users and the multi-dimensional density evolution can accurately predict the throughput performance. Last, an extension of the proposed IRSA with NOMA protocol to the frame-asynchronous setting is investigated, where a boundary effect similar to that in spatially-coupled low-density parity check codes can be observed to bootstrap the decoding process.

The Byzantine distributed quickest change detection (BDQCD) is studied, where a fusion center monitors the occurrence of an abrupt event through a bunch of distributed sensors that may be compromised. We first consider the binary hypothesis case where there is only one post-change hypothesis and prove a novel converse to the first-order asymptotic detection delay in the large mean time to a false alarm regime. This converse is tight in that it coincides with the currently best achievability shown by Fellouris et al.; hence, the optimal asymptotic performance of binary BDQCD is characterized. An important implication of this result is that, even with compromised sensors, a 1-bit link between each sensor and the fusion center suffices to achieve asymptotic optimality. To accommodate multiple post-change hypotheses, we then formulate the multi-hypothesis BDQCD problem and again investigate the optimal first-order performance under different bandwidth constraints. A converse is first obtained by extending our converse from binary to multi-hypothesis BDQCD. Two families of stopping rules, namely the simultaneous d-th alarm and the multi-shot d-th alarm, are then proposed. Under sufficient link bandwidth, the simultaneous d-th alarm, with d being set to the number of honest sensors, can achieve the asymptotic performance that coincides with the derived converse bound; hence, the asymptotically optimal performance of multi-hypothesis BDQCD is again characterized. Moreover, although being shown to be asymptotically optimal only for some special cases, the multi-shot d-th alarm is much more bandwidth-efficient and energy-efficient than the simultaneous d-th alarm. Built upon the above success in characterizing the asymptotic optimality of the BDQCD, a corresponding leader-follower Stackelberg game is formulated and its solution is found.

The two-user Gaussian interference channel (G-IC) is revisited, with a particular focus on practically amenable discrete input signalling and treating interference as noise (TIN) receivers. The corresponding deterministic interference channel (D-IC) is first investigated and coding schemes that can achieve the entire capacity region of the D-IC under TIN are proposed. These schemes are thensystematicallytranslated into multi-layer superposition coding schemes based on purely discrete inputs for the real-valued G-IC. Our analysis shows that the proposed scheme is able to achieve theentirecapacity region to within a constant gap for all channel parameters. To the best of our knowledge, this is the first constant-gap result under purely discrete signalling and TIN for the entire capacity region and all the interference regimes. Furthermore, the approach is extended to obtain coding schemes based on discrete inputs for the complex-valued G-IC. For such a scenario, the minimum distance and the achievable rate of the proposed scheme under TIN are analyzed, which takes into account the effects of random phase rotations introduced by the channels. Simulation results show that our scheme is capable of approaching the capacity region of the complex-valued G-IC and significantly outperforms Gaussian signalling with TIN in various interference regimes.

This article introduces an innovative model-based strategy for designing a legged robot to generate animal-like running dynamics with differentiated leg braking and thrusting force patterns. Linear springs were utilized as legs, but instead of having one end of each spring connected directly to the hip joint, one extra bar was added to offset the spring's direction. The robot's front and hind legs were offset with the same magnitudes but in different directions. Therefore, the legs produced different ground braking and thrusting force patterns. The robot's running motion was planned based on its reduced-order model. The model's fixed-point and passive-dynamics motion served as the robot's reference motion. The proposed strategy was experimentally validated, and the results confirmed that the robot could successfully perform stable running in a differentiated leg force pattern.

In this paper, we study the achievable rate performance and the design of using purely discrete input signaling and treating interference as noise (TIN) for the two-user complex Gaussian interference channel (G-IC), where the channel introduces random phase rotation for all links. To analyze the achievable rate performance under this scenario, we first look into the corresponding deterministic interference channel model and design schemes to achieve the entire capacity region under TIN. Then, we translate the scheme into a multi-layer superposition coding scheme based on discrete inputs for GIC and analyze the achievable rate under TIN. Our simulation results show that our scheme is capable of approaching the (outer bound of) capacity region of the complex G-IC and performs significantly better than Gaussian signalling with TIN.

Motivated by recently available services in the cloud computing industry, e.g., EC2 Spot or Azure Batch, where spare/low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice, we investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. Our contributions are two-fold: We first propose an efficient method to minimize the transition waste, a newly introduced concept quantifying the total number of tasks that existing machines have to abandon or take on anew when a machine joins or leaves, for the cyclic elastic task allocation scheme recently proposed in the literature (Yang et al. ISIT'19). We then proceed to generalize such a scheme and introduce new task allocation schemes based on finite geometry that achieve zero transition wastes as long as the number of active machines varies within a fixed range. The proposed solutions can be applied on top of existing coded computing schemes tolerating stragglers.

Streaming codes encode a string of source packets and output a string of coded packets in real time, which eliminate the queueing delay of block coding and are thus especially suitable for delay-sensitive applications. This work studies random linear streaming codes (RLSCs) and i.i.d. packet erasure channels. While existing works focused on the asymptotic error-exponent analyses, this work characterizes the error rate in the finite memory length regime and the contributions include: (i) A new information-debt-based description of the error event; (ii) A matrix-based characterization of the error rate; (iii) A closed-form approximation of the error rate that is provably tight for large memory lengths; and (iv) A new Markov-chainbased analysis framework, which can be of independent research interest. Numerical results show that the approximation, i.e. (iii), closely matches the exact error rate even for small memory length (≈ 20). The results can be viewed as a sequential- coding counterpart of the finite length analysis of block coding [Polyanskiy et al. 10] under the specialized setting of RLSCs.

