Next-generation wireless networks (NGWNs) demand highly efficient, low-latency channel access schemes to support emerging applications. To capture the distributed nature of participating stations, we formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP). Building on this formulation, we propose QIPPO/CA, a communication-efficient distributed channel-access method for collision avoidance based on the Listen-Before-Talk (LBT) protocol. QIPPO/CA leverages independent proximal policy optimization (IPPO) within a decentralized training and decentralized execution (DTDE) paradigm. It is further extended with a federated learning–inspired quantized gradient update, enabling efficient coordination among distributed stations without exchanging local information. Extensive simulations demonstrate that QIPPO/CA (i) consistently outperforms legacy random-access methods such as CSMA/CA, (ii) achieves the performance of centralized training frameworks, (iii) remains robust under dynamic channel sizes and traffic loads, and (iv) substantially reduces communication overhead during agent training. These results highlight QIPPO/CA as a promising framework for scalable, efficient, and standard-compatible distributed channel access in NGWNs.

In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techniques require substantial pilot overhead and computational complexity, especially when the number of RIS elements is extremely large. To address this issue, we introduce a two-timescale channel estimation strategy that leverages the asymmetric coherence times of both the RIS-base station (BS) channel and the User-RIS channel. We derive a time-scaling property indicating that, for any two effective channels within the longer coherence time, one effective channel can be represented as the product of a vector, termed the small-timescale effective channel, and the other effective channel. By integrating the estimated effective channel from the initial time block with observations from our piecewise beam training, we present an efficient method for estimating subsequent small-timescale effective channels. We theoretically verify the efficacy of the proposed RIS design and demonstrate, through simulations, that our channel estimation method outperforms existing methods in pilot overhead and computational complexity across various realistic channel models.

This paper proposes a fully independent multi-agent reinforcement learning (MARL) approach for distributed channel access (DCA) in wireless networks. The proposed scheme enables each device to be trained in a completely independent manner without utilizing any joint states or joint actions throughout the training phase. This maximizes the overall throughput and ensures fairness among users while keeping all the agents fully independent. Simulation results show that our proposed method outperforms the random access frameworks while incurring low computational overhead.

We study the channel estimation problem for a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. In particular, it is assumed that the channel between the RIS and a base station (BS) exhibits a near-field line-of-sight (LoS) channel as a dominant signal path in mm Wave communication system. Due to the high-rankness and non-sparsity of the RIS-BS channel, the existing methods, constructed based on far-field or near-field non-LoS channel, cannot be applied to this system. We for the first time develop an efficient channel estimation method with the idea of a piece-wise low-rank approximation. Via simulations, we demonstrate the effectiveness of our channel estimation method.

In this paper, we investigate the challenges of downlink communication in heavy payload Internet of Things (IoT) networks supported by frequency division duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The substantial overhead required for obtaining channel state information at the transmitter (CSIT) is crucial for achieving high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and incurs significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, termed circulant information classification via linear estimation (CIRCLE). Our primary contribution lies in the design of a CSIT-independent (or deterministic) precoding scheme, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design facilitates interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we validate the effectiveness of the proposed CIRCLE method.

Reconfigurable intelligent surface (RIS) is an emerging technique for robust millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for extremely large-scale RIS (XL-RIS) assisted multi-user XL-MIMO systems with hybrid beamforming structures. In this system, we propose an unified channel estimation method that yields a notable estimation accuracy in the near-field BS-RIS and near-field RIS-User channels (in short, near-near field channels), far-near field channels, and far-far field channels. Our key idea is that the effective channels to be estimated can be each factorized as the product of low-rank matrices (i.e., the product of a common matrix and a user-specific coefficient matrix). The common matrix whose columns are the basis of the column space of the BS-RIS channel is efficiently estimated via a collaborative low-rank approximation (CLRA). Leveraging the hybrid beamforming structures, we develop an efficient iterative algorithm that jointly optimizes the user-specific coefficient matrices. Via experiments and complexity analysis, we verify the effectiveness of the proposed channel estimation method (named CLRA-JO) for the three categories of wireless channels.

Robust reinforcement learning (RRL) aims to seek a robust policy by optimizing the worst case performance over an uncertainty set. This set contains some perturbed Markov decision processes (MDPs) from a nominal MDP (N-MDP) that generate samples for training, which reflects some potential mismatches between the training simulator (i.e., N-MDP) and real-world settings (i.e., the testing environments). Unfortunately, existing RRL algorithms are only applied to the tabular setting and it is still an open problem to extend them into more general continuous state space. We contribute to this subject in the following ways. We first construct an elaborated uncertainty set, which contains plausible (perturbed) MDPs only compared with the existing sets. Based on this, we propose a sample-based RRL algorithm [named adjacent robust Q-learning (ARQ-Learning)] for the tabular setting and characterize its finite-time error bound. Also, it is proved that ARQ-Learning converges as fast as the standard Q-learning and robust Q-learning (Robust-Q) while guaranteeing better robustness. Our major contribution is to introduce an additional pessimistic agent that can address the major hurdle for the extension of ARQ-Learning into cases with large or continuous state spaces. Leveraging this double-agent approach, we for the first time develop (model-free) RRL algorithms for continuous state/action spaces. Via experiments, we demonstrate the effectiveness of our algorithms.

