We consider a spectrum sharing problem where two users attempt to communicate over N channels. The Primary User (PU) has prioritized transmissions and its occupancy on each channel over time can be modeled as a Markov chain. The Secondary User (SU) needs to determine which channels are free at each time-slot and attempt opportunistic transmissions. The goal of the SU is to maximize its own throughput, while simultaneously minimizing collisions with the PU, and satisfying spectrum access constraints. To solve this problem, we first decouple the multiple-channel problem into N single-channel problems. For each decoupled problem, we prove that there exists an optimal threshold policy that depends on the last observed PU occupancy and the freshness of this occupancy information. Second, we establish the indexability of the decoupled problems by analyzing the structure of the optimal threshold policy. Using this structure, we derive a Whittle index-based scheduling policy that allocates SU transmissions using the Age of Information (AoI) of accessed channels. We also extend our insights to PU occupancy models that are correlated across channels and incorporate learning of unknown Markov transition matrices into our policies. Finally, we provide detailed numerical simulations that demonstrate the performance gains of our approach.

It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works consider either simple coverage maximization or simultaneous optimization of the placement of the RIS along with the beamforming and reflection coefficients, most of which assume that the location of the RIS, base station (BS), and users are known. However, in practice, only the spatial variation of user density and obstacle configuration are likely to be known prior to deployment of the system. Thus, we formulate a non-convex problem that optimizes the position of the RIS over the expected minimum signal-to-interference-plus-noise ratio (SINR) of the system with user randomness, assuming that the system employs joint beamforming after deployment. To solve this problem, we propose a recursive coarse-to-fine methodology that constructs a set of candidate locations for RIS placement based on the obstacle configuration and evaluates them over multiple instantiations from the user distribution. The search is recursively refined within the optimal region identified in each stage to determine the final optimal region for RIS deployment. Detailed numerical results are presented to corroborate our findings.

State estimation is essential in systems where the true state must be inferred from noisy measurements. The Kalman filter remains a core tool used for state estimation across various fields, including aerospace, navigation, and signal processing. Traditional estimation methods typically rely on fixed sampling strategies, which can limit performance in dynamic or resource-constrained environments. In this letter, we introduce a new adaptive sampling framework for the Kalman filter that optimizes the measurement sampling matrix to minimize mean square error under a power constraint. In the proposed framework, sampling decisions respond both to the evolving state and to shifts in the estimation objective. We propose two sampling methods that enable dynamic, resource-efficient sampling while enhancing estimation accuracy. The first method sequentially designs sampling matrices and applies to general systems. The second method jointly optimizes the sampling matrices over an entire block; this method further improves estimation performance and could be used for a more specialized problem. We also numerically show that the proposed methods outperform other heuristic sampling techniques.

The distance between a terrestrial terminal and an endpoint in a non-terrestrial network (NTN) greatly impacts the performance of a 6G wireless application. Considering the multitude of critical metrics influenced by this distance – such as channel degradation due to path-loss, latency incurred from propagation delay, and coverage probability dependent on node’s footprint – an analytical description of the terrestrial-NTN distance is imperative for network design. To this end, many researchers have employed the use of stochastic geometry to probabilistically describe the distance from a terrestrial terminal to the nearest node of an NTN. Although the nearest node statistics are a powerful analytical tool, this paper extends on these techniques by enabling the stochastic description of the ordered distance to any node in a hierarchical multi-layer NTN. Explicitly, we utilize stochastic geometry to derive and verify the distribution of the distance from a terrestrial terminal to i) thenth-nearest node on a specific layerk, and ii) the distance to thenth-nearest node across all layers of the NTN. An analytical understanding of these distances enable better evaluations of different hierarchical NTNs architectures and support the analysis of complex 6G applications which may not always choose the nearest node as the serving node.

Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable attention in horizontal FL, they are less understood for vertical FL (VFL), where devices hold different features of the samples, and only the server holds the labels. In this work, we propose a novel backdoor attack on VFL which (i) does not rely on gradient information from the server and (ii) considers potential collusion among multiple adversaries for sample selection and trigger embedding. Our label inference model augments variational autoencoders with metric learning, which adversaries can train locally. A consensus process over the adversary graph topology determines which datapoints to poison. We further propose methods for trigger splitting across the adversaries, with an intensity-based implantation scheme skewing the server towards the trigger. Our convergence analysis reveals the impact of backdoor perturbations on VFL indicated by a stationarity gap for the trained model, which we verify empirically as well. We conduct experiments comparing our attack with recent backdoor VFL approaches, finding that ours obtains significantly higher success rates for the same main task performance despite not using server information. Additionally, our results verify the impact of collusion on attack performance.

Interest continues to grow in utilizing federated learning (FL) for various signal processing and communications applications. Over-the-air (OTA) computation has been proposed to improve FL efficiency in bandwidth-limited environments by leveraging the superposition characteristic of a wireless multiple-access channel (MAC). However, OTA FL faces inherent challenges due to channel noise and fading in any wireless MAC scenario, which can degrade optimization and significantly reduce model accuracy. This paper aims to design a robust OTA FL system to counteract the effects of noise and fading over time-varying channels. We propose a novel approach employing a Kalman filter (KF)-based OTA FL algorithm under imperfect channel state information (CSI). We conduct a convergence analysis of our OTA FL scheme, which motivates our development of a complementary hierarchical optimization methodology to minimize the impact of bias and noise terms. Numerical results confirm that our methodology has superior performance to conventional OTA FL, and approaches the performance obtained by the upper limit of perfect CSI in low-SNR scenarios.

Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems. However, the implementation of ISAC technology is very challenging due to practical constraints such as high complexity. In this paper, we introduce a novel ISAC waveform design strategy, calledthe spatial-division ISAC (SD-ISAC) waveform, which simplifies the ISAC waveform design problem by decoupling it into separate communication and radar waveform design tasks. Specifically, the proposed strategy leverages the null-space of the communication channel to superimpose sensing signals onto communication signals without interference. This approach offers multiple benefits, including reduced complexity and the reuse of existing communication and radar waveforms. We then address the problem of optimizing the spatial and temporal properties of the proposed waveform. We develop a low-complexity beampattern matching algorithm, leveraging a majorization-minimization (MM) technique. Furthermore, we develop a range sidelobe suppression algorithm based on manifold optimization. We provide comprehensive discussions on the practical advantages and potential challenges of the proposed method, including null-space feedback. We evaluate the performance of the proposed waveform design algorithm through extensive simulations. Simulation results show that the proposed method can provide similar or even superior performance to existing ISAC algorithms while reducing computation time significantly.

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal’s polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target signal-to-interference-plus-noise ratio (SINR) leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for mean square error (MSE) minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

The ever-increasing demand for high-speed wireless communication has generated significant interest in utilizing frequency bands that are adjacent to those occupied by legacy wireless systems. Since the legacy wireless systems were designed based on often decades-old assumptions about wireless interference, utilizing these new bands will result in interference with the existing legacy users. Many of these legacy wireless devices are used by critical infrastructure networks upon which society depends. There is an urgent need to develop schemes that can protect legacy users from such interference. For many applications, legacy users are located within geographically-constrained regions. Several studies have proposed mitigating interference through the implementation of exclusion zones near these geographically-constrained regions. In contrast to solutions based on geographic exclusion zones, this paper presents a communication theory-based solution. By leveraging knowledge of these geographically-constrained regions, we aim to reduce the interference impact on legacy users. We achieve this by incorporating received power constraints, termed as region constraints, in our massive multiple-input multiple-output (MIMO) system design. We perform a capacity analysis for single-user massive MIMO and a sum-rate analysis for the multi-user massive MIMO system with transmit power and region constraints. We present a precoding design method that allows for the utilization of new frequency bands while protecting legacy users.

A novel multibeam time-division (TD) multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) approach is proposed to achieve a balanced tradeoff between sensing and communication functionalities and accurate sensing parameter estimation with a wide field-of-view. Firstly, the TD strategy is introduced to address the simultaneous high demands for sensing performance and communication rate. By allocating time resources between sensing and communications, this approach can reach a desired balance between them while avoiding spectrum and spatial interference, as well as competition in power allocation. Next, a new multibeam method is developed to achieve wide-area sensing for TD MIMO ISAC. Conventional multibeam methods typically rely on beam scanning for direction estimation, suffering from limited accuracy. Inspired by Doppler division multiple access (DDMA) approach, the proposed method divides the Doppler spectrum into more subbands, generating more beams than the number of transmit antenna elements using only phase modulation. Beyond enabling flexible control over the sensing coverage location through beam selection, the proposed method also improves parameter estimation accuracy by fully leveraging the inter-beam relationships, particularly for targets located at null directions. Specifically, for such targets, the proposed method achieves a significantly higher maximum unambiguous velocity, mitigating the velocity ambiguity inherent in conventional DDMA. Simulation results validate the effectiveness of the proposed approach in enhancing both the performance tradeoff between sensing and communication, and the accuracy of sensing parameter estimation.

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generative models have been proposed to synthesize channel matrices, but outputs produced by such methods may not correspond to geometrically viable channels and do not provide any insight into the scenario being generated. In this work, we aim to address both these issues by integrating established parametric, physics-based geometric channel (PPGC) modeling frameworks with generative methods to produce realistic channel matrices with interpretable representations in the parameter domain. We show that the generative model converges to prohibitively suboptimal stationary points when learning the underlying prior directly over the parameters due to the non-convex PPGC model. To address this limitation, we propose a linearized reformulation of the problem to ensure smooth gradient flow during generative model training, while also providing insights into the underlying physical environment. We evaluate our model against prior baselines by comparing the generated, scenario-specific samples in terms of the 2-Wasserstein distance and through its utility when used for downstream compression tasks.

In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks, by optimizing the placement of unmanned aerial vehicle (UAV) relays, relay beamforming, and receive combining. The design is separated into two problems: beamforming-aware UAV placement optimization and transceiver design for minimum SINR maximization. A significant challenge in beamforming-aware UAV placement optimization is the lack of instantaneous channel state information (CSI) prior to deploying UAV relays, making it difficult to derive the beamforming SINR in non-orthogonal multi-user transmission. To address this issue, an approximation of the expected beamforming SINR is derived using the narrow beam property of a massive MIMO base station. Based on this, a UAV placement algorithm is proposed to provide UAV positions that improve the minimum expected beamforming SINR among users, using a difference-of-convex framework. Subsequently, after deploying the UAV relays to the optimized positions, and with estimated CSI available, a joint relay beamforming and receive combining (JRBC) algorithm is proposed to optimize the transceiver to improve the minimum beamforming SINR among users, using a block-coordinate descent approach. Numerical results show that the UAV placement algorithm combined with the JRBC algorithm provides a 4.6 dB SINR improvement over state-of-the-art schemes.

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different twoway coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain.

The adversarial wiretap channel of type II (AWTC-II) is a communication channel that can a) read a fraction of the transmitted symbols up to a given bound and b) induce both errors and erasures in a fraction of the symbols up to given bounds. The channel is controlled by an adversary who can freely choose the locations of the symbol reads, errors and erasures via a process with unbounded computational power. The AWTC-II is an extension of Ozarow’s and Wyner’s wiretap channel of type II to the adversarial channel setting. The semantic-secrecy (SS) capacity of the AWTC-II is partially known, where the best-known lower bound is non-constructive and proven via a random coding argument that uses a large number (that is, exponential in blocklengthn) of random bits to describe the random code. In this work, we establish a new derandomization result in which we match the best-known lower bound via a non-constructive random code that uses onlyO(n2) random bits. Unlike fully random codes, our derandomized code admits an efficient encoding algorithm and benefits from some linear structure. Our derandomization result is a novel application ofrandom pseudolinear codes– a class of non-linear codes first proposed for applications outside the AWTC-II setting, which havek-wise independent codewords wherekis a design parameter. As the key technical tool in our analysis, we provide a novel concentration inequality for sums of random variables with limited independence, as well as a soft-covering lemma similar to that of Goldfeld, Cuff and Permuter that holds for random codes withk-wise independent codewords.

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a cooperative ground-to-satellite federated learning (FL) methodology to facilitate machine learning service management over remote regions. Our methodology orchestrates satellite constellations to provide the following key functions during FL: (i) processing data offloaded from ground devices, (ii) aggregating models within device clusters, and (iii) relaying models/data to other satellites via inter-satellite links (ISLs). Due to the limited coverage time of each satellite over a particular remote area, we facilitate satellite transmission of trained models and acquired data to neighboring satellites via ISL, so that the incoming satellite can continue FL for the region. We also develop a training latency minimizer which optimizes over the amount of data to be offloaded from ground devices to satellites. Through experiments on benchmark datasets, we show that our scheme can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.

Dual-function radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the proposed algorithm via comprehensive simulations.

Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for machine learning (ML) pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. These datasets were collected through an extensive measurement campaign in different environments, including farms, hilly ter-rains, and residential areas. This comprehensive data collection provides vital ground truth for model training. A set of channel features was engineered, including geographical attributes derived from LiDAR datasets. These features were then used to train our prediction model, incorporating the highly efficient and robust gradient boosting ML algorithm, CatBoost. The integration of synthetic data, as demonstrated in our study, significantly improves the generalizability of the model in different environments, achieving a remarkable improvement of approximately 12 dB in terms of mean absolute error for the best-case scenario. Moreover, our analysis reveals that even a small fraction of measurements added to the simulation training set, with proper data balance, can significantly enhance the model's performance.