We consider a distributed computing network consisting of a master and multiple workers processing tasks of different types. The master is running multiple applications. Each application stochastically generates real-time jobs with a strict job deadline, where each job is a collection of tasks of some types specified by the application. A real-time job is completed only when all its tasks are completed by the corresponding workers within the deadline. Moreover, we consider unreliable workers, whose processing speeds are uncertain. Because of the limited processing abilities of the workers, an algorithm for scheduling the jobs in the workers is needed to maximize the average number of completed jobs for each application. The scheduling problem is not only critical but also practical in distributed computing networks. In this paper, we develop two scheduling algorithms, namely, a feasibility-optimal scheduling algorithm and an approximate scheduling algorithm. The feasibility-optimal scheduling algorithm can fulfill the largest region of applications' requirements for the average number of completed jobs. However, the feasibility-optimal scheduling algorithm suffers from high computational complexity when the number of applications is large. To address the issue, the approximate scheduling algorithm is proposed with a guaranteed approximation ratio in the worst-case scenario. The approximate scheduling algorithm is also validated in the average-case scenario via computer simulations.

In this paper, we study the communication problem of multiuser multiple-input single-output (MISO) broadcast channels with imperfect channel state information (CSI) at the transmitter. Zero-forcing precoding based on the imperfect CSI is adopted so that the channel can be transformed into a Gaussian interference channel. We consider a practical setting where only discrete input signalings are employed and all the receivers adopt single-user treating-interference-as-noise (TIN) decoding, as opposed to rate-splitting and successive interference cancellation. Under this setting, we first use the deterministic model to approximate the original channel model and develop communication schemes to achieve the entire capacity region. By translating the results of the deterministic model back to the MISO model, we develop a systematic way to design discrete input signalings for the original problem. Our simulation results show that our scheme is capable of approaching the (outer bound of) capacity region of the Gaussian interference channel.

A fully distributed version of sequential change detection is studied, where a bunch of distributed sensors tries to collaboratively detect the occurrence of an abrupt event as quickly as possible in the absence of a fusion center. Every link connecting two nodes is subject to a finite bandwidth constraint. A novel stopping rule is proposed where each sensor computes a modified version of the well-known cumulative sum (CUSUM) statistic, exchanges a quantized version of this modified CUSUM statistic with its neighbors via links with a finite bandwidth, and then decides its view of whether the event has occurred. Using the trade-off between the worst-case expected detection delay (EDD) and average run length (ARL) of a false alarm as the performance metric, the proposed algorithm is shown, via extensive simulations, to outperform the current state-of-the-art based on average consensus. Moreover, the proposed stopping rule can adjust the quantization levels to comply with the bandwidth constraint and only exchanges information occasionally, which is more energy- efficient and much more spectrum-efficient than the one based on average consensus requiring infinite bandwidth and constantly exchanging information.

The problem of downlink non-orthogonal multiple access (NOMA) scheme over fast fading channels is studied. A new class of downlink NOMA scheme is proposed, where each user's signals are encoded to a constellation corresponding to the same algebraic lattices from number fields and the transmitter sends the superposition of users' signals. The minimum product distance achieved by the proposed scheme with an arbitrary power allocation factor is investigated and its upper bounds are derived. Within this class, a family of NOMA schemes based on lattice partitions of the underlying ideal lattice is identified, whose minimum product distances can be easily controlled. Numerical results show that the scheme based on lattice partitions always results in the largest possible minimum product distance among the proposed class. Simulation results further indicate that the proposed scheme significantly outperforms the conventional NOMA scheme and the current state-of-the-art.

Sequential change point detection with multiple decentralized sensors is studied. Each sensor makes a local decision based on its own observations and reports it through a bandlimited link to a fusion center, which then decides whether the change has occurred. Since sensors in many applications such as cyber-physical systems are prone to a number of attacks such as Byzantine attacks, combating such a security breach becomes one of the most crucial issues. Previous works on sequential change detection under Byzantine attacks only focus on binary-hypothesis case, which significantly limits the applicability. In this paper, we consider the extension to the multi-hypothesis setting. We show that naively extending the existing method from the binary case to the multi-hypothesis one can result in a catastrophic event preventing the fusion center from making a conclusive decision. Thus we propose the other two new methods by allowing each sensor to cast multiple local alarms, and both can avoid this catastrophic event and improve the asymptotic detection delay. In analyzing detection delays of our multi-hypothesis schemes, we also show that for each hypothesis, asymptotically, it suffices to focus on the competing hypothesis that is closest in Kullback-Leibler distance. Through large sensor analysis, we also show that as the number of honest sensors grows, one of the proposed scheme, called the simultaneous rule, approaches the optimal performance within a factor of 2.

The Byzantine distributed sequential change detection (BDSCD) problem is studied, where a fusion center monitors an abrupt event occurring at an unknown time through a bunch of distributed sensors. It is assume that a part of the sensors are compromised and each sensor, honest or compromised, communicates with the fusion center via a noiseless link. A new converse for this problem is presented whose first-order asymptotic delay subject to a certain false alarm rate coincides with the currently best known result achieved by the consensus rule proposed by Fellouris et al. This result characterizes the first-order asymptotic performance of BDSCD and shows that 1-bit links suffice to achieve the asymptotic optimality. The proof of the converse involves constructing an attack strategy, called the reverse attack, introducing a genie that gives the fusion center the identities of a subset of honest sensors and observations at each sensor used for generating its local report, and transforming the problem into an equivalent non-Byzantine sequential change detection but with reduced number of honest sensors.