Despite significant progress in federated learning (FL), persistent challenges, such as stragglers, adversaries, and communication costs remain. To address these issues, we propose FedLSC, a novel FL framework that leverages layer-selected correlation (LSC) to enhance both robustness and efficiency. In contrast to the existing methods, FedLSC does not rely on public data during model training, making it more practical and resilient in real-world scenarios. FedLSC introduces three key innovations: 1) preprocessing of layer selection (LS), which identifies significant layers to reduce communication costs and performance degradation; 2) local updates using LS-based scaled sign-stochastic gradient descent (SSS), introducing a layer-specific scaling mechanism to mitigate performance loss from quantization and significantly reduce communication costs; and 3) model aggregation via LSC-based schemes, which enhances robustness by processing only the significant layers and mitigating the impact of stragglers and adversaries. Furthermore, integrating the SSS scheme into FedLSC reduces communication costs to as little as 0.01% of those in state-of-the-art (SOTA) method while maintaining performance. Evaluations conducted across various FL scenarios show that FedLSC effectively supports robust performance and efficiency, even in bandwidth-constrained environments, thereby confirming its practicality in modern FL applications.

We investigate the channel estimation problem in a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. It is posited that the channel between the RIS and the base station (BS) comprises a mixed line-of-sight (LoS) and non-line-of-sight (NLoS) near-field channel. The LoS path component is modeled using the geometric free-space propagation assumption, whereas the NLoS path components are characterized by the near-field array response vectors. Existing channel estimation methods exhibit limited performance due to the lack of sparsity or low-rankness in this mixed channel. For the first time, we propose an efficient near-field LoS/NLoS channel estimation method for RIS-assisted MU-MIMO systems through a piece-wise low-rank approximation. Specifically, the effective channel to be estimated is divided into piece-wise effective channels, each exhibiting a low-rank structure. These channels are then estimated via collaborative low-rank approximation. The proposed method is referred to as PW-CLRA. Simulation results substantiate the effectiveness of PW-CLRA.

We study the joint optimization of a reflection vector and hybrid beamforming matrices for intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with hybrid beamforming structures. Recently, an efficient and practical method to estimate the so-called effective (or cascaded) channel has been proposed. For the first time, we derive the near-optimal solution of the joint optimization only using the estimated effective channel, in which the modulus constraints of the reflection vector and analog beamforming matrices are satisfied asymptotically. By simply projecting our asymptotic one, we also derive the practical solution. Via simulations, it is demonstrated that our method can outperform the state-of-the-art (SOTA) method. Furthermore, the proposed method can ensure the robustness for inevitable channel estimation errors.

Random feature-based online multikernel learning (RF-OMKL) is a promising low-complexity framework for machine learning optimization from continuous streaming data. Nonetheless, it is still an open problem to find an efficient algorithm with an analytical performance guarantee due to the challenge of an underlying online biconvex optimization (OBO). The state-of-the-art method [named expert-based online multikernel learning (EoKle)] tackled this problem approximately with the lens of expert-based online learning, in which multiple kernels (or experts) optimize their own kernel functions separately and the best sole one is determined via Hedge algorithm. It is asymptotically optimal as to the best sole kernel function in hindsight. We propose collaborative expert-based online multikernel learning (CoKle) by devising a collaborative Hedge (CoHedge) algorithm, in which kernel functions separately optimized as in EoKle are combined in an asymptotically optimal way. It is proved that CoKle is asymptotically optimal as to the best combination of each optimal kernel function in hindsight. Remarkably, this is the first method with a theoretical performance guarantee for expert-based RF-OMKL. Despite its effectiveness, CoKle is inherently suboptimal due to the individual optimization of kernel functions. We address this by presenting an OBO-based method (named BoKle) and partially prove its asymptotic optimality for RF-OMKL. Thus, BoKle can outperform the suboptimal expert-based methods such as CoKle and EoKle. Finally, we demonstrate the superiority of BoKle via experiments with real datasets.

This paper presents an efficient channel estimation algorithm for multi-user reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) systems. In this paper, the concept of low rank matrix completion (LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRMC with less beam training overhead. Then, the beam training is followed by multi-user channel estimation. For computationally efficient channel estimation, the proposed algorithm exploits the property that all the channel matrices share the same low-rank subspace in multi-user RIS-aided systems. The shared subspace is derived by combining candidate subspaces, which are estimated by fast alternating least squares (FALS) from partially observed channels. With the shared subspace, all the missing entries of channels are recovered via computationally efficient linear estimation. The simulations and complexity analysis demonstrate that the proposed algorithm shows a superior accuracy-complexity trade-off compared to existing works.