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In this work, we design a positioning neural network (P-NN) that substantially reduces the complexity of deep learning-based WP through carefully crafted minimum description features. Our feature selection is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We also develop a novel methodology for adaptively selecting the size of feature space, which optimizes over balancing the expected amount of useful information and classification capability, quantified using information-theoretic measures on the signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP).

Matrix computations are a fundamental building block of the edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Existing approaches for distributing the matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to slower nodes, called stragglers. In the edge learning context, however, these approaches will compromise sparsity properties that are often present in the original matrices found at the edge server. In this study, we consider the challenge of augmenting, such approaches to preserve input sparsity when distributing the task across the edge devices, thereby retaining the associated computational efficiency enhancements. First, we find a lower bound on the weight of coding, i.e., the number of submatrices to be combined to obtain coded submatrices to provide the resilience to the maximum possible number of straggler devices (for given number of devices and their storage constraints). Next, we propose distributed matrix computation schemes which meet the exact lower bound on the weight of the coding. Numerical experiments conducted in amazon Web services (AWSs) validate our assertions regarding straggler mitigation and computation speed for the sparse matrices.

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a ground-to-satellite cooperative federated learning (FL) methodology to facilitate machine learning service management over remote regions. Our methodology orchestrates satellite constellations to provide the following key functions during FL: (i) processing data offloaded from ground devices, (ii) aggregating models within device clusters, and (iii) relaying models/data to other satellites via inter-satellite links (ISLs). Due to the limited coverage time of each satellite over a particular remote area, we facilitate satellite transmission of trained models and acquired data to neighboring satellites via ISL, so that the incoming satellite can continue conducting FL for the region. We theoretically analyze the convergence behavior of our algorithm, and develop a training latency minimizer which optimizes over satellite-specific network resources, including the amount of data to be offloaded from ground devices to satellites and satellites’ computation speeds. Through experiments on three datasets, we show that our methodology can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art.

Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the$n$-bit codeword as input and computes an$rn$-bit output for some given$r \in [{0,1}]$. We consider the scenario where the$rn$-bit observation is computationally bounded – the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error/corruption. For all$r \in [0,1-H(p)]$where$H(\cdot)$is the binary entropy function and$p$is the adversary’s error budget, we characterize the capacity of the above channel and find that the capacity is identical to the completely oblivious setting ($r=0$). This result can be viewed as a generalization of known results on myopic adversaries and on channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.

Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global aggregations (at the server). In this paper, we develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions: (i) Network, allowing decentralized cooperation among the devices via device-to-device (D2D) communications. (ii) Heterogeneity, interpreted at three levels: (ii-a) Learning: PSL considers heterogeneous number of stochastic gradient descent iterations with different mini-batch sizes at the devices; (ii-b) Data: PSL presumes a dynamic environment with data arrival and departure, where the distributions of local datasets evolve over time, captured via a new metric for model/concept drift. (ii-c) Device: PSL considers devices with different computation and communication capabilities. (iii) Proximity, where devices have different distances to each other and the access point. PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning. We then propose network-aware dynamic model tracking to optimize the model learning vs. resource efficiency tradeoff, which we show is an NP-hard signomial programming problem. We finally solve this problem through proposing a general optimization solver. Our numerical results reveal new findings on the interdependencies between the idle times in-between the global aggregations, model/concept drift, and D2D cooperation configuration.

The extensive coverage offered by satellites makes them effective in enhancing service continuity for users on dynamic airborne and maritime platforms, such as airplanes and ships. In particular, geosynchronous Earth orbit (GEO) satellites ensure stable connectivity for terrestrial users due to their stationary characteristics when observed from Earth. This paper introduces a novel approach to model and analyze GEO satellite networks using stochastic geometry. We model the distribution of GEO satellites in the geostationary orbit according to a binomial point process (BPP) and examine satellite visibility depending on the terminal’s latitude. Then, we identify potential distribution cases for GEO satellites and derive case probabilities based on the properties of the BPP. We also obtain the distance distributions between the terminal and GEO satellites and derive the coverage probability of the network. We further approximate the derived expressions using the Poisson limit theorem. Monte Carlo simulations are performed to validate the analytical findings, demonstrating a strong alignment between the analyses and simulations. The simplified analytical results can be used to estimate the coverage performance of GEO satellite networks by effectively modeling the positions of GEO satellites.

Ultra-reliable low-latency communication (URLLC) and machine-to-machine (M2M) relaying are increasingly important. In-band full-duplex (IBFD) communication is desirable for low-latency communication because it can theoretically double the capacity of half-duplex (HD) communication, but is limited by self-interference (SI) in which the IBFD transmitter injects interference into its received signal due to large transmit and receive power differences. There are many methods to mitigate SI in IBFD, but we focus on multiple-input multiple-output (MIMO) precoding to spatially null SI, while also being careful to limit communication latency. Unlike works aiming to compute the highest achievable rate in IBFD relays with an essentially fixed precoder, we propose a strategy allowing the precoder to change on a per channel use basis to avoid rate bottlenecks at each hop. Our strategy generalizes to multiple relays. We frame finding the sequence of precoders to relay packets through$N$IBFD MIMO relays with the lowest communication latency as a shortest path problem. Specifically, we design a quantized covariance precoder codebook at each transmitter based on limiting the maximal SI power each precoder produces. Then, an iterative algorithm is used to optimize the selection of precoders over channel uses to relay packets in the fewest channel uses possible.

On-demand deployment of mobile communication infrastructure has emerged as a promising solution for extending cellular coverage in rural areas. However, most current research focuses on theoretically optimizing the trajectory of unmanned aerial vehicle (UAV) relays/base stations in simplified geographic scenarios. These network-side attempts have failed to provide low-cost, practical solutions to remove today’s digital gap. This paper proposes a large-scale simulation methodology based on real-life, high-precision geographic data. With its help, we provide a user-centric approach to coverage extension using follow-me relays. We focus on the rural case exemplified by Indiana, U.S., and present quantitative coverage analyses via channel simulation. Our results show that one UAV relay added to follow the user of interest at 10m high can effectively bring over 20% more area into coverage from the user’s point of view. With the relay added at 50m, the most challenging channel the network has to deal with to guarantee 90% area-wise coverage will experience a 20dB path loss reduction. These site-specific analyses can also be applied to future wireless network planning problems, including network-side equipment deployment simulation and optimization for millimeter-wave and terahertz communications.

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve conver-gence speed and reduce model bias through rapid local information exchange. However, data privacy concerns, device trust issues, and unreliable wireless channels each pose challenges to determining an effective yet resource efficient D2D structure. In this paper, we develop a decentralized reinforcement learning (RL) methodology for D2D graph discovery that promotes communication of non-sensitive yet impactful data-points over trusted yet reliable links. Each device functions as an RL agent, training a policy to predict the impact of incoming links. Local (device-level) and global rewards are coupled through message passing within and between device clusters. Numerical experiments confirm the advantages offered by our method in terms of convergence speed and straggler resilience across several datasets and FL schemes.

Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches.

This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1°, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU-R M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28 GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential models for the spatial/angular autocorrelation coefficient under distance and alignment effects.

Opportunistic spectrum access is a viable technique for cognitive radio (CR) networks to address the spectrum scarcity problem, where both spectrum sensing and resource allocation (SSRA) are significant to the system throughput performance. Previous works on SSRA often require complete network statistics which may not be feasible given the time-varying nature of practical CR networks. In this paper, we propose a learning-based optimization framework for SSRA in multi-band-multi-user CR networks. We develop a dynamic cooperative spectrum sensing strategy which allows secondary users to detect available spectrum bands of the primary user, followed by flexible power allocation for efficient data transmissions. To cope with the dynamic of channel and resource statistics, we propose an improved deep reinforcement learning scheme based on a maximum entropy-enabled actor critic algorithm. Numerical results demonstrate the superiority of our approach over existing schemes.

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high. Our code is available at https://anonymous.4open.science/r/RCode1.

Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such stragglers is to incorporate MDS (maximum distance separable) codes into the framework; this can achieve resilience against an optimal number of stragglers. However, these codes assign dense linear combinations of submatrices to the workers which increase the number of non-zero entries in the encoded matrices, and adversely affect the worker computation time. In this work, we develop a straggler-optimal distributed matrix computation approach where the assigned encoded submatrices are linear combinations of a small number of submatrices so that it is well suited for sparse input matrices. Numerical experiments conducted in Amazon Web Services (AWS) demonstrate up to 30% reduction in worker computation time and 100 times faster encoding compared to several recent methods.

We consider point-to-point communication with partial noiseless feedback in which the number of feedback bits is $O(1)$ in the number of transmitted symbols. For $q \geq 2$, we study the general q-ary alphabet setting with both errors and erasures and seek to characterize the zero-error capacity. As our main result, we provide a tight characterization of zero-error capacity which we prove via novel achievability and converse schemes inspired by the study of causal/online adversarial channels without feedback. Perhaps surprisingly, we show that $O(1)$-bits of feedback are sufficient to achieve the zero-error capacity of the error channel with full noiseless feedback when the fraction of transmitted symbols in error is sufficiently small.

This work considers the problem of sending a 1-bit message over an acyclic network, where the “edge” connecting any two nodes is a memoryless binary-input/symmetric-output (BISO) channel. For any arbitrary acyclic network topology and constituent channel models, a min-cut-based converse of the learning rate, denoted by r*, is derived. It is then shown that for any r*, one can design a scheme with learning rate r. Capable of approaching the optimal r*, the proposed scheme is thus the asymptotically fastest for sending one bit over any acyclic BISO-channel network. The construction is based on a new concept of Lossless Amplify-&-Forward, a sharp departure from existing multi-hop communication scheme designs.

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency; 2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service; 5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existing challenges of mmWave communication and adds new challenges associated with the wider bandwidths, more antennas, and harsher propagations. Notably, the frequency- and spatial-wideband (dual-wideband) effects are significant at sub-THz. This paper presents a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels. A set of frequency-dependent array response matrices are constructed, enabling channel recovery from multiple observations across subcarriers via multiple measurement vectors (MMV). Using the temporal correlation, MMV least squares (LS) is designed to estimate the channel based on the previous beam support, and MMV compressed sensing (CS) is applied to the residual signal. We refer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based (M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging the spreading loss structure, a channel refinement algorithm is proposed to estimate the path coefficients and time delays of the dominant paths. To reduce the computational complexity and enhance the beam resolution, a sequential search method using hierarchical codebooks is developed. Numerical results demonstrate the improved channel estimation accuracy of MMV-LS-CS over state-of-the-art techniques.

Many emerging applications require multi-hop wireless relaying, for which reliability requirements increase packet retransmissions, amplifying latency across hops. Existing works mostly focus on the simple two-hop, single-relay setting and ignore spatial diversity that enables a destination to receive independent noisy copies of data from multiple relays in parallel to improve error performance. In this paper, we consider a single-bit source message and construct learning-rate-optimal, delay-constrained multi-hop schemes by jointly designing time-varying, distributed relay mapping functions with destination decoding strategies. The learning-rate-optimal scheme, however, requires channel and relaying knowledge, which limits practical implementation. With the aim of practical implementation, we have also considered several low-complexity relay and destination strategies and analyzed their performances under different combinations. Numerical comparisons show that none of the alternatives universally dominate, and the system designer thus has to carefully opt for the best suitable schemes depending on the channel conditions. Finally, we show that carefully coordinating many low-quality relay channels in parallel can vastly outperform having only one high-quality relay channel, which demonstrates the spatial diversity gains for the first time in the learning over parallel-relay setting.

Multi-hop line networks have emerged as an important abstract model for modern and increasingly dense communication networks. In addition, the growth of real-time and mission-critical services has created high demand for and increased research interest in low-latency communications. The combination of these facts motivates a new investigation of data transmission schemes for$L$-hop line networks from a delay-vs-throughput perspective. To this end, this work defines a metric called the delay amplification factor for a target throughput$R$, denoted by${\mathsf {DAF}}(R)$, which characterizes the growth rate of the (asymptotic) delay with respect to the number of hops. We show that all existing relay schemes, e.g., Decode-&-Forward (DF), have$\lim _{R\nearrow C} {\mathsf {DAF}}(R)=\Omega (L)$, which is consistent with the decades-old perception that delay grows linearly with respect to$L$. We then design a scheme satisfying$\lim _{R\nearrow C} {\mathsf {DAF}}(R)=1$, if the bottleneck hop is the last hop, i.e., its asymptotic delay does not grow with respect to$L$. The results imply that this linearly growing delay is an artifact of the existing DF designs, and it is possible to surpass it and attain the true fundamental limit with a new delay-centric solution. In the second half of this work, we further show that if variable-length coding and one-bit stop-feedback are allowed, we can relax the condition bottleneck being the last hop and attain$\lim _{R\nearrow C} {\mathsf {DAF}}(R)= 1$for any arbitrary line networks.

Network sharing is widely accepted as a cost effective approach for mobile network deployment. It remains uncertain, however, how regulators will evaluate network sharing agreements (NSA) for future networks in the context of the current competition law. For example, 5G mobile network operators (MNOs) seeking to enter NSAs may risk legal challenges, as regulators have not given MNOs sufficient guidance for self-evaluation of their NSAs. One way for MNOs to reduce the risk of legal challenge is to avoid sharing variable costs in the NSA. However, constraining costs to be non-variable (i.e., fixed) rules out the use of most pricing mechanisms that have been widely adopted for dynamic resource trading between MNOs. In this article, we propose a network sharing framework to allow dynamic resource sharing without the use of resource pricing. To incentivize sharing without pricing, our framework presents sharing as a means for MNOs to differentiate services and better compete in the service market for profit. We evaluate our framework in a duopoly market model and demonstrate the economic and regulatory viability of our framework.