In this paper, the problem of downlink non-orthogonal multiple access (NOMA) over slow fading channels is studied. Full-channel state information (CSI) is assumed at the receivers, while only the statistical CSI is assumed to be available at the transmitter. A novel lattice-partition-based scheme is proposed which, according to statistical CSI, employs discrete inputs from appropriately designed constellations carved from a lattice, rather than continuous Gaussian inputs as used in most existing works. Theoretical analysis shows that for any outage probability smaller than 63.21%, which covers almost all the cases of practical interest, the proposed scheme with single-user decoding, i.e., without successive interference cancellation (SIC) is able to approach the NOMA outage capacity region within a constant gap, independent of the signal-to-noise ratio, and the number of users. Simulation results fortify the effectiveness of the proposed scheme by showing that the approach without SIC can achieve outage rates that are very close to the outage capacity region and the gap becomes even smaller when SIC is employed.

Multicasting K independent messages via multipleinput multiple-output channels to multiple users where each user already has a subset of messages as side information is studied. A general framework of constructing layered space-time index coding (LSTIC) from a large class of space-time block codes (STBC), including perfect STBC, is proposed. We analyze the proposed LSTIC and show that it provides minimum determinant gains that are exponential with the amount of information contained in the side information for any possible side information. When constructed over a perfect STBC, the proposed LSTIC is itself a perfect STBC and hence many desired properties are preserved. To illustrate, we construct LSTIC over the following wellknown STBCs: Golden code; 3×3, 4×4, and 6×6 perfect STBCs; and Alamouti code. Simulation results show that the obtained side information gain can be well predicted by our analysis.

The fifth-generation (5G) air interface, namely, dynamic multiple access (MA) based on multiuser superposition transmission (MUST) and orthogonal MA (OMA), may require a complicated scheduling and heavy signaling overhead. To address these challenges, we propose a unified MA scheme for future cellular networks, which we refer to as structured MUST (S-MUST). In S-MUST, we apply complex power allocation coefficients (CPACs) over multiuser legacy constellations to generate a composite constellation. In particular, the in-phase (I) and quadrature (Q) components of the legacy constellation of each user are separately multiplied by those of the CPACs. As such, the CPACs offer an extra degree of freedom for multiplexing users and guarantee fairness in symmetric broadcast channels. This new paradigm of superposition coding allows us to design IQ separation at the user side, which significantly reduces the decoding complexity without degrading performance. Hence, it supports low-complexity frequency-selective scheduling that does not entail dynamical switching between MUST and OMA. We further propose to quantize the CPACs into complex numbers where I and Q components of each quantized coefficient are primes, facilitating parallel interference cancellation at each user via modulo operations; last but not least, we generalize the design of S-MUST to exploit the capabilities of multiantenna base stations. The proposed S-MUST exhibits an improved user fairness with respect to conventional MUST (134% spectral efficiency enhancement) and a lower system complexity compared with dynamically alternating MUST and OMA.

In this paper, we investigate the problem of downlink non-orthogonal multiple access (NOMA) over block fading channels. For the single antenna case, we propose a class of NOMA schemes where all the users' signals are mapped into n-dimensional constellations corresponding to the same algebraic lattices from a number field, allowing every user attains full diversity gain with single-user decoding, i.e., no successive interference cancellation (SIC). The minimum product distances of the proposed scheme with arbitrary power allocation factor are analyzed and their upper bounds are derived. Within the proposed class of schemes, we also identify a special family of NOMA schemes based on lattice partitions of the underlying ideal lattices, whose minimum product distances can be easily controlled. Our analysis shows that among the proposed schemes, the lattice-partition-based schemes achieve the largest minimum product distances of the superimposed constellations, which are closely related to the symbol error rates for receivers with single-user decoding. The simulation results are presented to verify our analysis and to show the effectiveness of the proposed schemes as compared to benchmark NOMA schemes. Extensions of our design to the multi-antenna case are also considered where similar analysis and results are presented.

In this paper, we develop a lattice-partition-based downlink non-orthogonal multiple access (NOMA) scheme for slow fading channels without successive interference cancellation (SIC) at the receivers. With the knowledge of statistical channel state information at the transmitter, our scheme uses a finite constellation drawn from an n-dimensional lattice and employs channel coding on top of it. The outage rates achieved by our scheme without SIC are analyzed and their gaps to the multiuser outage capacity are derived. We show, both theoretically and numerically, that our scheme without SIC is capable of approaching any point in the multiuser outage capacity region within a constant gap when the required outage probability is smaller than 63.21%, which covers almost all cases of practical interest. Simulation results based on various lattices are provided and demonstrate that the near-capacity performance can be attained by our NOMA scheme without SIC.

Multicasting K independent messages via multiple-input multiple-output (MIMO) channels to multiple users where each user already has a subset of messages as side information is studied. A general framework of constructing layered spacetime index coding (LSTIC) from a large class of space-time block codes (STBCs), including perfect STBCs, is proposed. We analyze the proposed LSTIC technique and show that it provides minimum determinant gains that are exponential in the amount of information contained in the side information for any possible side information at the receivers. When constructed over a perfect STBC, the proposed LSTIC is itself a perfect STBC and hence enjoys many desired properties.

Delay performance of downlink non-orthogonal multiple access (NOMA) networks is investigated. To fully realize advantages offered by NOMA, we need to consider more realistic network environment; as such, departing from the literature on NOMA, this paper relaxes the full-buffer assumption and allows each user in the network to have its individual delay-cost function. The former captures the sporadic nature of data arrivals in some applications, while the latter accepts potential coexistence of heterogeneous users. In this context, we propose three transmission scheduling algorithms, namely the MDP-based, the c- μ-based, and the learning-based scheduling algorithms. While the MDP-based scheduling algorithm is shown to be delay-optimal, the other two scheduling algorithms enjoy the online feature where transmitters are oblivious to arrival statistics. Moreover, it turns out that the c-μ-based scheduling algorithm is a greedy version of the MDP-based scheduling algorithm and the learning-based scheduling algorithm is asymptotically delay-optimal. Simulation results corroborate our theoretical analysis and fortify the common belief about the superiority of NOMA over OMA, even without the full-buffer assumption and with heterogeneous users.