In federated learning (FL), it is generally assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in many real-world applications, ML tasks are expected to proceed in an online fashion, wherein data samples are generated as a function of time and each client has to predict a label (or make a decision) upon receiving an incoming data. To this end, online FL (OFL) has been introduced, which aims at learning a sequence of global models from distributed streaming data such that a cumulative regret is minimized. In this framework, the vanilla method (named FedOGD) by combining online gradient descent and model averaging, which is regarded as the counterpart of FedSGD in the standard FL. Despite its asymptotic optimality, FedOGD suffers from high communication costs. In this paper, we present a communication-efficient OFL method by means of intermittent transmission (enabled by client subsampling and periodic transmission) and gradient quantization. For the first time, we derive the regret bound which can reflect the impact of data-heterogeneity and communication-efficient techniques. Based on our tighter analysis, we optimize the key parameters of OFedIQ such as sampling rate, transmission period, and quantization bits. Also, we prove that the optimized OFedIQ asymptotically achieves the performance of FedOGD while reducing the communication costs by 99%. Via experiments with real datasets, we validate the effectiveness of our algorithm on various online ML tasks.

We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner receives continuous streaming data locally. This learning model is called a fully distributed online learning (or a fully decentralized online federated learning). For this model, we propose a novel learning framework with multiple kernels, which is named DOMKL. The proposed DOMKL is devised by harnessing the principles of an online alternating direction method of multipliers and a distributed Hedge algorithm. We theoretically prove that DOMKL over T time slots can achieve an optimal sublinear regret O(√T) , implying that every learner in the network can learn a common function having a diminishing gap from the best function in hindsight. Our analysis also reveals that DOMKL yields the same asymptotic performance as the state-of-the-art centralized approach while keeping local data at edge learners. Via numerical tests with real datasets, we demonstrate the effectiveness of the proposed DOMKL on various online regression and time-series prediction tasks.

We investigate an online federated learning (in short, OFL), in which many edge nodes receive their own time-series data and train a sequence of global models under the orchestration of a central server while keeping data localized. In this framework, we propose a communication efficient federated quantized online gradient descent (FedQOGD) by means of a stochastic quantization and partial node participation. We theoretically prove that FedQOGD over T time slots can achieve an optimal sublinear regret bound ${\mathcal{O}}(\sqrt T )$ for any quantization level (e.g., 1-level quantization), even when every node can participate in a learning process sporadically. Our analysis reveals that FedQOGD yields the same asymptotic performance as the centralized counterpart (i.e., all local data are gathered at the central server) while having a low-communication overhead and preserving an edge-node privacy. Finally, we verify the effectiveness of our algorithm via experiments with a real-world MNIST dataset on online classification task.

Online federated learning (OFL) is a promising framework to learn a sequence of global functions from distributed sequential data at local devices. In this framework, we first introduce a single kernel-based OFL (termed S-KOFL) by incorporating random-feature (RF) approximation, online gradient descent (OGD), and federated averaging (FedAvg). As manifested in the centralized counterpart, an extension to multi-kernel method is necessary. Harnessing the extension principle in the centralized method, we construct a vanilla multi-kernel algorithm (termed vM-KOFL) and prove its asymptotic optimality. However, it is not practical as the communication overhead grows linearly with the size of a kernel dictionary. Moreover, this problem cannot be addressed via the existing communication-efficient techniques (e.g., quantization and sparsification) in the conventional federated learning. Our major contribution is to propose a novel randomized algorithm (named eM-KOFL), which exhibits similar performance to vM-KOFL while maintaining low communication cost. We theoretically prove that eM-KOFL achieves an optimal sublinear regret bound. Mimicking the key concept of eM-KOFL in an efficient way, we propose a more practical pM-KOFL having the same communication overhead as S-KOFL. Via numerical tests with real datasets, we demonstrate that pM-KOFL yields the almost same performance as vM-KOFL (or eM-KOFL) on various online learning tasks.

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary "on the fly." This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data.

Fully decentralized online learning with multiple kernels (named FDOMKL) is studied, where each node in a network learns a sequence of global functions in an online fashion without the control of a central server. Every node finds the best global function only using information from its one-hop neighboring nodes via online alternating direction method of multipliers (ADMM) and the network-wise Hedge algorithm. The learning framework for an individual node is based on kernel learning and the proposed algorithm successfully harness multi-kernel method to find the best common function over the entire network. To the best of our knowledge, this is the first work that proposes a fully-decentralized online learning algorithm based on multiple kernels. The proposed FDOMKL preserves privacy by maintaining the local data at the edge nodes and exchanging model parameters only. We prove that FDOMKL achieves a sublinear regret bound compared with the best kernel function in hindsight under certain assumptions. In addition, numerical tests on real time-series datasets demonstrate the superiority of the proposed algorithm in terms of learning accuracy and network consistency compared to state-of-the-art single kernel methods.