We propose cooperative edge-assisted dynamic federated learning (CE-FL).CE-FLintroduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while the model training is conducted cooperatively at the end devices and the edge servers, enabled via data offloading from the end devices to the edge servers through base stations.CE-FLalso introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users’ mobility.CE-FLconsiders the heterogeneity of network elements in terms of communication/computation models and the proximity to one another.CE-FLfurther presumes a dynamic environment with online variation of data at the network devices which causes a drift at the ML model performance. We model the processes taken duringCE-FL, and conduct analytical convergence analysis of its ML model training. We then formulate network-awareCE-FLwhich aims to adaptively optimize all the network elements via tuning their contribution to the learning process, which turns out to be a non-convex mixed integer problem. Motivated by the large scale of the system, we propose a distributed optimization solver to break down the computation of the solution across the network elements. We finally demonstrate the effectiveness of our framework with the data collected from a real-world testbed.

Cooperation between network nodes is critical for supporting services in ad hoc networks. Cooperation, however, is an idealized assumption that may not always be present. This assumption can fail because of moral hazard, a scenario in part caused by misaligned incentives between the requesting node and supporting node. In this paper, we characterize a moral hazard that perversely incentivizes nodes to increase their routing payments by transmitting interference into the multi-hop network. We refer to this as the interference moral hazard (IMH) problem which is inherent to strategyproof mechanisms with low overpayments. We investigate IMH as a non-cooperative game played by network nodes on a random graph. For large networks, we show that IMH can be solved in the network design space. We provide sufficient conditions on the network distribution that guarantee an equilibrium path with interference-free play. This is achieved by 1) lower-bounding the number of nodes and 2) bounding the network density slightly above the 2-connectedness threshold and below a proposed upper-bound. Simulations suggest that density plays a fundamental role in IMH.

Accurate channel knowledge is critical in massive multiple-input multiple-output (MIMO), which motivates the use of channel prediction. Machine learning techniques for channel prediction hold much promise, but current schemes are limited in their ability to adapt to changes in the environment because they require large training overheads. To accurately predict wireless channels for new environments with reduced training overhead, we propose a fast adaptive channel prediction technique based on a meta-learning algorithm for massive MIMO communications. We exploit the model-agnostic meta-learning (MAML) algorithm to achieve quick adaptation with a small amount of labeled data. Also, to improve the prediction accuracy, we adopt the denoising process for the training data by using deep image prior (DIP). Numerical results show that the proposed MAML-based channel predictor can improve the prediction accuracy with only a few fine-tuning samples in various scenarios. The DIP-based denoising process gives an additional gain in channel prediction, especially in low signal-to-noise ratio regimes.

Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the n-bit codeword as input and computes an rn-bit output for some given r ∈ [0,1]. We consider the scenario where the rn-bit observation is computationally bounded – the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error. For all r ∈ [0,1 − H(p)] where H(•) is the binary entropy function and p is the adversary’s error budget, we characterize the capacity of the above channel. For this range of r, we find that the capacity is identical to the completely obvious setting (r = 0). This result can be viewed as a generalization of known results on myopic adversaries and channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.

An uplink non-orthogonal multiple-access communication system is developed for heterogeneous non-terrestrial networks (NTNs) that employ both low-Earth orbit (LEO) satellite constellations and high-altitude platforms (HAPs) as relays with hybrid automatic repeat request capabilities. The system is designed as a random-access and interference-resistant network to support low-rate users. A common waveform and frequency band is used for all uplink transmissions (whether the transmitter is a user terminal or a HAP). Both theoretical and simulation-based performance analysis results are provided as a function of user transmit power and packet arrival rates for channels with interference. The use of HAP relays is shown to significantly improve throughput and reduce queue size relative to direct transmission to the LEO satellites, especially for the case of low-power users in high interference environments.

Ultra-reliable low-latency communications (URLLC) is increasingly necessary for contemporary and future wireless networks. In-band full-duplex (IBFD) relaying has potential to boost spectral efficiency and decrease latency, but is limited by self-interference (SI) in practice. Many works investigate the overall capacity of the IBFD relay with SI, but few, if any, attempt to minimize the overall delay in a transmission of a block of information. We introduce a dynamic programming algorithm that shows how precoding at the relay transmitter may be dynamically leveraged to balance SI suppression and downlink rate optimization across channel uses to minimize the overall delay. We show the precoder at the relay transmitter is biased towards reducing SI early in a transmission before switching to a precoder biased towards improving the downlink rate. On average, our algorithm significantly exceeds the performance of using the overall relay capacity maximizing precoder for all channel uses and provokes interesting future research directions.

In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously. To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs. We then propose a new decentralized ML model aggregation solution at the edge layer based on a consensus mechanism to build a global ML model via peer-to-peer (P2P)-based blockchain communications. Blockchain builds trust among MDs and ESs to facilitate reliable ML model sharing and cooperative consensus formation, and enables rapid elimination of manipulated models caused by poisoning attacks. We formulate latency-aware BFL as an optimization aiming to minimize the system latency via joint consideration of the data offloading decisions, MDs’ transmit power, channel bandwidth allocation for MDs’ data offloading, MDs’ computational allocation, and hash power allocation. Given the mixed action space of discrete offloading and continuous allocation variables, we propose a novel deep reinforcement learning scheme with a parameterized advantage actor critic algorithm. We theoretically characterize the convergence properties of BFL in terms of the aggregation delay, mini-batch size, and number of P2P communication rounds. Our numerical evaluation demonstrates the superiority of our proposed scheme over baselines in terms of model training efficiency, convergence rate, system latency, and robustness against model poisoning attacks.

Federated learning has generated significant interest, with nearly all works focused on a “star” topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through thenetworkdimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra-and inter-layer model learning that considers the network as amulti-layer cluster-based structure.MH-FLconsiders thetopology structuresamong the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes asemi-decentralized architecturefor federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to formlocal consensuson the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence forMH-FLwith respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm forMH-FLto tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages ofMH-FLin terms of resource utilization metrics.

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a “best-effort” at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.

Medical applications have driven many areas of engineering to optimize diagnostic capabilities and convenience. In the near future, wireless body area networks (WBANs) are expected to have widespread impact in medicine. To achieve this impact, however, significant advances in research are needed to cope with the changes of the human body’s state, which make coherent communications difficult or even impossible. In this paper, we consider a realistic noncoherent WBAN system model where transmissions and receptions are conducted without any channel state information due to the fast-varying channels of the human body. Using distributed reception, we propose several symbol detection approaches where on-off keying (OOK) modulation is exploited, among which a supervised-learning-based approach is developed to overcome the noncoherent system issue. Through simulation results, we compare and verify the performance of the proposed techniques for noncoherent WBANs with OOK transmissions. We show that the well-defined detection techniques with a supervised-learning-based approach enable robust communications for noncoherent WBAN systems.

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are configured by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design, random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.

In this work, a compressed sensing-aided wideband MIMO-OFDM channel training framework is proposed to reduce the training overhead in slowly-varying channels with frequency- and spatial-wideband (dual-wideband) effects. To combat the beam squint effect, a set of frequency-dependent array response matrices are constructed, enabling the recovery of the sparse beamspace channel from multiple observations across OFDM subcarriers, via multiple measurement vectors (MMV). A channel training algorithm (MMV-LS-CS) is proposed to estimate slowly-varying multipath channel parameters: MMV least squares (MMV-LS) is first used to estimate the channel on the previous beam index support, followed by MMV compressed sensing (MMV-CS) on the residual to estimate the time-varying multipath components. Finally, a channel refining algorithm is proposed to estimate the gains and time delays of the dominant channel paths jointly on pilot subcarriers. Numerical results show that MMV-LS-CS achieves more accurate and robust channel estimation than the state-of-the-art approach on slowly-varying dual-wideband MIMO-OFDM: given a moderate SNR of 20 dB, our algorithm attains$\text{NMSE}=0.15$, as opposed to the state-of-the-art which attains$\text{NMSE}=0.43$in the same configuration. Besides, MMV-LS-CS necessitates$\text{SNR} =14\ \text{dB}$to achieve the spectral efficiency of 6 bit/s/Hz/stream, while the state-of-the-art scheme needs$\text{SNR}=17\ \text{dB}$to attain the same spectral efficiency.

In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large prelabeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori channel knowledge and pre-labeled data. Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates. Based on this process, we formulate the denoising mechanism as a Markov decision process, where we define the actions through a geometry-based channel estimation update, and the reward function based on a policy that reduces mean squared error (MSE). We then resort to Q-learning to update the channel estimates. Numerical results verify that our denoising algorithm can successfully mitigate noise in channel estimates. In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics.

Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.

The growing need for reliable communication over untrusted networks has caused a renewed interest in adversarial channel models, which often behave much differently than traditional stochastic channel models. Of particular practical use is the assumption of a causal or online adversary who is limited to causal knowledge of the transmitted codeword. In this work, we consider stochastic-adversarial mixed noise models. In the setup considered, a transmit node (Alice) attempts to communicate with a receive node (Bob) over a binary erasure channel (BEC) or binary symmetric channel (BSC) in the presence of an online adversary (Calvin) who can erase or flip up to a certain number of bits at the input of the channel. Calvin knows the encoding scheme and has strict causal access to Bob's reception through feedback snooping. For erasures, we provide a complete capacity characterization with and without transmitter feedback. For bit-flips, we provide converse and achievability bounds.

Future wearable and portable devices with multiple transmit antennas operating below 6 GHz are constrained by regulatory limitations on the level of electromagnetic radiation a user can be exposed to, measured using the specific absorption rate (SAR). Signaling designs that are optimized to include SAR constraints can improve the performance of uplink transmission. These signaling schemes could include closed-loop beamforming, closed-loop precoding, and space-time coding, which have all been shown to achieve increased rates when optimized as a function of SAR. Previous research addressed SAR constrained optimization only within a single coherence time block. In this paper, we present transmit policies that dynamically allocate user electromagnetic radiation exposure over time. We propose three exposure allocation methods - optimal, uniform, and asymptotic - in the practical case with causal channel state information (CSI), and an on-off transmission approach for the low SAR-to-noise ratio regime. Our results demonstrate that the performance of SAR-aware transmission can be further improved by exploiting frequency and time diversity.

The number of applications requiring signal classification continues to climb, fueled at least partly by the increase in sophistication and throughput of mobile devices. One particular use case of interest is when a sensor can record samples, process the samples, and transmit this data. In this paper, we are interested in understanding the design and behavior of these relay-like classification nodes. We propose a system model consisting of a compress-and-forward relay network where the data at a given relay node is quantized and broadcasted to a fusion center which will determine a corresponding class label for the sample data using online process. In this context, we propose and study an online kernel scalar quantization learning strategy to estimate the decision function and associated empirical conditional probabilities to enhance the overall classification accuracy rate. In doing so, we devise a jointly optimum classification quantization approach that can be applied in a variety of settings in signal processing, machine learning, and communications.

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.

With the rapid popularity of unmanned aerial vehicles (UAVs, also known as drones), UAV data relay has demonstrated potential extending wireless communication coverage, especially for rural areas. The flexibility of this approach has attracted research attention from a variety of areas, including Internet of Things, intelligent transportation systems, and digital agriculture. However, most current research effort focuses on modeling and theoretically optimizing data relay systems via UAV trajectories in simplified geographic environments, while taking advantage of UAVs for practical wireless communication networks requires large-scale quantitative performance analysis results based on real-life environment information. In this paper, we propose algorithms for generating large-scale blockage and path loss maps via terrain-based channel modeling for cellular communication systems with fixed-height relay drones. Our analyses reveal the coverage ratios for Tippecanoe County and the Wabash Heartland Innovation Network region in Indiana, with relay drones simulated at different heights. A coverage ratio gain over 40% can be achieved at a drone height of 100 m, compared to a typical pedestrian height of 1.5 m. These site-specific analyses are important in locating poorly covered spots and quantifying the coverage improvement from UAV data relay.

In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. For distributed computing, resource management is challenging due to limited network and communication resources, leading to inter-channel interference. To overcome this, recent research has addressed the problems of wireless scheduling, subchannel allocation, power allocation, and multiple-input multiple-output (MIMO) signal design, but has not considered them jointly. In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency. Given that the resulting problem is a non-convex mixed integer program (MIP) which is prohibitive to solve at scale, we decompose its solution into two parts: (i) a resource allocation subproblem, which optimizes the link selection and subchannel allocations, and (ii) MIMO signal design subproblem, which optimizes the transmit beamformer, transmit power, and receive combiner. Simulation results using wireless edge topologies show that our method yields substantial improvements in energy efficiency compared with cases of no offloading and partially optimized methods and that the efficiency scales well with the size of the network.