In this paper, a novel lattice-partition-based downlink non-orthogonal multiple access framework is proposed. This framework is motivated by recognizing the algebraic structure behind the previous scheme recently proposed by Shieh and Huang as a lattice partition in Z and is in fact a generalization of the scheme to any base lattice. The schemes in the proposed framework enjoy many desirable properties such as explicit and systematic design and discrete input distributions. Moreover, the proposed method only requires a limited knowledge of channel parameters. The rates achieved by the proposed scheme with any base lattice and with single-user decoding (i.e., without successive interference cancellation) are analyzed, and a universal upper bound on the gap to the multiuser capacity is obtained as a function of the normalized second moment of the base lattice. Since the proposed framework has a substantially larger design space than that of the previous scheme of Shieh and Huang whose base lattice is a 1-D lattice, one can easily find instances in larger dimensions that can provide superior performance. Design examples with the base lattices A2, D4, E8, and Construction A lattices, respectively, are provided, and both theoretical and simulation results exhibit smaller gaps to the multiuser capacity as dimensions increase.

In this paper, we extend Construction A of lattices to the ring of algebraic integers of a general imaginary quadratic field that may not form a principal ideal domain (PID). We show that such a construction can produce good lattices for coding in the sense of Poltyrev and for MSE quantization. As an application, we then apply the proposed lattices to the compute-and-forward paradigm with limited feedback. Without feedback, compute-and-forward is typically realized with lattice codes over the ring of integers, the ring of Gaussian integers, or the ring of Eisenstein integers, which are all PIDs. A novel scheme called adaptive compute-and-forward is proposed to exploit the limited feedback about the channel state by working with the best ring of imaginary quadratic integers. Simulation results show that by adaptively choosing the best ring among the considered ones according to the limited feedback, the proposed adaptive compute-and-forward provides a better performance than that provided by the conventional compute-and-forward scheme which works over Gaussian or Eisenstein integers solely.

In this paper, downlink non-orthogonal multiple access (NOMA) with receivers performing single- user decoding i.e., without successive interference cancellation (SIC) is studied. Using lattice partitions, we generalize the scheme recently proposed by Shieh and Huang [1] to general n-dimensional constellations carved from lattices. The achievable rates of the proposed scheme without SIC and the gap to the capacity region are investigated. Design examples based on lattice partition chains in Z2, A2, and D4 are studied. Numerical and simulation results are provided, which demonstrate advantages of the proposed scheme over the one in [1] and any orthogonal multiple access scheme.

We study the problem of constructing good spacetime codes for broadcasting K independent messages over a MIMO network to L users, where each user demands all the messages and already has a subset of messages as side information. As a first attempt, we consider the 2 × 2 case and propose golden-coded index coding by partitioning the golden codes into K subcodes, one for each message. The proposed scheme is shown to have the property that for any side information configuration, the minimum determinant of the code increases exponentially with the amount of information contained in the side information.

The problem of computing the modulo-sum of messages over a finite-field erasure multiple access channel (MAC) is studied, and the role of feedback for function computation is explored. Our main contribution is two-fold. First, a new outer bound on the non-feedback computation capacity is proved, which strictly improves the state of the art [1]. The new outer bound answers a previously unsettled question in the affirmative: delayed state feedback strictly increases computation capacity for the two-user erasure MAC universally. The proof leverages the subset entropy inequality by Madiman and Tetali [2]. Second, focusing on the family of linear coding schemes with hybrid-ARQ-type retransmissions, we develop the optimal computation rate with delayed state feedback. For the considered family of schemes, it is always sub-optimal to compute modulo-sum by decoding all messages first. This is in contrast to the nonfeedback case [1] where sometimes the aforementioned “decode-all” strategy can reach the best known achievable rates.

Nested lattice codes have played an important role in network information theory. However, their achievability proofs are often involved, even for the case of the additive white Gaussian noise (AWGN) channel. In sharp contrast, their finite-field counterparts, nested linear codes, enjoy much simpler achievability proofs. In this paper, we present a simple and direct proof that nested lattice codes achieve the AWGN channel capacity. In particular, we make use of an intriguing connection between nested lattice codes and nested linear codes, which allows us to keep the proof as simple as that for nested linear codes.

The modulo-sum computation of messages over a K-user finite-field erasure multiple access channel (MAC) is studied, with emphasis on the role of feedback in the large system regime. For the non-feedback case, we propose a grouping scheme which has higher computation rate than that of the conventional “compute-and-forward” (CF) scheme where each transmitter uses the same linear code and the receiver leverages the additive structure of the multiple access channel to compute the modulo sum. Furthermore, with a growing number of users, the proposed grouping scheme strictly outperforms the conventional “decode-and-forward (DF)” scheme when the erasure probability is smaller than 1-e1/e≈ 0.3078, where the receiver first decodes messages of all users and then computes the modulo sum. This is in contrast to the two-user case where the currently best known achievability, reported by Khisti, Hern, and Narayanan in 2013, coincides with the better one between DF and CF. For the case with delayed state feedback, a new hybrid-ARQ-type scheme is proposed, and in the large system regime, it achieves a computation rate scaling like Ω(1/log(K)), much higher than the scaling Θ(1/K) achieved by the grouping scheme without feedback. Our result hints at significant gain in function computation due to feedback in the large system regime when the transmitters are connected intermittently to the receiver, in sharp contrast to the static case where feedback provides no gain at all.