We propose a novel greedy algorithm to recover a sparse signal from a small number of noisy measurements. In the proposed method, a new support index is identified for each iteration, based on bit-wise maximum a posteriori (B-MAP) detection. This approach is an optimal in the sense of detecting one of the remaining support indices, provided that all the indices during the previous iterations are perfectly recovered. Unfortunately, the exact computation of B-MAP detection is not practical since it requires a heavy marginalization of a highdimensional sparse vector to compute a posteriori probability of each remaining support. Our major contribution is to present a good proxy, named B-MAP proxy, on the a posteriori probability. The proposed proxy is easily evaluated only using vector correlations as in popular orthogonal matching pursuit (OMP) and accurate enough to represent a relative ordering on the probabilities. Via simulations, we demonstrate that the proposed greedy algorithm yields a higher recovery accuracy than the existing benchmark methods as OMP and MAP-OMP, having the same computational complexity.

We consider a compressed sensing problem to recover a sparse signal vector from a small number of one-bit quantized and noisy measurements. In this system, a probabilistic greedy algorithm, called bayesian matching pursuit (BMP), has been recently proposed in which a new support index is identified for each iteration, via a local optimal strategy based on a Gaussian-approximated maximum a posteriori estimation. Although BMP can outperform the other existing methods as Quantized Compressive Sampling Matched Pursuit (QCoSaMP) and Quantized Iterative Shrinkage-Thresholding Algorithm (QISTA), its accuracy is still far from the optimal, yielding a locally optimal solution. Motivated by this, we propose an advanced greedy algorithm by leveraging the idea of a stack algorithm, which is referred to as stacked BMP (StBMP). The key idea of the proposed algorithm is to store a number of candidate partial paths (i.e., the candidate support sets) in an ordered stack and tries to find the global optimal solution by searching along the best path in the stack. The proposed method can efficiently remove unnecessary paths having lower path metrics, which can provide a lower complexity. Simulation results demonstrate that the proposed StBMP can significantly improve the BMP by keeping a low computational complexity.

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

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

In this paper, we propose computationally efficient yet near-optimal soft-output detection methods for coded millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) systems with low-precision analog-to-digital converters (ADCs). The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then, we harness this sparse channel model to create a trellis diagram with a reduced number of states and a factor graph with very sparse edge connections, which are used for the computationally efficient soft-output detection methods. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant frame-error-rates gains compared with the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs.

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

In this paper, we present a successive-cancellation-soft-output (SCSO) detector for an uplink multiuser multiple-input-multiple- output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). The proposed detector produces soft outputs (e.g., log- likelihood ratios (LLRs)) from one-bit quantized observations in a {\em successive} way: each user k's message is sequentially decoded from a channel decoder k for k=1,...,K in that order, and the previously decoded messages are exploited to improve the reliabilities of LLRs. Furthermore, we develop an efficient greedy algorithm to optimize a decoding order. Via simulation results, we demonstrate that the proposed ordered SCSO detector outperforms the other detectors for the coded MU-MIMO systems with one-bit ADCs.

This paper considers an uplink multiuser multiple-input-multiple-output (MU-MIMO) system. In this system, we have presented a sparse-aware minimum-mean-square-error (SA-MMSE) detector which improves an underlying linear detector using the sparsity of a residual error vector (difference from the transmit vector and the detected one by the linear detector). Despite its attractive performance, the conventional SA-MMSE detector is only available for 4-QAM systems. In this paper, we generalize the SA-MMSE detector for a higher-order modulation system in a non-trivial method. This is referred to as generalized SA-MMSE (GSA-MMSE) detector. The key idea of the proposed detector is to exploit the hierarchical structure of a residual error vector. To be specific, the residual error vector can be decomposed into orthogonal sub-error vectors and, leveraging the orthogonality, the sub-error vectors can be decoded using the corresponding SA-MMSE detector in a successive fashion. Via simulation results, we demonstrate that the GSA-MMSE detector significantly outperforms the conventional linear detectors with a comparable complexity.

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

In this paper, we present an efficient method to construct a rate-compatible punctured polar (RCPP) code. In the proposed method, a common information set is simply optimized for the highest rate and then, it is effectively improved for the other codes in the family, by satisfying the condition that information bits are kept during retransmissions. This is enabled by proposing a novel hierarchical puncturing and information-copy technique. Specifically, some information bits (carefully chosen according to puncturing patterns) are copied to frozen-bit channels, yielding an information-dependent frozen vector. The improved information sets are then obtained by properly combining the common information set and the information-dependent frozen vector. Furthermore, the impact of “unknown” frozen bits are cleverly addressed using the property of the proposed hierarchical puncturing. Simulation results are provided to verify the superiority of the proposed construction method.