There has been a continuing demand for improving the accuracy and ease of use of medical devices used on or around the human body. Communication is critical to medical applications, and wireless body area networks (WBANs) have the potential to revolutionize diagnosis. Despite its importance, WBAN technology is still in its infancy and requires much research. We consider body channel communication (BCC), which uses the whole body as well as the skin as a medium for communication. BCC is sensitive to the body's natural circulation and movement, which requires a noncoherent model for wireless communication. To accurately handle practical applications for electronic devices working on or inside a human body, we configure a realistic system model for BCC with on-off keying (OOK) modulation. We propose novel detection techniques for OOK symbols and improve the performance by exploiting distributed reception and supervised-learning approaches. Numerical results show that the proposed techniques are valid for noncoherent OOK transmissions for BCC.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the fifth-generation (5G) networks, it is time for the research community to look for new multiple antenna technologies to meet the immensely higher data rate, reliability, and traffic demands in the beyond 5G era. Radically new approaches are required to achieve orders-of-magnitude improvements in these metrics. There will be large technical challenges, many of which are yet to be identified. In this paper, we survey three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive MIMO, and intelligent reflecting surfaces. For each of these technologies, we present the fundamental motivation, key characteristics, recent technical progresses, and provide our perspectives for future research directions. The paper is not meant to be a survey/tutorial of a mature subject, but rather serve as a catalyst to encourage more research and experiments in these multiple antenna technologies.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

To account for the ever-increasing demand for wireless broadband, it is critical to develop new techniques that can increase throughput. Network-coded (NCed) hybrid automatic repeat request (HARQ) has previously been shown to hold much potential for increasing network throughput by utilizing the retransmissions and side-information. However, the previous performance analysis did not take into account that the implementation of NCed HARQ requires additional control information that indicates which packets are included in one network coded packet and additional ARQ acknowledgements for the packets overheard by the unintended users. In this paper, we analyze the resources required for additional overhead and derive the outage probability and throughput for both downlink and uplink. With the analytical expressions and Monte Carlo simulation results, we study the trade-off between the maximum number of users (MNU) for NCed HARQ system and the overall throughput. Finally, we numerically show the existence of the optimal MNU and derive an approximate closed-form expression for the optimal value.

Thanks to the notice of the authors of[1], it should be noted that, due to a missing factor, the following corrections are appropriate for the original paper[2]:

The abstract model of the multi-hop relay channel is fundamental to a vast variety of modern communication systems. This fact, coupled with the demand for ultra-reliable-low-latency communication (URLLC), motivates a new investigation of relay channels from a delay-vs-throughput perspective. This work seeks to analyze this tradeoff in the regime of asymptotically large, yet still finite delay. A new metric called the Delay Amplification Factor (DAF) is introduced, which allows analytic comparison of the asymptotic delay across different relay solutions, e.g. decode-&-forward (DF), compress-&-forward, etc. The optimal DAF (over all possible existing/future designs) is then characterized for two special settings, one with fixed-length coding and one with variable-length coding and 1-bit stop feedback. The results show that under some general conditions, the optimal end-to-end delay over an L-hop line network is asymptotically comparable to the delay over the single bottleneck hop, and it does not grow linearly with respect to L. The linearly growing delay penalty commonly encountered in DF and other schemes is thus an artifact rather than a fundamental limit of multi-hop relay communication.

Millimeter wave (mmWave) communication links for 5G cellular technology require high beamforming gain to overcome channel impairments and achieve high throughput. While much work has focused on estimating mmWave channels and designing beamforming schemes, the time dynamic nature of mmWave channels quickly renders estimates stale and increases sounding overhead. We model the underlying time dynamic state space of mmWave channels with a linear Gauss-Markov process and design sounding beamformers suitable for tracking in an unscented Kalman filtering framework. Given an initial channel estimate, simulation results show that unscented filtering efficiently leads to better estimates than the extended Kalman filter with limited sounding and allows forward prediction for higher sustained beamforming gain during data transmission. Moreover, from tracked prior channel estimates, optimal and constrained suboptimal beams are adaptively chosen for low sounding overhead while minimizing estimation error.

There has been a strong interest in understanding hybrid precoding tradeoffs for millimeter wave (mmW) multi-input multi-output (MIMO) systems. A common assumption in most of these works is that the analog part of the hybrid precoder can only be designed with phase shifters. The consequent search for analog and digital precoding matrices is solved with different black box-type optimization algorithms. In contrast, this work motivates an analog precoding structure at the base-station end that can be realized with both phase shifters and gain controls. Such a structure is necessary for interference management in multi- user transmissions and is easily realized with low complexity and cost. We then propose a feedback framework of the top-P beams over a beam alignment phase from each user. This framework allows the base-station to reconstruct the channel matrix between it and each user, and to manage interference with a simple zeroforcing solution. Such a structured solution is implemented with the amplitude and phase control of the analog part of the hybrid precoder. We finally illustrate the performance improvement with the proposed solution over simpler constructions such as beam steering that can be implemented with phase shifters alone.

This paper presents millimeter wave propagation measurements at 28 GHz for a typical suburban environment using a 400-megachip-per-second custom- designed broadband sliding correlator channel sounder and highly directional 22-dBi (15° half-power beamwidth) horn antennas. With a 23-dBm transmitter installed at a height of 27m to emulate a microcell deployment, the receiver obtained more than 5000 power delay profiles over distances from 80m to 1000m at 50 individual sites and on two pedestrian paths. The resulting basic transmission losses were compared with predictions of the over-rooftop model in recommendation ITU-R P.1411-9. Our analysis reveals that the traditional channel modeling approach may be insufficient to deal with the varying site-specific propagations of millimeter waves in suburban environments. For line-of-sight measurements, the path loss exponents obtained for the close-in (CI) free space reference distance model and the alpha-beta-gamma (ABG) model are 2.00 and 2.81, respectively, which are close to the recommended site-general value of 2.29. The root mean square errors (RMSEs) for these two reference models are 9.93dB and 9.70dB, respectively, which are slightly lower than that for the ITU site-general model (10.34dB). For non-line-of-sight measurements, both reference models, with the resulting path loss exponents of 2.50 for the CI model and 1.12 for the ABG model, outperformed the site-specific ITU model by around 14dB RMSE.

Fifth generation wireless standards require much lower latency than what current wireless systems can guarantee. The main challenge in fulfilling these requirements is the development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. Since the available training resources are limited by the packet size, reliable channel and interference covariance estimation with reduced training overhead are crucial to any system using short data packets. In this paper, we propose an efficient receiver that exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. In the proposed method, the receive filter (i.e., the sample covariance matrix) is estimated using the received samples from the data transmission without using an interference training period. A channel estimation algorithm to use the most reliable data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed receiver algorithms enhance the reception quality of the short packet transmission.

Communication at millimeter wave frequencies will be one of the essential new technologies in 5G. Acquiring an accurate channel estimate is the key to facilitate advanced millimeter wave hybrid multiple-input multiple-output (MIMO) precoding techniques. Millimeter wave MIMO channel estimation, however, suffers from a considerably increased channel use overhead. This happens due to the limited number of radio frequency (RF) chains that prevent the digital baseband from directly accessing the signal at each antenna. To address this issue, recent research has focused on adaptive closed-loop and two-way channel estimation techniques. In this paper, unlike the prior approaches, we study a non-adaptive, hence rather simple, open-loop millimeter wave MIMO channel estimation technique. We present a random phase rotation design of channel subspace sampling signals and show that they obey the restricted isometry property (RIP) with high probability. We then formulate the channel estimation as a low-rank subspace decomposition problem and, based on the RIP, show that the proposed framework reveals resilience to a low signal-to-noise ratio. It is revealed that the required number of channel uses ensuring a bounded estimation error is linearly proportional to the degrees of freedom of the channel, whereas it converges to a constant value if the number of RF chains can grow proportionally to the channel dimension while keeping the channel rank fixed. In particular, we show that the tighter the RIP characterization the lower the channel estimation error is. We also devise an iterative technique that effectively finds a suboptimal, but stationary, solution to the formulated problem. The proposed technique is shown to have improved channel estimation accuracy with a substantially low channel use overhead as compared to that of previous closed-loop and two-way adaptation techniques.

We consider the problem of optimal precoder design for a multi-input single-output wideband wireless system to maximize two different figures of merit: the total communication capacity and the total received power, subject to individual power constraints on each transmit element. We show that the two optimal precoders satisfy a separation principle that reveals a simple structure for these precoders. We use this separation principle extensively to derive several interesting properties of these two optimal precoders. Some key analytical results are as follows. We show that the power-maximizing precoders must concentrate all their energy in a small number of active channels that cannot exceed the number of input terminals. The capacity-maximizing precoder turns out to be very different from the classical water filling solutions and also very different from the power-maximizing precoders except at asymptotically low SNRs where the power-maximizing precoders also maximize capacity. We also show that the capacity of the wideband system is lower bounded by the sum rate of a multiple-access channel with the same channel gains and power constraints. Finally, the separation principle also yields simple fixed-point algorithms that allow for the efficient numerical computation of the two optimal precoders.

Multiple-input multiple-output (MIMO) millimeter-wave (mm-wave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio-frequency hardware components. In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations. In this paper, we study the performance of an MIMO mm-wave hybrid beamforming scheme in the presence of nonlinear PAs. First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter. This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored. Then, by employing a realistic power consumption model for the PAs, we investigate the tradeoff between spectral and energy efficiency in such systems. Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency. Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes.

Wireless relay networking has been proposed as a solution for extending coverage in the past few decades. The relay network facilitates communication between users who are unable to reliably share information due to severe pathloss or blockage. In this paper, we utilize spatial diversity of distributed multiple-input multiple-output systems for the relay network. Hence, the relay network consists of many separate relay nodes. Due to limited computational power, we assume each relay node receives the sum of the transmissions from all users and then performs one-bit quantization. The quantization bits from the relay network are broadcast back to the users through a downlink channel that is modeled as a low-rate binary symmetric channel. Based on the noisy quantization bits from the relay network and its own prior transmission, each user detects the transmitted symbols from other users. We first derive the maximum likelihood detector for the described system. Then we develop a sub-optimal detector called the orthogonal subset maximum likelihood (OSML) detector, which exploits only a subset of relay nodes for detection, to reduce the computational complexity. By using combinatorial geometry, we derive the minimum number of required relay nodes for the OSML detector to operate. The derived results are verified through numerical simulations.

This paper introduces a system model for simultaneous information and power transfer (SWIPT) over inductively coupled circuits from the standard communication-theoretic perspective. It is shown that the rate-energy (R-E) regions, which characterize the performance of SWIPT, for inductively coupled circuits can be obtained by circuit analysis. To evaluate the performance, the R-E regions for SISO and SIMO circuit models are calculated by numerical analysis.

Magnetic resonance imaging (MRI) plays a critical role in medicine today. Over the last few years, there has been an interest in techniques that use multiple coils on one side of the imaging system such as parallel excitation or parallel imaging to improve performance in high-field MRI. In this paper, we explore the use of multiple coils at both the transmit and receive sides for enhanced imaging. We term this a multiple-input multiple-output (MIMO) MRI system. We use tools from the well-developed MIMO communication models and propose a novel radio-frequency (RF) pulse design method for multiple coils. We show that our MIMO MRI techniques can provide an improved signal-to-noise ratio (SNR) in the reconstructed image.

We propose two novel, low-complexity techniques for beam alignment in time division duplexing (TDD) multiple-input multiple-output (MIMO) systems. The techniques are inspired by the power method, an iterative algorithm to determine eigenvalues and eigenvectors through repeated matrix multiplications and improve upon this simple idea by providing a better performance in the low-SNR regime. The first technique sequentially constructs a least-squares estimate of the channel matrix, which is then used to calculate the optimal beamformer/combiner pair. The second technique aims to mitigate the effect of additive noise by using a linear combination of the previously tried beams to calculate the next beam in the iteration. Simulation results provide insight on the performance of both algorithms in the presence of noise and compare them with similar techniques from the literature.

Network-coded (NCed) automatic repeat request (ARQ) techniques have been shown to provide significant throughput improvements over basic ARQ systems in two-way wireless systems. Most results derived so far, however, used the assumption of no extra overhead. In practical systems, NCed-ARQ requires more information exchange between base-station and end-nodes, and therefore it is crucial to study the impact of the extra-overhead on such systems. In this paper, we analyze the performance of a practical NCed-ARQ system. We assume M end-users wish to exchange information with a base-station. We derive first the average number of extra acknowledgments required to facilitate NCed-ARQ scheme. Then, we derive both downlink and uplink throughput expressions and study the tradeoff between feedback and re-transmission. Finally, we numerically optimize the throughput with respect to the number of end-users.

This paper studies the broadcast channel with M antennas at the base-station and M single antenna users. Most of the works in the literature assume perfect channel state information at both ends of each link. In this work, we assume that these links are spatially correlated and only the long-term statistical information corresponding to the covariance matrices of the links are available at both ends. We are interested in the design of beamforming vectors for data transmission to the M users to maximize the ergodic sum-rate obtained by treating interference as noise. In the simpler M = 2 setting, our prior work has shown the optimality of a generalized eigenvector beamformer structure for this problem. However, these results are not easily extendable to the general M user setting. We overcome these difficulties by first establishing a tractable approximation for the ergodic sum-rate in terms of the beamforming vectors and covariance matrices that is asymptotically tight in M. We then cast the sum-rate approximation maximization problem as a manifold optimization and illustrate the optimality of the generalized eigenvector structure in the general M user setting.