Broadcasting K independent messages to multiple users where each user demands all the messages and already has a subset of the messages as side information is studied. Recently, Natarajan et al. proposed a novel broadcasting strategy called lattice index coding, which adopts lattices constructed over some principal ideal domains (PID) for transmission. Using the structure of lattices over PID, they showed that this scheme provides uniform side information gain for any side information configuration, a desired property which essentially guarantees a fair signal-to-noise ratio gain when normalized by the amount of information contained in side information. In this paper, we generalize this strategy to a general ring of algebraic integers, which may not be a PID. Upper and lower bounds on the side information gains for the proposed scheme constructed over some interesting classes of number fields are provided and are shown to coincide asymptotically in message rate. This generalization substantially enlarges the design space and partially includes the scheme by Natarajan et al. as a special case. Perhaps more importantly, in addition to side information gains, the proposed lattice index codes benefit from diversity gains inherent in constellations carved from number fields when used over Rayleigh fading channel. Some interesting examples are provided for which the proposed scheme allows the messages to be from the same field.

A novel construction of lattices is proposed. This construction can be thought of as a special class of Construction A from codes over finite rings that can be represented as the Cartesian product of L linear codes over Fp1,..., FpL, respectively, and hence is referred to as Construction πA. The existence of a sequence of such lattices that is good for channel coding (i.e., Poltyrev-limit achieving) under multistage decoding is shown. A new family of multilevel nested lattice codes based on Construction πAlattices is proposed and its achievable rate for the additive white Gaussian noise channel is analyzed. A generalization named Construction πDis also investigated, which subsumes Construction A with codes over prime fields, Construction D, and Construction πAas special cases.

In this paper, we propose a new downlink non-orthogonal multiuser superposition transmission scheme for future 5G cellular networks, which we refer to as the lattice partition multiple access (LPMA). In this proposed design, the base station transmits multilevel lattice codes for multiple users. Each user's code level corresponds to a distinct prime and is weighted by a product of all distinct primes of the other users excluding its own. Due to the structural property of lattice codes, each user can cancel out the interference from the other code levels by using the modulo lattice operation in a successive/parallel manner. LPMA can provide better user fairness in symmctrical broadcast channels, compared with non- orthogonal multiple access (NOMA). We demonstrate that the proposed LPMA shows a clear throughput enhancement over the current NOMA scheme.

We consider the problem of physical-layer network coding when the channel exhibits block fading. Specifically, we focus on the use of lattice codes in a compute-and-forward framework for realizing physical-layer network coding. We construct a novel lattice ensemble called the root-Low-Density Construction-A (root-LDA) ensemble which uses Construction A with root-low-density parity check (LDPC) codes. Using extensive simulations, we show that the proposed lattice codes exhibit full diversity when used over the block fading channels. In addition, their performance is comparable to the performance of LDA lattice codes optimized by the progressive edge growth algorithm over the additive white Gaussian noise AWGN channel. This suggests that root-LDA lattice codes provide a robust solution to the problem of implementing physical layer network coding over fading channels.

We analyze interleaved concatenation schemes of polar codes with outer binary BCH codes and convolutional codes. We show that both BCH-polar and Conv-polar codes can have a frame error rate that decays exponentially with the code length for all rates up to capacity, which is a substantial improvement in the error exponent over stand-alone polar codes. Interleaved concatenation with long constraint length convolutional codes is an effective way to leverage the fact that polarization increases the cutoff rate of the channel. Simulation results show that Conv-polar codes when decoded with the proposed soft-output multistage iterative decoding algorithm can outperform stand-alone polar codes decoded with successive cancellation or belief propagation decoding. It may be comparable to stand-alone polar codes with list decoding in the high SNR regime. In addition to this, we show that the proposed concatenation scheme requires lower memory and decoding complexity in comparison to belief propagation and list decoding of polar codes. Practically, the scheme enables rate compatible outer codes which ease hardware implementation. Our results suggest that the proposed method may strike a better balance between performance and complexity compared to existing methods in the finite-length regime.

In this paper, the downlink nonorthogonal multiple access (NOMA) system is studied where purely discrete input distributions are found that achieve the capacity region to within a constant gap without successive interference cancellation (SIC). The approach is a two-step approach where the corresponding linear deterministic model is first studied and the results are then systematically translated into purely discrete input distributions for the original model. A simple yet powerful coding scheme, which adopts off-the-shelf turbo codes with pulse amplitude modulations (PAM) is then used to simulate the proposed input distributions. Simulation results show that the proposed simple scheme under turbo decoding, both with and without SIC, can operate close to information-theoretic bounds of the proposed input distributions, which lies outside the achievable rate region of any orthogonal multiple access (OMA)-type scheme.

We study the capacity region of the parallel Gaussian bidirectional relay channel with L independent subchannels and propose efficient coding schemes for approaching the capacity limit within a constant gap. A two-step approach is considered. First, the corresponding finite field linear deterministic model is studied, for which linear network coding across sub-channels is shown to achieve the capacity region of the channel. Next, based on the insight obtained, a lattice-based compute-and-forward scheme together with simple linear network coding across sub-channels is proposed and is shown to achieve the capacity region of the Gaussian model to within L bits per user regardless of the channel parameters. Even though coding across different sub-channels is necessary for approaching the capacity region, it is shown that this can be realized through a simple linear network coding scheme (across different sub-channels) at the relay.

This paper studies the information-theoretic secrecy rates of wireless two-way relay systems where two users wish to exchange information through a single relay with an eavesdropper observing all communications. We formulate and compare the achievable secrecy rates of the system that employs one of the three common relay protocols: conventional decode-and-forward (DF), DF with network coding (NC), and compute-and-forward (CF) based on physical-layer network coding (PNC). We show that CF based on PNC achieves the highest secrecy rate at high signal-to- noise ratio (SNR), while, interestingly, the other two protocols have mixed performance depending on the power allocation scheme and network topology. Our study offers insights into designing wireless two-way relay protocols from a secrecy perspective.