This paper presents a low complexity maximum likelihood detection (MLD) algorithm called one-bit-sphere-decoding for an uplink massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs). The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple sub-vectors each with reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the sub-vector and contains a list of sub-codeword vectors. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

In this paper, we present an efficient method to construct a good rate-compatible punctured polar (RCPP) code for incremental redundancy hybrid automatic repeat request schemes. One of the major challenges on the construction of a RCPP code is to optimize a common information set which is good for all the (punctured) polar codes in the family. Unfortunately, there is no efficient way to solve the above problem. In the proposed construction, a common information set is simply optimized for the highest-rate code in the family and then it is updated to yield an effective information set for each other code, by keeping the condition that information bits are unchanged during retransmissions. This is enabled by presenting a novel hierarchical (or reciprocal) puncturing and information-copy technique. Specifically, some information bits are copied to frozen-bit channels whose locations are carefully determined according to rate-compatible puncturing patterns. This yields an information-dependent frozen vector in the encoding part. Also, in the decoding part, the effective information sets are obtained by properly combining the common information set and the information-dependent frozen vector. More importantly, the impact of unknown frozen bits are avoided due to the special structure of the proposed hierarchical (or reciprocal) puncturing. Simulation results verify that the proposed RCPP code can yield a significant performance gain (about 2 dB) over a benchmark RCPP code where both codes use the same rate-compatible puncturing patterns but the latter uses the conventional all-zero frozen vector. Therefore, the proposed method would be crucial to construct a good RCPP code efficiently.

This paper considers an uplink multiuser multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs), in which K users equipped with a single-antenna communicate with one base station (BS) with Nrantennas. In this system, we present a novel multiuser MIMO detection framework inspired by coding theory. The key idea of the proposed framework is to create a code C of length 2Nrover a spatial domain. This code is constructed by an autoencoding function that is completely described by a channel transformation followed by a quantization function of the ADCs for a fixed input constellation set. Using the proposed framework, we present a novel weighted minimum distance decoding (wMDD) that achieves the optimal detection performance by appropriately exploiting unequal channel reliabilities. In addition, we show that bit error rate exponentially decreases with the minimum distance of the code C, which plays a similar role with a condition number in conventional MIMO systems. Furthermore, we develop the communication method that uses the wMDD when the explicit channel state information is not available at the BS. Finally, numerical results are provided to verify the superiority of the proposed method.

This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit sphere decoding for an uplink massive multiple-input multiple-output system with one-bit analog-to-digital converters. The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple subvectors each with a reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the subvector and contains a list of subcodeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the subcodeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

This paper considers an uplink multiuser massive multiple-input-multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which K users with a single transmit antenna communicate with one base station (BS) with N receive antennas. In this system, a weighted minimum distance (wMD) decoding was recently proposed by viewing the multiuser MIMO detection problem into an equivalent coding problem with a non-linear channel-dependent code. It was shown that the wMD decoding can outperform the state-of-the-art MIMO detection techniques. Despite its attractive performance, the complexity of the wMD decoding grows exponentially with the number of uplink users, which can prevent from its use in practice. In this paper, we reduce the complexity of the wMD decoding by introducing hierarchical code partitioning. The main idea of the proposed method is that the code is partitioned into the subcodes in a hierarchical manner and using the hierarchical structure, some unnecessary codewords are efficiently removed from the search-space. Then, the wMD decoding is performed over the reduced search-space. Via numerical results, we demonstrate that the proposed method almost achieves the optimal performance of the wMD decoding with a lower decoding complexity.

This paper considers a multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we present a new MIMO detection approach using coding theory. The principal idea of the proposed approach is to transform a non-linear MIMO channel to a linear MIMO channel by leveraging both a p-level quantizer and a lattice code where p ≥ 2. After transforming to the linear MIMO channel with the sets of finite input and output elements, efficient MIMO detection methods are proposed to attain both diversity and multiplexing gains by using algebraic coding theory. In particular, using the proposed methods, the analytical characterizations of achievable rates are derived for different MIMO configurations. One major observation is that the proposed approach is particularly useful for a large MIMO system with the ADCs that use a few bits.

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.

This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which K users with a single-antenna communicate with one base station (BS) with nrantennas. In this system, we propose a novel MIMO detection framework, which is inspired by coding theory. The key idea of the proposed framework is to create a non-linear code C of length nrand rate K/nrusing the encoding function that is completely characterized by a non-linear MIMO channel matrix. From this, a multiuser MIMO detection problem is converted into an equivalent channel coding problem, in which a codeword of the C is sent over nrparallel binary symmetric channels, each with different crossover probabilities. Levereging this framework, we develop a maximum likelihood decoding method, and show that the minimum distance of the C is strongly related to a diversity order. Furthermore, we propose a practical implementation method of the proposed framework when the channel state information is not known to the BS. The proposed method is to estimate the code C at the BS using a training sequence. Then, the proposed weighted minimum distance decoding is applied. Simulations results show that the proposed method almost achieves an ideal performance with a reasonable training overhead.

A method of constructing rate-compatible polar codes that are capacity achieving at multiple code rates with low-complexity sequential decoders is presented. The underlying idea of the construction exploits certain common characteristics of polar codes that are optimized for a sequence of successively degraded channels. The proposed code consists of parallel concatenation of multiple polar codes with information-bit divider at the input of each polar encoder. Thus, it is referred to as parallel concatenated polar (PCP) codes. A lower-rate PCP code is simply constructed by adding more constituent polar codes, which enables incremental retransmissions at different rates in order to adapt to channel conditions. Due to the length limitation of polar codes, the PCP code can only support a restricted set of rates that is characterized by the size of the kernel when conventional polar codes are used. To overcome this limitation, punctured polar codes, which provide more flexibility on blocklength by controlling a puncturing fraction, are considered as constituent codes. The existence of capacity-achieving punctured polar codes for any given puncturing fraction is proven. Using such punctured polar codes as constituent codes, it is shown that the proposed PCP code is capacity achieving for an arbitrary sequence of rates and for any class of degraded channels.