A primary challenge for deploying dense small-cell networks comes from the lack of practical techniques that efficiently handle the increased network interference at a low cost. This has aroused considerable interest in the design of distributed precoder/combiner coordination techniques that leverage channel reciprocity, while relying on the local channel state information (CSI) available at each communication end. We present, in this paper, a power-efficient distributed coordination technique for dense small-cell multiple-input multiple-output (MIMO) networks. We optimize the linear filters by minimizing the transmit power subject to target signal-to- interference-plus-noise ratios (SINRs). Although this strategy enhances the power efficiency, the considered optimization problem is non-convex and not directly solvable even under centralized coordination. To address this difficulty, we propose distributed filter adaptation and power allocation techniques that are based on the primal and its dual problem formulations. Under this construction, the two sub-problems, i.e., the linear filter design and power allocation problems, are separable. To solve them, we devise the distributed Jacobi-type power allocation and maximum SINR filter design techniques. Improved power efficiency of the proposed technique as compared to other existing distributed techniques is evidenced.

There is much discussion in industry and academia about possible technical solutions to address the growth in demand for wireless broadband. Massive multiple-input multiple-output (MIMO) systems are one of the most popular solutions to addressing this broadband demand in fifth generation (5G) cellular systems. Massive MIMO systems employ tens or hundreds of antennas at the base station to enable advanced multiuser MIMO communications. To reap the massive MIMO throughput gain, coherent transmission exploiting accurate channel state information at the transmitter is required. While it is expected that many 5G systems will employ frequency division duplexing (FDD), channel sounding for FDD systems requires a large pilot overhead, which usually scales proportionally to the number of transmit antennas. To resolve this problem, a compressed sensing (CS)-aided channel estimation scheme is proposed, which exploits the observation that the channel statistics change slowly in time. By utilizing a conventional least squares approach and a CS technique simultaneously, the proposed scheme reduces the pilot overhead. Simulation results show that the proposed scheme can estimate the channel with a reduced pilot overhead even when conventional CS cannot be applied.

Fifth generation wireless networks are expected to utilize wide bandwidths available at millimeter wave (mmWave) frequencies for enhancing system throughput. However, the unfavorable channel conditions of mmWave links, such as, higher path loss and attenuation due to atmospheric gases or water vapor, hinder reliable communications. To compensate for these severe losses, it is essential to have a multitude of antennas to generate sharp and strong beams for directional transmission. In this paper, we consider mmWave systems using uniform planar array (UPA) antennas, which effectively place more antennas on a 2-D grid. A hybrid beamforming setup is also considered to generate beams by combining a multitude of antennas using only a few radio frequency chains. We focus on designing a set of transmit beamformers generating beams adapted to the directional characteristics of mmWave links assuming a UPA and hybrid beamforming. We first define ideal beam patterns for UPA structures. Each beamformer is constructed to minimize the mean squared error from the corresponding ideal beam pattern. Simulation results verify that the proposed codebooks enhance beamforming reliability and data rate in mmWave systems.

Millimeter wave (mm-wave) communication is expected to be widely deployed in fifth generation (5G) wireless networks due to the substantial bandwidth available for licensed and unlicensed use at mm-wave frequencies. To overcome the higher path loss observed at mm-wave bands, most prior work focused on the design of directional beamforming using analog and/or hybrid beamforming techniques in large-scale multiple-input multiple-output systems. Obtaining potential gains from highly directional beamforming in practical systems hinges on sufficient levels of channel estimation accuracy, where the problem of channel estimation becomes more challenging due to the substantial training overhead needed to sound all directions using a high-resolution narrow beam. In this paper, we consider the design of multi-resolution beamforming sequences to enable the system to quickly search out the dominant channel direction for single-path channels. The resulting design generates a multilevel beamforming sequence that strikes a balance between minimizing the training overhead and maximizing beamforming gain, where a subset of multilevel beamforming vectors is chosen adaptively to maximize the average data rate within a constrained time. We propose an efficient method to design a hierarchical multi-resolution codebook utilizing a Butler matrix, i.e., a generalized discrete Fourier transform matrix. Numerical results show the effectiveness of the proposed algorithm.

Fifth generation (5G) and beyond cellular systems are expected to support multiple uplink transmit antennas. Previous research demonstrates that designing waveforms satisfying near field user exposure constraints affects the farfield data rates achievable by portable devices using multiple transmit antennas. Therefore, user exposure constraints need to be taken into account in the uplink transmission covariance matrix design (e.g., precoder design) for 5G. Specific absorption rate (SAR) is a widely accepted user exposure measurement used in wireless communication regulations throughout the world. In this paper, we perform sum-rate analysis for a multi-user multiple-input multiple-output (MIMO) system with SAR constraints enforced at each user. The maximum achievable sum rates for various channel state information at the transmitter scenarios are studied in this paper. The SAR-aware MIMO transmission methods are based on the modified waterfilling algorithm. Simulation results show our proposed methods outperform the conventional transmission strategy for the two user case.

5G wireless standards require a much lower latency than what current wireless systems can guarantee. The main challenge to fulfill this requirement is the capability to support short packet transmission, in contrast to most of the current standards which use a long data packet structure. In this paper, we propose an efficient receiver technique that exploits information obtained during the data transmission period to improve the reception quality of the short packet transmission. Two key ingredients of the proposed method are 1) estimation of the receiver filter using the received samples in the data transmission period, not in the interference training period, and 2) soft decision- directed channel estimation that uses the data symbols for re-estimation of the channels. Numerical results confirm the effectiveness of the proposed receiver algorithms.

Uniform planar antenna (UPA) structures are expected to become popular for massive multiple-input multiple-output (MIMO) systems because they enable deployment of a large number of antennas in limited space. In frequency division duplexing (FDD) systems, quantized channel state information (CSI) should be fed back from users to base stations to make adaptive transmission possible. However, it is difficult to accurately quantize massive MIMO channels due to their large dimensions. In this paper, we propose practical CSI quantizers for full-dimension (FD) MIMO systems employing UPAs. We focus on quantizing and combining dominant beams in channels by keeping realistic channel properties and antenna structures into account. To scan a channel space, we also develop a multi-round beam search approach. Numerical results verify that the proposed quantizers show better quantization performance than the previous channel quantization techniques.

Millimeter wave multiple-input multiple-output (MIMO) communication systems must operate over sparse wireless links and will require large antenna arrays to provide high throughput. To achieve sufficient array gains, these systems must learn and adapt to the channel state conditions. However, conventional MIMO channel estimation can not be directly extended to millimeter wave due to the constraints on cost-effective millimeter wave operation imposed on the number of available RF chains. Sparse subspace scanning techniques that search for the best subspace sample from the sounded subspace samples have been investigated for channel estimation. However, the performance of these techniques starts to deteriorate as the array size grows, especially for the hybrid precoding architecture. The millimeter wave channel estimation challenge still remains and should be properly addressed before the system can be deployed and used to its full potential. In this work, we propose a sparse subspace decomposition (SSD) technique for sparse millimeter wave MIMO channel estimation. We formulate the channel estimation as an optimization problem that minimizes the subspace distance from the received subspace samples. Alternating optimization techniques are devised to tractably handle the non-convex problem. Numerical simulations demonstrate that the proposed method outperforms other existing techniques with remarkably low overhead.

Acquiring reliable channel state information (CSI) at the basestation is crucial to fully exploit the advantages of massive multiple-input multiple-output (MIMO) systems. However, giving the basestation knowledge of the downlink channel is an important and challenging problem in frequency division duplexing (FDD) systems when the number of basestation antennas is large. In this paper, we propose an eigenvalue decomposition based feedback compression (EFC) framework that transforms the channel information for pursuing further reduction in feedback overhead. The main idea of EFC is to use the compressed channels for generating short-term CSI by exploiting the dominant eigendirections of long-term channel-statistical information. By using the proposed codebook, each user terminal sends the compressed channel information as short-term feedback and channel-statistical information as long-term feedback. Numerical results demonstrate that the proposed method achieves significant feedback overhead reduction over the conventional beamforming techniques under the same target sum rate requirement.

In this paper, we consider a multiple-access broadcast channel (MABC) with ARQ feedback, in which M endusers wish to exchange messages with a central node or basestation. In this scenario, an end-user may overhear other endusers' messages prior to the re-transmission phase. We propose a new network coded (NCed)-ARQ scheme with reverse-link-assistance (RLA) that exploits this overheard information in uplink transmission to increase the downlink throughput. We derive throughput expressions for the new NCed-ARQ scheme in wireless additive white Gaussian noise with Rayleigh fading channel, which we numerically evaluate. For low/moderate SNRs, NCed-ARQ with RLA greatly improves the performance of downlink throughput.

A heterogeneous system of a Massive multiple- input-multiple-output (MIMO) macro cell with low power ancillary small cells can achieve higher spectral and energy efficiency than a Massive MIMO macro cell alone. The performance of such heterogeneous system is examined in this paper. A few small cells are used to enhance the spectral and energy efficiency of the overall system. Macro Massive MIMO base station uses a large number of antennas, which enables accurate nulling of small cell users to suppress the interference between macro cell and small cells. We derive analytical expressions for the capacity and signal-to- interference-plus-noise-ratio (SINR) lower bounds for both the downlink (DL) and uplink (UL) of the heterogeneous Massive MIMO system. Our simulation results show that nulling from macro cell Massive MIMO is essential for small cells' good and stable performance.

Spatial modulation (SM) is a new modulation technique where the information to be sent is encoded using one or more symbols and the subspace over which the symbols are transmitted. Unfortunately, a general capacity analysis that encompasses different forms of SM systems has not been developed. In this paper, we consider a general form of SM, where the number of transmitted data streams is allowed to vary. We refer to this form of SM as generalized spatial modulation with multiplexing (GSMM). A Gaussian mixture model (GMM) is shown to accurately model the transmitted spatially modulated signal using a precoding framework. Using this transmit model, a closed-form expression for the achievable rate when operating over Rayleigh fading channels is evaluated, and tight upper and lower bounds for the achievable rate are proposed. The expressions of the achievable rate obtained are flexible enough to accommodate any form of SM—where any subspace can be used for the transmission—by adjusting the precoding set. Simulations are presented to show the tightness of the proposed bounds. The effect of the system dimensions and a comparison with other prominent capacity results are also demonstrated in simulations.

Massive multiple-input multiple-output (MIMO) communication is an emerging technology that uses an excess of transmit antennas to realize high spectral efficiency. Achieving potential gains with large-scale antenna arrays hinges on sufficient channel estimation accuracy. Much prior work focuses on time-division duplex (TDD)-based networks, relying on reciprocity between the uplink and downlink channels. However, most currently deployed commercial wireless systems are frequency-division duplex (FDD)-based, making it difficult to exploit channel reciprocity. In massive MIMO FDD systems, the problem of channel estimation becomes even more challenging due to the substantial training resources and feedback requirements which scale with the number of antennas. In this paper, we consider the problem of training sequence design and the mapping of training signals to training periods. We focus on reduced-dimension training sequence designs, along with transmit precoder designs, aimed at reducing both hardware complexity and power consumption. The resulting designs are extended to hybrid analog-digital beamforming systems, which employ a limited number of active RF chains for transmit precoding, by applying the Toeplitz distribution theorem to large-scale linear antenna systems. A practical guideline for training sequence parameter selection is presented along with performance analysis.

Wireless spectrum sharing techniques have become very important due to the increasing demand for spectrum. Hence, there is growing interest in using real-time auctions for economically incentivizing users to share their excess spectrum. However, the communication and control requirements of real-time secondary spectrum auctions would be overwhelming for wireless networks. Prior literature has not considered critical communication constraints such as bid price quantization and error prone bid revelation. These schemes also have high overheads which cannot be accommodated in wireless standards. We propose auction schemes where a central clearing authority auctions spectrum to bidders, while explicitly accounting for these communication constraints. Our techniques are related to the posterior matching scheme, which is used in systems with channel output feedback. We consider several scenarios where the clearing authority's objective is to award spectrum to bidders who value spectrum the most. We prove that this objective is asymptotically attained by our scheme when bidders are nonstrategic with constant bids. We propose separate schemes to make strategic users reveal their private values truthfully, auction multiple subchannels among strategic users, and track slowly time-varying bid prices. We provide extensive simulation results to illustrate the performance and effectiveness of our algorithms.

The use of high-frequency millimeter wave (mmWave) bands for 5G communication systems has received much attention over the last few years. Analog-to-digital converters (ADCs) contribute significantly to the implementation cost and power consumption of wireless receivers. The use of large antenna arrays in mmWave communications causes these costs to rise even further. Using low precision quantizers in ADCs can reduce these costs significantly. In this paper, we propose a novel receiver design using low precision quantizers drawing ideas from the parallel ADC design literature. Utilizing structural similarities between multi-antenna receivers and parallel ADCs, we show that the signal-to-noise ratio and achievable rate, respectively, scale linearly and logarithmically with the number of antennas. We also extend the idea to the scenario where multiple streams can be transmitted simultaneously. Our simulations of the receiver show promising bit error rate performance under different scenarios and also show how error control coding can be incorporated to improve performance. All our designs depend only on symbol rate sampling, which eliminates costly oversampling of high bandwidth signals.

Massive multiple-input multiple-output (MIMO) with uniform planar arrays (UPAs), which is often referred to as full-dimension (FD) MIMO, is being strongly considered for future wireless communication standards. FD-MIMO can control transmit and receive beams in both the horizontal and vertical domains and fully exploit the large number of antennas of massive MIMO. It is widely accepted that Kronecker-product codebooks using a discrete Fourier transform (DFT) structure are suitable to quantize the downlink channel at the user for FD-MIMO systems relying on frequency division duplexing (FDD). In this paper, we numerically study the characteristics of FD-MIMO channels using a three-dimensional (3D) channel model that captures realistic channel properties. Based on the study, we identify a limitation of conventional Kronecker-product codebooks and propose advanced channel quantization techniques that can further improve channel quantization quality.