Broadcasting K independent messages to multiple users where each user has a subset of the K messages as side information is studied. This problem can be regarded as a natural generalization of the well-known index coding problem to the physical-layer additive white Gaussian noise channel due to the analogy between these two problems. Recently, Natarajan, Hong, and Viterbo proposed a novel broadcasting strategy called lattice index coding which uses lattices constructed over principal ideal domains (PIDs) as a transmission scheme and showed that such a scheme provides uniform side information gains. In this paper, we generalize this strategy to rings of algebraic integers of number fields which may not be PIDs and show upper and lower bounds on the achievable side information gains. This generalization substantially enlarges the design space and includes some interesting examples in which all the messages are from the same field.

We consider the compute-and-forward relay network with limited feedback. A novel scheme called adaptive compute-and-forward is proposed to exploit the channel knowledge by working with the best ring of imaginary quadratic integers. This is enabled by generalizing Construction A lattices to other rings of imaginary quadratic integers which may not form principal ideal domains and by showing such construction can produce good lattices for coding in the sense of Poltyrev and for MSE quantization. Since there are channel coefficients (complex numbers) which are closer to elements of rings of imaginary quadratic integers other than Gaussian and Eisenstein integers, by always working with the best ring among them, we can obtain better performance than that provided by working over Gaussian or Eisenstein integers.

The limits of treating interference as noise are studied for the canonical two-user symmetric Gaussian interference channel. A two-step approach is proposed for finding approximately optimal input distributions in the high signal-to-noise ratio (SNR) regime. First, approximately and precisely optimal input distributions are found for the Avestimehr-Diggavi-Tse (ADT) linear deterministic model. These distributions are then translated, systematically, into Gaussian models, which we show can achieve the sum capacity to within O(log log(SNR)).

Communication in the presence of bounded timing asynchronism, which is known to the receiver but cannot be easily compensated, is studied. Examples of such situations include point-to-point communication over intersymbol interference (ISI) channels and asynchronous wireless networks. In these scenarios, although the receiver may know all the delays, it is often not an easy task for the receiver to compensate the delays as the signals are mixed together. A novel framework, which is called interleave/deinterleave transform (IDT), is proposed to deal with this problem. It is shown that the IDT allows one to design the delays so that quasi-cyclic (QC) codes with a proper shifting constraint can be used accordingly. When used in conjunction with QC codes, IDT provides significantly better performance than existing schemes relying solely on cyclic codes. Two instances of asynchronous physical-layer network coding, namely, the integer-forcing equalization for ISI channels and asynchronous compute-and-forward, are then studied. For integer-forcing equalization, the proposed scheme provides improved performance over using cyclic codes. For asynchronous compute-and-forward, the proposed scheme shows that there is no loss in the achievable information rates due to delays that are integer multiples of the symbol duration. Furthermore, the proposed approach shows that delays introduced by the channel can sometimes be exploited to obtain higher information rates than those obtainable in the synchronous case. The proposed IDT can be thought of as a generalization of the interleaving/deinterleaving idea proposed by Wang et al., which allows the use of QC codes, thereby substantially increasing the design space.

Communication systems are traditionally designed to have tight transmitter-receiver synchronization. This requirement has negligible overhead in the high-signal-to-noise ratio (SNR) regime. However, in many applications, such as wireless sensor networks, communication needs to happen primarily in the energy-efficient regime of low SNR, where requiring tight synchronization can be highly suboptimal. In this paper, we model the noisy channel with synchronization errors as a duplication/deletion/substitution channel. For this channel, we propose a new communication scheme that requires only loose transmitter-receiver synchronization. We show that the proposed scheme is asymptotically optimal for the Gaussian channel with synchronization errors in terms of energy efficiency as measured by the rate per unit energy. In the process, we also establish that the lack of synchronization causes negligible loss in energy efficiency. We further show that, for a general discrete memoryless channel with synchronization errors and a general input cost function admitting a zero-cost symbol, the rate per unit cost achieved by the proposed scheme is within a factor two of the information-theoretic optimum.

In this paper, we consider the use of lattice codes over Eisenstein integers for implementing a compute and-forward protocol in wireless networks when channel state information is not available at the transmitter. We extend the compute-and-forward paradigm of Nazer and Gastpar to decoding Eisenstein integer combinations of transmitted messages at relays by proving the existence of a sequence of pairs of nested lattices over Eisenstein integers in which the coarse lattice is good for covering and the fine lattice can achieve the Poltyrev limit. Using this result, we show that both the outage performance and error-correcting performance of the nested lattice codebooks over Eisenstein integers surpass those of lattice codebooks over integers considered by Nazer and Gastpar with no additional computational complexity.

Communication in the presence of bounded timing asynchronism which is known to the receiver but cannot be easily compensated is studied. Examples of such situations include point-to-point communication over inter-symbol interference (ISI) channels and asynchronous wireless networks. In these scenarios, although the receiver may know all the delays, it may not be an easy task for the receiver to compensate the delays as the signals are mixed together. A novel framework called interleave/deinterleave transform (IDT) is proposed to deal with this problem. It is shown that the IDT allows one to design the delays so that quasi-cyclic (QC) codes with a proper shifting constraint can be used accordingly. When used in conjunction with QC codes, IDT provides significantly better performance than existing schemes relying solely on cyclic codes. Two instances of asynchronous physical-layer network coding, namely the integer-forcing equalization for ISI channels and asynchronous compute-and-forward, are then studied where the gap-to-capacity can be bridged for the former and significant gains can be obtained for the later. The proposed IDT can be thought of as a generalization of the interleaving/deinterleaving idea in [1] which allows the use of QC codes thereby substantially increasing the design space.