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

We present a method of constructing rate-compatible polar codes that are capacity-achieving with low-complexity sequential decoders. The proposed code construction allows for incremental retransmissions at different rates in order to adapt to channel conditions. The main idea of the construction exploits certain common characteristics of polar codes that are optimized for a sequence of degraded channels. The proposed approach allows for an optimized polar code to be used at every transmission thereby achieving capacity. Due to the length limitation of conventional polar codes, the proposed construction can only support a restricted set of rates that is characterized by the size of the kernel when conventional polar codes are used. We thus consider punctured polar codes which provide more flexibility on block length by controlling a puncturing fraction. We show the existence of capacity-achieving punctured polar codes for any given puncturing fraction. Using punctured polar codes as constituent codes, we show that the proposed rate-compatible polar code is capacity-achieving for an arbitrary sequence of rates and for any class of degraded channels.

In this paper, we propose coded compressive sensing that recovers an n-dimensional integer sparse signal vector from a noisy and quantized measurement vector whose dimension m is far-fewer than n. The core idea of coded compressive sensing is to construct a linear sensing matrix whose columns consist of lattice codes. We present a two-stage decoding method named compute-and-recover to detect the sparse signal from the noisy and quantized measurements. In the first stage, we transform such measurements into noiseless finite-field measurements using the linearity of lattice codewords. In the second stage, syndrome decoding is applied over the finite-field to reconstruct the sparse signal vector. A sufficient condition of a perfect recovery is derived. Our theoretical result demonstrates an interplay among the quantization level p, the sparsity level k, the signal dimension n, and the number of measurements m for the perfect recovery. Considering 1-bit compressive sensing as a special case, we show that the proposed algorithm empirically outperforms an existing greedy recovery algorithm.

In this paper, we present a cooperative relaying strategy for half-duplex multihop relay networks. This scheme consists of three parts: 1) relay selection to yield a layered relay network; 2) group successive relaying that establishes a relay schedule to efficiently exploit half-duplex relays; and 3) a cooperative relaying scheme named short message noisy network coding with sliding-window decoding (SNNC-SW) that outperforms other state-of-the-art information theoretical schemes with lower decoding complexity and delay. We derive an achievable rate region of the proposed SNNC-SW scheme and attain a closed-form rate expression in the asymptotic case for several network models of interests. We then focus on the first part of our relaying strategy regarding efficient relay selection. We develop interference-harnessing routing that exploits the fact that in SNNC-SW, interference is treated as a useful signal. We show that, due to the efficient treatment of interference, this scheme can outperform routing schemes that deploy store-and-forward, a solution previously proposed for practical wireless multihop networks. Finally, we develop a low-complexity successive decoder of our scheme (implemented by a conventional MIMO decoder), which is a solution that can readily be implemented in practice. It is shown that also this practical scheme provides a significant gain over routing (based on store-and-forward) and the performance gap increases as the network becomes denser.

In this paper we present a novel transmission scheme for wireless multihop backhaul networks. The scheme consists of group successive relaying that efficiently exploits half-duplex relays and a coding scheme that improves quantize-map-and- forward (QMF). We derive an achievable rate region of the proposed scheme and attain a closed-form expression in the asymptotic case for several network models of interests. It is shown that the proposed scheme outperforms the multihop routing, which is a solution currently proposed for wireless multihop backhaul networks. Furthermore, the performance gap increases as a network becomes denser. Based on the proposed scheme, we present energy-efficient routing referred to as energy- harvesting in which each node requires a lower transmission power to achieve a desired performance compared to other schemes.

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

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

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

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

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

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

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

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

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

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

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

In this paper we present a novel framework to convert the K-user linear deterministic multiple access channel (MAC) over Fpminto a K-user MAC over ground field Fpwith m multiple inputs/outputs (MIMO). This framework makes it possible to develop coding schemes for MIMO channels as done in symbol extension for time-varying channels. Using aligned network diagonalization based on this framework, we show that the sum-rate of (2m - 1)log p is achievable for a 2 × 2 × 2 interference channel over Fpm, under certain conditions on channel coefficients. We also provide some interesting relation between field extension and symbol extension.

We study a distributed antenna system where L antenna terminals (ATs) are connected to a central processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). This model has been widely investigated both for the uplink (UTs to CP) and for the downlink (CP to UTs), which are instances of the general multiple-access relay and broadcast relay networks. We contribute to the subject in the following ways: 1) For the uplink, we consider the recently proposed “compute and forward” (CoF) approach and examine the corresponding system optimization at finite SNR. 2) For the downlink, we propose a novel precoding scheme nicknamed “reverse compute and forward” (RCoF). 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding. 4) We provide extensive numerical results and finite SNR comparison with other “state of the art” information theoretic techniques, in scenarios including fading and shadowing. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. In both cases, for a given set of transmitters, the goal consists of selecting a subset of the receivers such that the corresponding system matrix has full rank and the sum rate is maximized. We present low-complexity ATs and UTs selection schemes and demonstrate through Monte Carlo simulation that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the noninteger penalty affecting CoF/RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, show competitive performance of the proposed approaches with significantly lower complexity.