There is growing interest in using millimeter wave (mmWave) communication for fifth generation (5G) wireless systems because of the large bandwidth available in these frequencies. To overcome the higher path loss at mmWave bands, highly directional beamforming using large-scale, or massive, multiple-input multiple-output (MIMO) systems is critical. In this scenario, the problem of channel estimation becomes challenging due to the substantial training overhead needed to sound all possible beam directions using a high-resolution narrow beam. To tackle the issue of beam search in a mmWave system, we consider the use and design of multi-resolution beamforming sequences, which can quickly search out the dominant channel direction in a bisection manner. Given the multi-resolution codebook, the proposed multi-resolution beamforming sequence is designed to strike a balance between minimizing the training overhead and maximizing beamforming gain. We discuss how the multi-resolution codebook can be designed using a phase-shifted version of a {\em discrete Fourier transform} (DFT) matrix. Numerical results show the effectiveness of the proposed algorithm.

Massive multiple-input multiple-output (MIMO) systems hold the potential to be an enabling technology for 5G cellular. Uniform planar array (UPA) antenna structures are a focus of much commercial discussion because of their ability to enable a large number of antennas in a relatively small area. With UPA antenna structures, the base station can control the beam direction in both the horizontal and vertical domains simultaneously. However, channel conditions may dictate that one dimension requires higher channel state information (CSI) accuracy than the other. We propose the use of an additional one bit of feedback information sent from the user to the base station to indicate the preferred domain on top of the feedback overhead of CSI quantization in frequency division duplexing (FDD) massive MIMO systems. Combined with variable-rate CSI quantization schemes, the numerical studies show that the additional one bit of feedback can increase the quality of CSI significantly for UPA antenna structures.

Small cell networks utilizing millimeter wave (mmWave) links are expected to enhance system throughput, since the wide bandwidths at mmWave frequencies can afford high data rates. Due to mmWave's unfavorable channel conditions, it is necessary for mmWave communication systems to use beamforming with a large number of antennas to generate sharp and strong beams. In this paper, we propose a codebook design algorithm for the beamforming by considering the directional characteristic of mmWave links. The proposed codebook design algorithm can be easily adapted to different kinds of antenna arrays. Simulation results show that the proposed codebook outperforms previously reported codebooks for mmWave systems.

Massive multiple-input multiple-output (MIMO) systems can attain a high channel capacity and spectral efficiency by using a very large antenna array at the base station (BS). However, the cost of having one radio frequency (RF) chain behind every antenna element can be prohibitive. In addition, the overall power consumption of the RF hardware can be excessively high. A hybrid analog-digital structure can be utilized to reduce the required number of RF chains at the BS. In this paper, we present achievable rates of hybrid beamforming in multi-user MIMO (MU-MIMO) when employing only one RF chain per user. The analysis and simulation results show that the asymptotic signal-plus-interference-to-noise ratio (SINR) of hybrid beamforming is reduced by a factor of π/4 compared to conventional beamforming methods, and the resulting achievable sum-rate degradation can be compensated by simply employing 27% more transmit antennas.

Linear coding schemes have been the main choice of coding for the additive white Gaussian noise broadcast channel (AWGN-BC) with noiseless feedback in the literature. The achievable rate regions of these schemes go well beyond the capacity region of the AWGN-BC without feedback. In this paper, a concatenated coding design for the K-user AWGN-BC with noisy feedback is proposed that relies on linear schemes as inner codes to achieve rate tuples outside the no-feedback capacity region. The boundary of an achievable rate region of purely linear coding schemes for noiseless feedback is shown to be arbitrarily closely approached by the concatenated coding scheme for sufficiently small feedback noise levels. Then, a linear coding scheme for the K-user symmetric AWGN-BC with noisy feedback is presented and optimized for use in the concatenated coding scheme. For noiseless feedback, the presented linear coding scheme achieves the optimal sum-rate of linear-feedback coding. An upper bound on the inner code blocklength needed to achieve a sum-rate above no-feedback sum-capacity is derived.

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the base station. In order to achieve the performance that massive MIMO systems promise, accurate transmit-side channel state information (CSI) should be available at the base station. While transmit-side CSI can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user terminal to the base station is needed for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), maps multiple correlated antenna elements to a single representative value using predesigned patterns. The proposed method modifies the feedback packet by introducing the concept of a header to select a suitable group pattern and a payload to quantize the reduced dimension channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach performing the vector quantization of whole channel vector under the same target sum rate requirement.

A linear beamformer design for the broadcast channel where the base station is equipped with Ntantennas and signals to M = 2 users (each with a single antenna) and where the vector channels are spatially correlated is considered. This problem is of relevance in wireless standardization efforts where two users are simultaneously scheduled (instead of the theoretically feasible Ntuser scheduling) to minimize signaling overhead. The users are assumed to have perfect channel state information (CSI), whereas the base station has statistical information of the channels. In the first part of this work, the role of the relevance of feedback is studied via the quantification of the gap in ergodic sum-rate between the perfect CSI and statistics-only extremes. In this direction, the importance of orthogonality of the dominant eigenmodes of the two users in maximizing the gap is established. In the second part of this work, in scenarios with a large gap, limited feedback beamforming codebooks and a codeword selection metric are designed for the low-rate feedback setting. The main contribution here is the proposal of a generalized eigenvector codebook and feedback of the codeword index that maximizes an estimate of the signal-to-interference-and-noise ratio (SINR) of each user. Extensions of this scheme are also proposed for the general Ntantenna case with M users, where M ≤ Nt. It is shown via numerical studies that this scheme leads to significant performance improvement across a large family of channels over schemes such as Grassmannian/Random Vector Quantization/single-user codebooks with the channel projection metric for codeword selection.

It is of great interest to develop efficient ways to acquire accurate channel state information (CSI) for massive multiple-input-multiple-output (MIMO) systems using frequency division duplexing (FDD). It is theoretically well known that the codebook size (in bits) for CSI quantization should be increased as the number of transmit antennas becomes larger, and 3GPP Long Term Evolution (LTE) and LTE-Advanced codebooks have sizes that scale according to this rule. It is hard to apply the conventional approach of using unstructured and predefined vector quantization codebooks for CSI quantization in massive MIMO because of the codeword search complexity. In this paper, we propose a trellis-extended codebook (TEC) that can be easily harmonized with current wireless standards, such as LTE or LTE-Advanced, because it can allow standardized codebooks designed for two, four, or eight antennas to be extended to larger arrays by using a trellis structure. TEC exploits a Viterbi decoder for CSI quantization and a convolutional encoder for CSI reconstruction. By quantizing multiple channel entries simultaneously using standardized codebooks in a state transition of a trellis search, TEC can achieve a fractional number of bits per channel entry quantization and a practical feedback overhead. Thus, TEC can solve both the complexity and the feedback overhead issues of CSI quantization in massive MIMO systems. We also develop trellis-extended successive phase adjustment (TE-SPA), which works as a differential codebook for TEC. This is similar to the dual codebook concept of LTE-Advanced. TE-SPA can reduce CSI quantization error with lower feedback overhead in temporally and spatially correlated channels. Numerical results verify the effectiveness of the proposed schemes in FDD massive MIMO systems.

Low power sensor networks with communication enabled by WiFi are expected to be widely deployed. A major challenge is collecting event-driven uplink data from a large number of low-power sensors with low latency. In WiFi, the access point (AP) typically polls nodes individually to schedule uplink transmission times, resulting in a large latency. In this paper, we present a physical (PHY) layer-aided medium access control (MAC) framework to enhance the uplink throughput of sensor data traffic. In the approach, the acknowledgements from the sensor nodes to the poll message are parallelized. By detecting the parallel acknowledgement, the AP knows which nodes have data to send and allocates channel resources by sending a pull message. This approach is referred to as the probe and pull MAC (PPMAC) mechanism. Our scheme is based on maximizing the achievable throughput of PPMAC by optimizing the PHY layer components. More precisely, we investigate the parallel acknowledgement detector design problem and develop a non-convex optimization framework that maximizes the PPMAC throughput by optimizing the parallel acknowledgement detection statistics. Numerical examples illustrate that PPMAC outperforms the point coordination function (PCF) and distributed coordination function (DCF) mechanisms, standardized in IEEE 802.11, in terms of the achievable throughput and the overhead.

Physical layer network coding has attracted extensive theoretical interest, although relatively little research has been done in support of deployment to wireless networks where internode synchronization is difficult to achieve. In particular, wireless networks constructed with inexpensive and commercially available software defined radio technology, or more generally, radio front-end samplers connected to internet-based remote processors (i.e., the Internet of Things network) may exhibit large time, frequency, and phase offsets that are difficult to control. In this paper, we define an asynchronous discrete-time model that accounts for these impairments as part of the information transfer between network users and a relay. Derived from this model are maximum likelihood algorithms for relay parameter estimation and a symbol decoder inspired from asynchronous multi-user detection. Additionally, null space-based frequency offset estimation that reduces computational complexity is proposed. Simulation results and the design and performance of a two-user system implemented with the Universal Software Radio Peripheral (USRP) platform and GNU radio are included to demonstrate the proof of concept. Our results indicate that the physical layer network coding technique can be successfully deployed and yields significant benefits even in the presence of impairments found in practical settings.

Fifth-generation wireless systems are expected to employ multiple-antenna communication at millimeter wave (mmWave) frequencies using small cells within heterogeneous cellular networks. The high path loss of mmWave and the physical obstructions make communication challenging. To compensate for the severe path loss, mmWave systems may employ a beam alignment algorithm that facilitates highly directional transmission by aligning the beam direction of multiple antenna arrays. This paper discusses a mmWave system employing dual-polarized antennas. First, we propose a practical soft-decision beam alignment (soft-alignment) algorithm that exploits orthogonal polarizations. By sounding the orthogonal polarizations in parallel, the equality criterion of the Welch bound for training sequences is relaxed. Second, the analog beamforming system is adapted to the directional characteristics of the mmWave link, assuming a high Ricean K-factor and poor scattering environment. A soft-alignment algorithm enables the mmWave system to align a large number of narrow beams to the channel subspace in an attempt to effectively scan the mmWave channel. Third, we propose a method to efficiently adapt the number of channel sounding observations to the specific channel environment based on an approximate probability of beam misalignment. Simulation results show that the proposed soft-alignment algorithm with adaptive sounding time effectively scans the channel subspace of a mobile user by exploiting polarization diversity.

Mobile handsets, classified as portable devices, are regulated on the amount of user electromagnetic exposure. The widely accepted exposure measurement is the specific absorption rate (SAR). Despite the prevalence of SAR constraints throughout the world, there has been barely any work on the design and analysis of communication signals for SAR-constrained wireless systems. In this paper, we show that multiple-antenna systems greatly reduce the SAR measurements when proper precoders are used. Fifth-generation (5G) and beyond cellular systems will be expected to support high rate uplinks, making multiple transmit antennas on user equipment a necessity. Our proposed SAR-aware transmission for multiple-antenna systems can be applied in 5G handsets to reduce the SAR and increase the rate. Assuming that channel knowledge is available at the transmitter and the receiver, we perform capacity analysis for multiple-antenna systems under both transmit power and SAR constraints. Analytical and numerical results demonstrate substantial performance improvements over schemes that ignore the SAR constraint. Our work shows that SAR-constrained precoders have structures similar to precoders designed for spatially correlated channels.

Interest in massive multiple-input multiple-output (MIMO) systems is growing because of their potential ability to improve spectral and energy efficiency. Most prior work on massive MIMO considers TDD operation that relies on channel reciprocity between uplink and downlink channels, whereas most current cellular systems adopt FDD without channel reciprocity. In an FDD mode, downlink channel estimation becomes a challenging issue due to the substantial training overhead that scales with the number of antennas, which can limit the potential gain of massive MIMO systems. To tackle the issue of channel estimation, we consider the design of a training codebook that has a suitable mapping for the training signal patterns in block transmissions under the assumption of a Kaiman filtering framework. We focus on a reduced dimensionality training codebook and transmit precoding design to enable low-complexity system configuration. We discuss how this framework can extend to hybrid analog-digital precoding using a limited number of active RF chains for transmit beamforming by applying the Toeplitz distribution theorem to large-scale linear antenna arrays. A practical guideline for training codebook parameters is presented, and numerical results show the effectiveness of the proposed algorithm.

Massive MIMO is a promising technology for next generation cellular networks. It differentiates from conventional MIMO systems because of the excessive number of transmit antennas at the base stations. To implement a massive MIMO system with FDD, a practical low-overhead downlink channel training and estimation method is required. We propose a codebook based feedback framework for FDD massive MIMO systems that divides the coverage area into sub-sectors, where each sub-sector is formed by a set of narrow beams that cover a pre-assigned area in azimuth and elevation. A feedback process is then used where the codebook based feedback is limited to the beams covering the sub-sector to which the user device belongs. Our simulation results show that the proposed feedback framework with a large 2D antenna array provides substantial performance improvement compared to existing LTE/LTE-Advanced systems that currently support no more than eight antenna ports.