Product construction with two levels proposed in [1] is a lattice construction which can be thought of as Construction A with codes that can be represented as the Cartesian product of two linear codes. This paper first generalizes the product construction to arbitrary number of levels. More importantly, the existence of a sequence of such lattices that are good for quantization and Poltyrev-good under multistage decoding is proved. This family of lattices is then used to generate a sequence of nested lattice codes based on the recent construction of Ordentlich and Erez. This allows one to achieve the same computation rate of Nazer and Gastpar for compute-and-forward with multistage decoding, which is termed multistage compute-and-forward.

We propose a class of lattices constructed using Construction D where the underlying linear codes are nested binary spatially-coupled low-density parity-check codes (SC-LDPC) codes with uniform left and right degrees. By leveraging recent results on the optimality of spatially-coupled codes for binary input memoryless channels and Forney et al.'s earlier results on the optimality of construction D, we show that the proposed lattices achieve the Poltyrev limit under multistage belief propagation decoding. Lattice codes constructed from these lattices are shown to provide excellent performance for the three user symmetric interference channel. They can also be naturally used in applications such as integer-forcing and compute-and-forward.

In this paper, using compute-and-forward as an example, we provide an overview of constructions of lattices from codes that possess the right algebraic structures for harnessing interference. This includes Construction A, Construction D, and Construction πA(previously called product construction) recently proposed by the authors. While most of the results in this paper have been available in the literature, we discuss two generalizations where the first one is a general construction of lattices named Construction πDsubsuming the above three constructions as special cases and the second one is to go beyond principal ideal domains and build lattices over algebraic integers.

The integration of EPON and WiMAX access networks has demonstrated considerable promise as an access solution applicable to the architecture of Fixed Mobile Convergence (FMC). The complementary features of these two systems provide high bandwidth and mobility, while reducing network deployment costs. Despite the fact that many network hardware architectures have been proposed for integrated EPON/WiMAX networks, the issue of bandwidth allocation for heterogeneous traffic (WiMAX and Ethernet) remains unresolved. This study proposes a novel Dynamic Bandwidth Allocation (DBA) scheme, under which the discrete network protocols of WiMAX and EPON access networks can run concurrently and efficiently. The proposed DBA mechanism adopts a framed approach, in which the time domains in both the optical and wireless access networks are partitioned into successive fixed-length frames. Within each frame, heterogeneous traffic is synchronously transmitted in the optical and wireless domains, and sufficient network resources are provided to ensure the respective Quality of Service (QoS) requirements of WiMAX and Ethernet traffic. Simulation results confirm the effectiveness and efficiency of the proposed DBA mechanism.

Communication systems are traditionally designed to have tight transmitter-receiver synchronization. This requirement has negligible overhead in the high-SNR regime. However, in many applications, such as wireless sensor networks, communication needs to happen primarily in the energy-efficient regime of low SNR, where requiring tight synchronization can be highly suboptimal. In this paper, we model the noisy channel with synchronization errors as an insertion/deletion/substitution channel. For this channel, we propose a new communication scheme that requires only loose transmitter-receiver synchronization. We show that the proposed scheme is asymptotically optimal for the Gaussian channel with synchronization errors in terms of energy efficiency as measured by the rate per unit energy. In the process, we also establish that the lack of synchronization causes negligible loss in energy efficiency. We further show that, for a general discrete memoryless channel with synchronization errors and a general cost function (with a zero-cost symbol) on the input, the rate per unit cost achieved by the proposed scheme is within a factor two of the information-theoretic optimum.

A novel construction of lattices is proposed. This construction can be thought of as Construction A with linear codes that can be represented as the Cartesian product of two linear codes over Fq; hence, is referred to as the product construction. The existence of a sequence of Poltyrev-good lattices generated by the product construction under some conditions is shown. This family of lattices is then used to generate signal constellations with q2elements which can be used in conjunction with multilevel coding with channel codes over Fqinstead of Fq2to design good coded modulation schemes for compute-and-forward.

We provide inner and outer bounds on the capacity region for the Gaussian bi-directional relaying over inter-symbol interference channels. The outer bound is obtained by the conventional cut-set argument. For the inner bound, we propose a compute-and-forward coding scheme based on lattice partition chains and study its achievable rate. The coding scheme is a time-domain coding scheme which uses a novel precoding scheme at the transmitter in combination with lattice precoding and a minimum mean squared error receiver to recover linear combinations of lattice codewords. The proposed compute-and-forward coding scheme substantially outperforms decode-and-forward schemes. While it is well known that for the point-to-point communication case, both independent coding along sub-channels and time-domain coding can approach the capacity limit, as a byproduct of the proposed scheme, we show that for the bi-directional relay case, independent coding along sub-channels is not optimal in general and joint coding across sub-channels can improve the capacity for some channel realizations.

In recent years, an intelligent micro-grid system composed of renewable energy sources is becoming one of the interesting research topics. The success design of daily load forecasting enables the intelligent micro-grid system to manipulate an optimized loading and unloading control by measuring the electrical supply for achieving the best economical and power efficiency. In this study, intelligent forecasting structures via a similar time method with historical load change rates are developed based on the basic frameworks of fuzzy neural network (FNN) and particle swarm optimization (PSO). In the regulative aspect of network parameters, conventional back-propagation (BP) and PSO tuning algorithms are used, and varied learning rates are designed in the sense of discrete-time Lyapunov stability theory. The performance comparisons of different intelligent forecasting structures including neural network (NN) structure with BP tuning algorithm (NN-BP), FNN structure with BP tuning algorithm (FNN-BP), FNN structure with BP tuning algorithm and varied learning rates (FNN-BP-V), FNN structure with PSO tuning algorithm (FNN-PSO) and PSO structure are given by numerical simulations of a real case in Taiwan campus.