The two-user Gaussian interference channel with a shared out-of-band relay is considered. The relay observes a linear combination of the source signals and broadcasts a common message to the two destinations, through a perfect link of fixed limited rateR0bits per channel use. The out-of-band nature of the relay is reflected by the fact that the common relay message does not interfere with the received signal at the two destinations. A general achievable rate is established, along with upper bounds on the capacity region for the Gaussian case. ForR0values below a certain threshold, which depends on channel parameters, in achievable rates asymptotically in regimes where joint a two-for-one gain is achievable by the capacity region of this channel is determined in this paper to within a constant gap of Δ = 1.95 bits. We identify interference regimes where a two-for-one gain in achievable rates is possible for every bit relayed, up to a constant approximation error. Instrumental to these results is a carefully designed quantize-and-forward type of relay strategy along with a joint decoding scheme employed at destination ends. Further, we also study successive decoding strategies with optimal decoding order (corresponding to the order at which common, private, and relay messages are decoded), and identify interference regimes where with an optimal decoding order, successive decoding may also achieve two-for-one gains similar to joint decoding; yet, in general, successive decoding produces unbounded loss asymptotically when compared to joint decoding.

We consider a distributed antenna system where L antenna terminals (ATs) are connected to a Central Processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). This system model has been widely investigated both for the uplink and the downlink, which are instances of the general multiple-access relay and broadcast relay networks. In this work we focus on the downlink, and propose a novel downlink precoding scheme nicknamed “Reverse Quantized Compute and Forward”(RQCoF). For this scheme we obtain achievable rates and compare with the state-of-the-art available in the literature. We also provide simulation results for a realistic network with fading with K >; L UTs, and show that channel-based user selection produces large benefits and essentially removes the problem of rank deficiency in the system matrix.

We consider a low-complexity version of the Compute and Forward scheme that involves only scaling, offset (dithering removal) and scalar quantization at the relays. The proposed scheme is suited for the uplink of a distributed antenna system where the antenna elements must be very simple and are connected to a joint processor via orthogonal perfect links of given rate R0. We consider the design of non-binary LDPC codes naturally matched to the proposed scheme. Each antenna element performs individual (decentralized) Belief Propagation decoding of its own quantized signal, and sends a linear combination of the users' information messages via the noiseless link to the joint processor, which retrieves the users' messages by Gaussian elimination. The complexity of this scheme is linear in the coding block length and polynomial in the system size (number of relays).

In this letter, a new rate-control scheme, called splitting, is proposed to construct low-rate codes from highrate codes, which splits rows of parity-check matrices of repeat accumulate-type (RA-Type) LDPC codes by adding new parity bits. When a high-degree check node is split into two low-degree check nodes, by making the check node degree distribution in a concentrated form, the performance of low-rate codes can be improved. We also explicitly construct rate-compatible repeat RA-Type LDPC (RC RA-Type LDPC) codes using splitting for code rates from 1/3 to 4/5 and compare this with other RC RA-Type LDPC codes.

In this paper, we analyze the sequential message- passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results indicate that as the number of partitioned subsets of check nodes increases, the BER performance is improved. We also derive the recursive equations for mean values of messages at check and variable nodes by using density evolution with a Gaussian approximation. From these equations, the mean values are obtained at each iteration of the sequential decoding algorithm and the corresponding BER values are calculated. They show that the sequential decoding algorithm converges faster than the conventional one. Finally, the analytical results are confirmed by the simulation results.

In this paper, we show that irregular concatenated zigzag (ICZZ) codes are suitable as mother codes to support a wide range of code rates. We introduce the degree distribution matching method to derive the optimal puncturing and pruning patterns in order to achieve the rate-compatible target code rates of ICZZ code. By combining ICZZ code with the optimal puncturing and pruning patterns, new rate-compatible ICZZ (RC-ICZZ) codes are constructed. In an example, n RC-ICZZ code to achieve the code rates 1/3, 1/2, 2/3, 4/5, and 8/9 is constructed and shown to outperform RCTC adopted in 3GPP. Therefore, RC-ICZZ codes are suitable for hybrid automatic repeat request to increase the system throughput.

In this paper, an adaptive bit-reliability mapping is proposed for the bit-level Chase combining in LDPC-coded high-order modulation systems. Contrary to the previously known bit-reliability mapping that assigns the information (or parity) bits to more (or less) reliable bit positions, the proposed mapping flexibly assigns codeword bits to the bit positions of various reliabilities by considering the characteristics of code and protection levels. Compared with the symbol-level Chase combining and the constellation rearrangement bit mapping, the proposed mapping gives 0.7 - 1.3 dB and 0.1 - 1.0 dB performance gain at FER = 10-3with no additional complexity, respectively. The adaptive bit-reliability mappings are derived for various environments and the validity of them is confirmed through simulation.