In this paper, the design of training signals for channel estimation in massive multiple-input multiple-output (MIMO) systems is considered. Under a stationary, block Gauss-Markov channel model, a method for optimal pilot beam pattern design for enhanced channel estimation is proposed, exploiting both the properties of Kalman filtering and the spatio-temporal channel correlation. First, pilot beam pattern design is considered under the assumption of orthogonal beam patterns within a block. The orthogonality assumption is subsequently relaxed and the design problem is solved via a greedy approach. Numerical results show the efficacy of the proposed algorithm.

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the basestation. Although transmit-side channel state information (CSI) can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user to the basestation is required for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), groups antenna elements using pre-designed patterns. The proposed method introduces the concept of using a header of overall feedback resources to select a suitable group pattern and the payload to quantize the effective channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach.

A 2D antenna array introduces a new level of control and additional degrees of freedom in multiple-input-multiple-output (MIMO) systems particularly for the so-called “massive MIMO” systems. To accurately assess the performance gains of these large arrays, existing azimuth-only channel models have been extended to handle 3D channels by modeling both the elevation and azimuth dimensions. In this paper, we study the channel correlation matrix of a generic ray-based 3D channel model, and our analysis and simulation results demonstrate that the 3D correlation matrix can be well approximated by a Kronecker production of azimuth and elevation correlations. This finding lays the theoretical support for the usage of a product codebook for reduced complexity feedback from the receiver to the transmitter. We also present the design of a product codebook based on Grassmannian line packing.

Cellular wireless network architectures employing a tiered structure, consisting of traditional macro-cells and small-cells, have attracted much attention recently because of their potential to dramatically increase network capacity. These architectures can reduce the distances of transmit-receiver pairs, thus enhancing radio link qualities. However, a tiered cell deployment could incur severe cochannel interference. Prior work for single-tier networks has proposed coordinated multi-point (CoMP) transmission, which includes joint transmission, as a possible solution for overcoming the rate limitations induced by cochannel interference. Because users in each cell experience various interference conditions, each cell needs to support single transmission (in which only one cell transmits to a single user) and joint transmission. In a conventional implementation, a particular combination of transmissions over the entire network is fixed in advance for each time slot and opportunistic scheduling chooses one of the users supported by each transmission. However, when the number of users to be scheduled is small, the throughput improvement from opportunistic scheduling becomes limited. To overcome this problem, we propose a cumulative distribution function-based scheduling scheme which jointly chooses the transmitter set and the corresponding scheduled users in a two-tier network. It is shown that the throughput performance can be improved compared to those of the fixed slot resource allocation scheme because more users "compete" for slot resources.

This paper analyzes the performance of random vector quantization (RVQ) for limited feedback nullforming in multi-input multi-output (MIMO) communication systems with and without receiver coordination. A single-stream scenario is considered in which one or more primary receivers request nulls by providing limited feedback to the transmitter. Without receiver coordination, each primary receiver informs the transmitter of its best beamforming precoding vector. The transmitter then selects a zero-forcing precoding vector orthogonal to all of the beamforming precoding vectors. With receiver coordination, the primary receivers feed back the common precoding vector that minimizes the average interference. In both cases, secondary receivers in the network do not provide feedback and experience channels statistically equivalent to a single-antenna fading channel. Analytical results show that, for a system with K primary receivers and random codebooks with N=2Bprecoding vectors, the mean received power at the primary receivers is upper bounded by N-1/K= 2-B/Kwith or without receiver coordination. Exact results are also derived for the K=1 receiver case. Numerical results verify the scaling and also show that systems with receiver coordination outperform those without receiver coordination by a constant gap for large N in terms of average interference.

The broadcast problem with multiple antennas at the transmitter end and a single antenna at each user is studied in this work. A low-complexity transmitter architecture where linear beamforming is used to convey information to the users, and multi-user interference is treated as noise at the receiver unit of each user is considered. The vector channels that connect the transmitter to the users are assumed to be spatially correlated. The goal of this work is the design of a low-rate limited feedback framework to improve the achievable sum-rate of the system relative to a baseline scenario where only correlation information is known at the transmitter. While prior work considers either a codebook structure that is designed for spatially uncorrelated channels or for spatially correlated point-to-point systems, it is argued here that a “good” codebook design should incorporate a statistical version of the interference structure that is seen at each user. Further, instead of feeding back the codeword index that is best aligned to the user's channel vector, the index that maximizes an estimate of the signal-to-interference-and-noise ratio as perceived by the user is fed back. It is shown via numerical results that the proposed framework results in a substantial performance improvement over existing schemes across a large family of spatial correlation and across a practically relevant operating regime.

With the continuous evolution of cellular systems, portable wireless communication devices, such as mobile phones, are becoming more and more powerful, complex, and essential to our daily life. However, the debate over the health effects from using portable devices in close proximity has still not been settled. To limit the amount of user exposure, regulatory agencies in most countries set exposure threshold in terms of the specific absorption rate (SAR), measured in Watts per kilogram. SAR is a measure of the rate of electromagnetic energy absorption by the human body. Surprisingly, portable devices are often designed with little attention to the SAR thresholds, instead focusing on transmit power constraints. In this paper, we propose SAR-aware transmission that considers constraints on both transmit power and SAR with multiple antennas. We discover that the effect of a SAR constraint can be regarded in a way similar to a transmitter-side spatial correlation. Numerical results demonstrate the proposed method increases the capacity by 1.75 folds under the regulatory SAR limitation of 1.6 W/kg over schemes that only consider the power constraint.

Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold (for finding a vector in the codebook which maximizes beamforming gain) and noncoherent sequence detection (for maximum likelihood decoding subject to uncertainty in the channel gain). Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Monte Carlo simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.

Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access and backhaul. This paper proposes the use of outdoor millimeter wave communications for backhaul networking between cells and mobile access within a cell. To overcome the outdoor impairments found in millimeter wave propagation, this paper studies beamforming using large arrays. However, such systems will require narrow beams, increasing sensitivity to movement caused by pole sway and other environmental concerns. To overcome this, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a corresponding performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. The results show significant performance improvement with reduced search time.

It is crucial to have accurate channel state information at the transmit side to achieve the maximum performance in multiple-input multiple-output (MIMO) systems, especially for multi-user systems. Frequency division duplexing systems based on limited feedback facilitate this, but these techniques are reliant on carefully design codebooks that are optimized for various antenna deployment scenarios. In this paper we discuss the channel model for rotated-dual-polarized (RDP) antenna systems and propose an efficient way of designing practical differential codebooks for RDP scenarios. We assess the performance of the proposed differential codebook design technique by simulations, and it is shown that codebooks designed by the proposed technique outperform conventional fixed and differential codebooks with small number of codewords.

This work studies the capacity of the 2-receiver multiple-input/multiple-output (MIMO) broadcast packet erasure channels (PECs) with channel output feedback, which is in contrast with the single-input/single-output setting of the existing works. Motivated by the immense success of linear network coding (LNC) in theory and in practice, this work focuses exclusively on LNC schemes and characterizes the LNC feedback capacity region (R1*, R2*) of 2-receiver MIMO broadcast PECs. A new linear-space-based approach is proposed, which unifies the problems of finding a capacity outer bound and devising the achievability scheme into a single linear programming (LP) problem. Specifically, an LP solver is used to exhaustively search for the LNC scheme(s) with the best possible throughput, the result of which is thus guaranteed to attain the LNC feedback capacity.

In downlink multiuser multiple-input multiple-output (MIMO) systems, system performance highly depends on the reliability of downlink channel state information (CSI) at the base station (BS). In frequency division duplexing, the most practical solution is to have downlink CSI from the users fed back to the BS. Most work on this feedback design has assumed independent block fading channels. However, this paper proposes a new differential feedback scheme using the observation that the channel realizations are usually temporally correlated. The sum-rate loss assuming differential feedback is analyzed for a system with the number of users K equal to the number of transmit antennas M. When K >; M, a user selection algorithm based on an approximated signal-to-interference plus noise ratio (SINR) estimation is proposed. In simulation results, the proposed differential feedback scheme increases the sum-rate compared to previous differential feedback schemes. Moreover, the proposed user selection algorithm outperforms semi-orthogonal user selection in the moderate signal-to-noise ratio (SNR) region, despite requiring less feedback information. In low mobility channels, utilizing the channels' time correlation during quantization is shown to play a bigger role in determining sum-rate performance than multiuser diversity for most SNR regimes when a practical number of users is considered.

The above paper mistakenly employs the incorrect in- equalities of and in Appendix B, and as a result, it leads to incorrect derivation procedures of the lower and the upper-bounds on the average mutual information.

We consider a homogeneous multiple cellular scenario with multiple users per cell, i.e., K ≥ 1 where K denotes the number of users in a cell. In this scenario, a degrees of freedom outer bound as well as an achievable scheme that attains the degrees of freedom outer bound of the multicell multiple access channel (MAC) with constant channel coefficients are investigated. The users have M antennas, and the base stations are equipped with N antennas. The found outer bound is general in that it characterizes a degrees of freedom upper bound for K ≥ 1 and L >; 1 where L denotes the number of cells. The achievability of the degrees of freedom outer bound is studied for two cell case (i.e., L = 2). The achievable schemes that attains the degrees of freedom outer bound for L = 2 are based on two approaches. The first scheme is a simple zero forcing with M = Kβ+β and N = Kβ, and the second approach is null space interference alignment with M = Kβ and N = Kβ + β where β >; 0 is a positive integer.

In this paper, an adaptive limited feedback linear precoding technique for temporally correlated multiple-input multiple-output (MIMO) channels is proposed, where the receiver has perfect channel knowledge but the transmitter only receives a quantized channel direction. To perform adaptation to the time correlation structure, we employ a differential feedback, where the "amount" of the perturbation added to the previous precoder is determined by the statistics of the directional variation. Based on random matrix quantization analysis, we develop a spherical cap codebook approach, where the cap is centered at the previous precoder and the radius of the cap is determined proportional to the identified directional variation. If the channel is highly correlated in time, it is shown that the proposed differential feedback scheme achieves significant throughput improvement in the large codebook size regime. The rest of the paper is devoted to developing a systematic spherical cap codebook generation method. The developed approach employs a feedback scheme that uses a differential rotation of the previously used precoder. Our codebook adaptation is based on generating a perturbation in Euclidean space and projecting the perturbation onto the unitary space. Simulation results show that the proposed adaptation scheme accurately tracks the channel using only a small rate of feedback.

In modern wireless communications, systems that send a common information stream (called multicasting systems) are widely needed for distributing content such as television or radio. In this paper, the multiple antenna physical layer multicasting channel is considered, in which a common message is simultaneously transmitted to multiple single-antenna users. To achieve the capacity of this multicasting set-up, the covariance matrix of the transmit signal vector needs to be determined to maximize the smallest maximum achievable rate among all the users. In this paper, we develop a simple successive algorithm to determine the covariance matrix of the signal vector under the assumption that channel state information (CSI) is perfectly available at the transmitter. We first characterize properties of the capacity achieving covariance matrix and derive a closed-form expression for the matrix in the two-user case. We then derive a closed-form expression for the rate maximizing beamformer in the case of two users and propose a successive beamforming algorithm that generates the beamforming vector with low computational complexity. In the proposed precoding design scheme, the covariance matrix is successively constructed by orthogonalizing the subspace spanned by each user's channel vector until the maximum number of recursions (which is the same as the minimum of the number of transmit antennas and the number of users) is reached. The achievable rate of the proposed scheme is compared with the capacity of optimal transmission, transmit beamforming, antenna subset selection, and open-loop transmission which does not require CSI at the transmitter. As the number of users and/or antennas grows large, it is shown that the average achievable rate of the proposed scheme achieves the same average achievable rate scaling as the true capacity while reducing the computational complexity.

Wireless multicasting (also called common information broadcasting) is a technique where a common information message is transmitted to multiple users. This is typically accomplished by having the basestation broadcast out a signal representing this message. In this paper, we consider a multicasting scheme where the basestation transmits the multicasting signal without any a-priori knowledge of the users' channel state information. In this set-up, a hybrid automatic repeat request (ARQ) control is employed to improve the reliability of multicasting communication. In most hybrid ARQ set-ups, each user is usually allocated a dedicated feedback channel to tell the basestation if the previously transmitted signal was correctly decoded. However, dedicated feedback channels waste significant uplink resources, especially when the number of users is large. To mitigate this problem, we consider a negative acknowledgement (NACK) based hybrid ARQ control where the failed users are allowed to transmit the NACK signal (which is assumed to be the same for all users) through a common channel while the other users remain silent. We consider the effect of feedback error and multicasting signal decoding error on our hybrid ARQ system performance as the number of users grows large. Specifically, it is shown that the throughput performance of the proposed hybrid ARQ scheme using repetition retransmission is not severely degraded relative to the throughput performance of the noiseless feedback case. In addition, we show that the performance improvement obtained by replacing repetition encoding with incremental redundancy becomes insignificant for the large number of users asymptote. On the other hand, it is shown that the improvement provided by incremental redundancy is significant when some of the users are allowed to fail to decode the message.