We study the joint source-channel coding problem of transmitting a discrete-time analog source over an additive white Gaussian noise (AWGN) channel with interference known at transmitter. We consider the case when the source and the interference are correlated. We first derive an outer bound on the achievable distortion and then, we propose two joint source-channel coding schemes. The first scheme is the superposition of the uncoded signal and a digital part which is the concatenation of a Wyner-Ziv encoder and a dirty paper encoder. In the second scheme, the digital part is replaced by the hybrid digital and analog scheme proposed by Wilson et al. When the channel signal-to-noise ratio (SNR) is perfectly known at the transmitter, both proposed schemes are shown to provide identical performance which is substantially better than that of existing schemes. In the presence of an SNR mismatch, both proposed schemes are shown to be capable of graceful enhancement and graceful degradation. Interestingly, unlike the case when the source and interference are independent, neither of the two schemes outperforms the other universally. As an application of the proposed schemes, we provide both inner and outer bounds on the distortion region for the generalized cognitive radio channel.

We propose two compute-and-forward coding schemes for the bi-directional relay channel with inter- symbol interference (ISI) based on lattice codes and study their achievable rates. The first coding scheme is similar in spirit to coded orthogonal frequency division multiplexing (OFDM) with independent coding across sub-carriers and uses nested-lattice code with a power allocation strategy that can exploit the group property of lattices. The second coding scheme is a time-domain coding scheme which uses a novel precoding scheme at the transmitter in combination with lattice precoding and a minimum mean squared error receiver to recover linear combinations of lattice codewords. The proposed compute-and-forward coding schemes substantially outperform decode-and-forward schemes. While it is well known that for the point-to-point communication case, both the coded OFDM approach and the time-domain coding scheme can approach the capacity limit, we show that for the bi-directional relaying case, the performance of the two coding schemes are different. Particularly, we show that independent coding across sub-channels is not optimal and joint coding across sub-channels can improve the exchange capacity for some channel realizations.

In this paper, we study the joint source-channel coding problem of transmitting a discrete-time analog source over an additive white Gaussian noise (AWGN) channel with interference known at transmitter. We consider the case when the source and the interference are correlated. We first derive an outer bound on the achievable distortion and then, we propose two joint source-channel coding schemes to make use of the correlation between the source and the interference. The first scheme is the superposition of the uncoded signal and a digital part which is the concatenation of a Wyner-Ziv encoder and a dirty paper encoder. In the second scheme, the digital part is replaced by a hybrid digital and analog scheme so that the proposed scheme can provide graceful degradation in the presence of (signal-to-noise ratio) SNR mismatch. Interestingly, unlike the independent interference setup, we show that neither of both schemes outperform the other universally in the presence of SNR mismatch.

In this paper, we propose a general phase rotated conjugate cancellation (PRCC) scheme for intercarrier interference (ICI) cancellation in orthogonal frequency division multiplexing (OFDM) systems. We also derive the optimal phase rotation for this general scheme such that the carrier-to-interference ratio would be maximized. It is shown that the previous conjugate cancellation (CC) scheme is equivalent to a special case of our proposed scheme. The general PRCC scheme not only inherits advantages of the conventional CC scheme, such as backward compatibility with the existing OFDM systems, low receiver complexity, and two-path diversity, but also provides better performance, especially at high frequency offset situations.

Radio Frequency Identification (RFID) is a contactless technology, it considered the way to replace the barcode, since the barcode is data read with line of sight and limits the utility for item-level of logistic and supply chain application in the future. RFID is intimate linking real and virtual also creates considerable security and privacy risk in RFID adoption. Until now, many researches on the RFIDpsilas security and/or privacy were proposed. In this paper, we surveys the literature of hash-based access control scheme and propose an effective scheme to enhance the security and privacy about the passive RFID tag.

This paper presents a cooperative single-input multiple-output (SIMO) transmission scheme for wireless sensor networks (WSNs), where the number of antennas and the constellation size of modulation are jointly optimized for different transmission distances such that the energy consumption is minimized. As compared to previous cooperative MIMO schemes for WSNs, the proposed one achieves higher energy efficiency and has a smaller critical distance above which cooperative MIMO/SIMO outperforms single-input single-output (SISO). The proposed optimization method is further extended to a clustered multi-hop WSN scenario. Through joint optimization of the number of transmitter antennas, the number of receiver antennas, the constellation size, and the hop length, we derive an energy-efficient clustered cooperative SIMO multi-hop scheme. Numerical results show that the proposed scheme not only reduces the overall energy consumption but also balances the energy consumption among clusters.

In this paper, we propose a general phase rotated conjugate cancellation (PRCC) scheme for intercarrier interference (ICI) cancellation in orthogonal frequency division multiplexing (OFDM) systems. It is shown that the previous conjugate cancellation (CQ scheme is equivalent to a special case of our proposed scheme. The general PRCC scheme contains advantages of the conventional CC scheme, such as backward compatibility with the existing OFDM systems, low receiver complexity, and two-path diversity, but provides better performance, especially at high frequency offset situations.

In this paper, we propose a new intercarrier interference (ICI) suppression scheme for orthogonal frequency division multiplexing (OFDM) systems. The proposed approach can be regarded as an improved version of the method using correlative coding, where the data samples of the tail subcarriers in an OFDM symbol are encoded with the data samples of the head ones in the same OFDM symbol, instead of being encoded with those of the subsequent OFDM symbol for the original approach. For low complexity, the modified correlative coding scheme is realized as a windowing function, where the parameters are optimized through theoretical analysis. A demodulation algorithm which does not need prior information of the transmit sequence is also developed. Computer simulation results show that the proposed scheme not only achieves better performance in ICI suppression, but also prevents error propagation through OFDM symbols. The penalty is only a slight increase in the computational complexity at the receiver.