In this paper, the authors propose new decoding scheme of punctured LDPC codes with dual-diagonal parity structure by merging check nodes connected to the punctured parity nodes. This check-node merging decoding not only needs smaller number of operations at each iteration but also shows faster decoding convergence speed than the conventional erasure decoding. For the binary erasure channel (BEC) and AWGN channel, the authors analyze and compare the check-node merging and the conventional erasure decoding schemes of the punctured LDPC codes with dual-diagonal parity structure using density evolution. Analytical results show that the check-node merging decoding gives faster convergence speed for both BEC and AWGN channel. Also, simulation results are provided to confirm the analytical results.

In this paper, we study and propose a puncturing algorithm of block-type low-density parity-check (B-LDPC) codes. This optimal puncturing algorithm is derived from the fact that puncturing of parity bits is equivalent to merging the check nodes. Furthermore, we propose a new decoding algorithm suitable for the punctured B-LDPC codes. This decoding algorithm needs not only smaller number of operations at each iteration, but also shows faster decoding convergence speed than the conventional erasure decoding algorithm. If the optimally punctured B-LDPC code is decoded by the new decoding algorithm, it results in the same performance as the unpunctured B-LDPC code of the same code rate.

In this paper we investigate the puncturing scheme of low-density parity-check code with zigzag parity structure (Z-LDPC code) using the fact that two check nodes can be merged if they are connected to the same parity node. By applying this result to Tanner graph of punctured Z-LDPC code, we can obtain simple Tanner graph, called effective Tanner graph (eTanner graph), which does not include the punctured parity nodes. Based on degree distributions of eTanner graph of punctured Z-LDPC code, we propose a simple algorithm to design good rate-compatible puncturing for finite-length Z-LDPC code. It is shown that the proposed algorithm is the optimal for Z-LDPC code and the designed rate-compatible Z-LDPC code outperforms the rate-compatible Turbo code adopted in 3GPP.

In this paper, we construct rate-compatible block-type low-density parity-check (B-LDPC) codes using splitting that cover a wide range of code rates from 1/3 to 4/5. They outperform other rate-compatible B-LDPC codes for all rates and can be decoded conveniently and efficiently. A strong motivation for proposing splitting scheme comes from the observation that the quality of the initial transmission is the most important factor to achieve high throughput of type-II HARQ. Proposed scheme builds low-rate codes from high-rate codes by splitting rows of the given LDPC parity-check matrix. Thus, rate-compatible LDPC codes obtained by splitting have good FER (Frame Error Rate) performance in the first transmission. Contrary to other rate-control methods such as puncturing, shortening, and extending, the splitting algorithm not only needs smaller number of operations but also shows faster decoding convergence speed since more efficient Tanner graph is used for the decoding.

In the next-generation mobile communication systems for various high-speed data services, the error correcting codes are required to have rate-compatibility, low decoding complexity and good performance for various frame lengths. In this paper, new rate-compatible punctured concatenated zigzag (RCPCZ) codes are proposed and analyzed by using the density evolution. As their application, type-II HARQ using RCPCZ code is shown to have better throughput at short frame lengths than yype-II HARQ using turbo code.

In this paper, new rate-compatible punctured concatenated zigzag (RCPCZ) codes are proposed and analyzed by using the density evolution technique. This analysis gives the design criteria for the rate-compatible puncturing patterns and accordingly good puncturing patterns for constructing RCPCZ codes are obtained. Since RCPCZ codes are linear-time encodable and show capacity-approaching performance, they can be a good candidate technology for the next-generation communication systems. As their applications, Type-II hybrid automatic repeat request (HARQ) using RCPCZ code is constructed, which gives higher throughput than Type-II HARQ using RCMCZ code

Capacity-approaching codes using iterative decoding have been the main research subject of coding area during past decade. In this paper, a new channel coding scheme called irregular concatenated zigzag (ICZZ) code is proposed. ICZZ codes can be viewed as a special case of irregular low-density parity-check (LDPC) codes with linear-time encodable structure. They are different from concatenated zigzag (CZZ) codes in the sense that the number of information bits entering into each zigzag encoder can be different and different zigzag codes can be used as component codes. A simple method to design ICZZ codes is proposed and by using an example, it is shown that ICZZ code of rate 1/3 has better performance than the optimal CZZ code and turbo code adopted in 3GPP

The block coding scheme for OFDM, which is combining Golay complementary sequence with Reed-Muller code (GCRM) that limits PAPR up to 3 dB with error-correcting capability, has been introduced recently [J. A. Davis et al. (1999)]. However, the coding gain of GCRM cannot overcome ICI caused by frequency error in OFDM. In this paper, a new combined scheme of ICI self-cancellation [Y. Zhao et al. (2001)] [Y. Fu et al. (2002)] and GCRM code is proposed. This scheme is analyzed to show that it limits PAPR up to 6 dB and can compensate the nonlinear distortion by TWTA. It is also verified that for the low back-off value of TWTA, our scheme shows better performance than GCRM.