The use of open-loop coding can be easily extended to a closed-loop concatenated code if the transmitter has access to feedback. This can be done by introducing a feedback transmission scheme as an inner code. In this paper, this process is investigated for the case when a linear feedback scheme is implemented as an inner code and, in particular, over an additive white Gaussian noise (AWGN) channel with noisy feedback. To begin, we look to derive an optimal linear feedback scheme by optimizing over the received signal-to-noise ratio (SNR). From this optimization, a linear feedback scheme is produced that is asymptotically optimal in the sense of blocklength-normalized SNR; it is then compared to other well-known schemes. Then, the linear feedback scheme is implemented as an inner code to a concatenated code over the AWGN channel with noisy feedback. This code shows improvements not only in error exponent bounds, but also in bit error rate (BER) and frame error rate (FER). It is also shown that if the concatenated code has total blocklengthLand the inner code has blocklength,N, the inner code blocklength should scale asN=O(C/R), whereCis the capacity of the channel andRis the rate of the concatenated code. Simulations with low-density parity-check (LDPC) and turbo codes are provided to display practical applications and their error rate benefits.

In this paper we study the beamforming vector quantization and feedback transmission problem for a cooperative transmission environment. Feedback is the primary means of providing the transmitter with channel state information (CSI) in a frequency division duplex (FDD) system. The conventional codebook approach uses a common codebook at the transmitter and at the receiver, while the feedback takes the form of the index of the codebook. This approach has a complexity that scales exponentially with the number of transmit antennas M, making the approach less favorable when M is large. We propose to use trellis-based quantization to reduce the complexity to grow linearly in M. A codebook design criteria is derived for the trellis-based quantizer. We also present three enhancements to the base scheme, each giving 0.2-0.8 dB gain. These enhancements address issues relevant to feedback error, trellis termination, and unequal path-loss from different transmission points. Performance results are shown in numerical simulations.

Allowing the receiver in a multiple antenna wireless system to send a limited amount of channel state information (CSI) feedback is an effective way to enable channel adaptive signaling. This paper addresses the problem of controlling the feedback update period and feedback rate of limited feedback multiple antennas systems in temporally correlated channels. The challenge in our problem is how to assign the feedback update period and feedback rate subject to a constraint on feedback overhead. The presented approach analyzes the required CSI feedback rate and feedback update period by maximizing a lower bound on the average normalized effective signal-to-noise ratio. By imposing the channel evolution structure and employing a random quantization argument, we are able to determine a closed-form solution. This result leads to a bound on the feedback rate that characterizes when the proposed feedback update control scheme outperforms the conventional feedback scheme. Both analytical and numerical results demonstrate that the proposed feedback control strategy improves the average effective SNR with a relatively small amount of feedback overhead.

In certain communications systems, it is a reasonable to assume the presence of a noisy feedback channel. The ability to send data from receiver back to transmitter opens new doors in the way of modulation schemes. In this paper, we investigate the class of linear feedback encoding schemes in which the transmitter linearly encodes the feedback information along with the message to be sent. Given a general framework, we propose a new scheme and compare it to a well-known linear encoding method - the Schalkwijk-Kailath scheme. The new technique is then optimized based on power allocation requirements.

This paper concerns a simple limited feedback scheme taking temporal correlation into account during the feedback design in slow fading environment. In this method, the transmitter and the receiver reuse the past channel state information (CSI) as side information. Feedback, that is designed to leverage the side information, is sent from the receiver to the transmitter using a predetermined update period. The feedback update period is determined by characterizing the temporal correlation statistic, so that the proposed feedback reuse scheme outperforms a feedback scheme that does not adapt to the temporal correlation. To measure the performance, average effective SNR loss is used. Bounds on the feedback update period and the amount of feedback needed are derived. Simulation results show a reduction in the required average feedback overhead and a performance improvement when comparing the proposed scheme with prior feedback approaches.

Communications systems operating over additive Gaussian noise channels with no feedback or ideal channel output feedback are now well studied. However, these set-ups model extreme cases and fail to provide much insight into more practical feedback systems. Therefore, in this paper, a noisy feedback communication system is considered. We study the application of linear multi-dimensional coding of the feedback and data signal. Upper bounds on the achievable rate for our set-up, that are functions of the open-loop capacity and ideal feedback capacity, are derived.

Physical layer multicasting in wireless networks has been proposed to efficiently send an identical message to multiple users simultaneously. In this paper, we consider multiple antenna multicasting where the transmitter is equipped with Mtantennas and data is transmitted to K single-antenna users. When the downlink channels are assumed to follow an uncorrelated Rayleigh distribution, the multicasting capacity scaling for the large K asymptote is known. On the other hand, the effect of channel spatial correlation on the capacity performance has not been well addressed. Therefore, we investigate the effect of correlation using the channel correlation information at the transmitter. Using extreme value theory, it is shown that signaling using uniformly allocated transmit powers on the spatial channel correlation matrix's eigenvectors with non-zero eigenvalues approaches the multicasting capacity for the large K asymptote. Compared to the performance of uncorrelated fading channels with a constraint on the trace of the spatial correlation matrix, it is shown that the channel correlation degrades the performance. Specifically, if the correlation matrix is full rank the asymptotic outage (average) capacity ratio, which is defined as the ratio of the outage (average) capacities of the correlated fading channel and the uncorrelated fading channel as K goes to infinity, is equivalent to the geometric mean of the eigenvalues of the transmit channel correlation matrix of the correlated channel. On the other hand, if the correlation matrix is not full rank, the capacity ratios become zero. To assess the value of knowledge of the channel correlation information at the transmitter, we compare the asymptotic performances with and without knowledge of the channel correlation information. Specifically, the asymptotic performance improvement due to this correlation knowledge becomes (Mt/M)1/Mfor the large K asymptote, where M is the rank of the transmit channel correlation matrix. In addition, we discuss the issues of nonidentical transmit channel correlations among the users and correlation between users' channels.

In this paper, we present a hybrid automatic repeat request (ARQ) scheme for wireless multicast with incremental redundancy channel coding and packet retransmission. With this scheme, we can reliably deliver the same copy of information to different users with mild delay. In addition, the design of the feedback channel for this scheme can be greatly simplified as no effort must be expended to combat cross user interference. We assume that there is always a packet for the transmitter to send whenever the channel is available. The transmitter is assumed to have a buffer of infinite length so that there is no packet-dropping. Three specific schemes are studied, including generalized slotted ALOHA (GSA), repetition time diversity (RTD), and general incremental redundancy (IR). The scaling laws of the average delay and average throughput with respect of the number of users are derived. In addition, we also derive a condition to obtain a linear scaling for both the throughput and the delay with respect to the number of users. Since every user can achieve no more than the ergodic capacity, we actually achieve the optimal scaling law in this case. Simulation results confirm our findings.

Dual-polarized multiple-input multiple-output (MI-MO) antenna systems, where the antennas are grouped in pairs of orthogonally polarized antennas, are a spatially-efficient alternative to single polarized MIMO antenna systems. A limited feedback beamforming technique is proposed for dual-polarized MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives partial information regarding the channel instantiation. The system employs an effective signal-to-noise ratio (SNR) distortion minimizing codebook to convey channel state information (CSI) in the form of beamforming direction. By investigating the average SNR performance of this system, an upper bound on the average SNR distortion is found as a weighted sum of two beamforming distortion metrics. The distortion minimization problem is solved by designing a concatenated codebook. Finally, we propose a codebook switching scheme exploiting the cross-polar discrimination (XPD) statistics. Simulations show that the proposed codebook switching scheme with an XPD dependent concatenated codebook has the ability to adapt to dual-polarized channels.

It is difficult for users in multiuser multiple-input multiple-output (MU-MIMO) systems to obtain co-channel interference (CCI) statistics without user cooperation. We propose a technique through which each user can effectively obtain the statistics of the interference that it experiences in a precoded MU-MIMO system. This allows true maximum-likelihood detection to be performed in place of minimum-distance detection. Also, we propose a low-complexity perturbation codebook decoder that attempts to mitigate the effects of both CCI and AWGN. The effectiveness of this decoder in reaching near-optimal performance is shown through Monte Carlo simulations.

Generally limited feedback systems that multiplex multiple user signals are noise power limited in the low signal-tonoise- ratio (SNR) regime and interference power limited in the high SNR regime. If the amount of feedback does not grow with SNR, then there is a sum rate ceiling that increasing the transmit power alone cannot surpass. This paper proposes a simple dual mode system where the base station serves either one user or as many users as the number of transmit antennas. The switching mechanism is smooth and is based on instantaneous system conditions. The mobile determines its preferred transmission mode by using a very simple signal-to-interference-and-noiseratio (SINR) threshold. Based on the mobiles' feedback, the base station chooses the method of signalling accordingly. With a finite number of feedback bits per mobile, it is shown that the proposed system achieves close to the maximum of the sum-rate for single user and multiuser modes. We identify the preferrable mode for an asymptotically large number of users and/or SNR. This proposed architecture is suitable for systems with moderate coherence time, a moderate to large number of users, and a moderate SNR that cannot afford complex processing at the base station or at the mobile.

Multiple-input multiple-output (MIMO) systems can be leveraged to increase capacity in fading channels. Especially in multiuser downlink communication systems, it has been shown that knowledge of channel state information at the transmitter (CSIT) is critical to leverage the capacity gain available from multiple antennas. When duplexing is performed using time division, CSIT can often be successfully obtained when channel reciprocity is available. CSIT acquisition, however, is much more difficult in frequency division duplexing. Sending feedback on the uplink has been shown to be a powerful technique to improve downlink performance in single user MIMO systems. The basic idea is to restrict the CSIT to a B bit codebook so that the mobiles can easily transmit these bits on the uplink. In this paper, we consider the multiuser downlink model with unitary precoding when there is a codebook consisting of 2Bunitary matrices that the precoder is restricted to lie in. This codebook is designed offline and known to both the base-station and all users. Each user sends back signal-to-interference plus noise ratio (SINR) information along with binary feedback about the unitary precoder. Based on the CSIT received on the uplink, the base-station selects one of the unitary matrices in the codebook to maximize the sum-rate. For this set-up, we first analyze the sum-rate performance of the unitary precoding scheme. We then show that the codebook of unitary pre-coders represents a collection of points in a special kind of manifold and show how the achievable sum-rate performance relates to the minimum distance of the codebook points in this space. Finally, we present a framework for constructing the codebook to maximize this minimum distance. Monte Carlo simulation results are presented to show the sum-rate performance of the proposed codebook design.

In a two-way system where two users transmit data to each other, limited feedback beamforming is a simple method to supply channel state information to the transmitter (CSIT) for a multiple-input-multiple-output (MIMO) system when channel reciprocity is unavailable. For analytical tractability, most existing papers assume the existence of an ideal fixed-rate feedback channel to assist the transmitter to adapt to instantaneous channel conditions. The relationship between the feedback rate and data rate is typically analyzed in a unidirectional manner. In reality, judicious resource allocation to transmitting feedback and data leads to a tradeoff between the effective forward and reverse data rates. In this paper, we represent the achievable rate by effective SNRs, and we present a framework to analyze the tradeoff. We find that the forward and reverse rate tradeoff can be decomposed into two local tradeoffs, resulting from the resource allocation policy of each user. The local tradeoff region for each user is found in closed-form, whereas the overall tradeoff region is approximated for the special case when the two users have equal hardware configurations.

Wireless cellular networks often need to convey the same data to multiple users simultaneously. This kind of transmission is known as physical layer multicasting. Unfortunately, when any user fails to receive the data correctly, the information must be retransmitted to all the users, resulting in a waste of radio resources. Thus, it is important to investigate the outage probability of multicast channels, which is defined as the probability that the smallest maximum achievable rate among all of the users is smaller than a specified transmission rate. In this paper, we consider the use of multiple antenna multicast channels where the transmitter is equipped with Mtantennas and independent data is transmitted on each antenna to K users. Using extreme value theory, we derive a closed form limiting distribution that the exact distribution of the multicasting channel converges to when the number of users K is taken to infinity. From this result, we find upper and lower-bounds on the outage probability. It is shown that for a given outage probability the upper-bound becomes sufficiently tight to approximate the exact outage probability with K such that it converges to the exact one with a speed faster than Theta (K-1/Mt). Using this upper-bound, we identify conditions on the transmission rate and the transmit power necessary to maintain constant outage performance as K increases. Specifically, the transmission rate should be decreased as Theta (K-1/Mt) or the transmit power should be increased as Theta (K1/Mt). This means that the outage performance improves as the number of transmit antennas increases, because spatial diversity can be well exploited. However, this increase in the number of transmit antennas requires a significant cost. To improve the performance without requiring additional cost, we consider a multiple-slot multicasting that transmits the same data over multiple slots and an antenna subset selection scheme that transmits the data over some subset of the transmit antennas. It is shown that the transmission rate of multiple-slot scheme can be increased when the number of slots is carefully chosen and the diversity-multiplexing trade-off is the same as that of K = 1. Also, the gain in the transmission rate from selecting some subset of the transmit antennas is derived.

Collocated dual-polarized multiple-input multiple-output (MIMO) antenna systems provide a cost-space efficient alternative to current MIMO antenna systems. A limited feedback beamforming technique is proposed for dual-polarized MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives quantized information regarding the channel instantiation. By decomposing the dual-polarized channel into a single polarized and a decoupled dual-polarized channel, the performance distortion metric can be expressed as a weighted sum of two beamforming distortion metrics. The distortion minimization problem is solved in a suboptimal way by locally minimizing each beamforming distortion metric. A concatenated codebook design approach is proposed. The capacity performance is compared with an interpolated codebook adapted to the cross- polar discrimination (XPD) through linear interpolation. From the simulation results, a concatenated codebook outperforms an interpolated codebook given the same number of feedbacks bits.

In fading channels, multiple-input multiple-output (MIMO) wireless systems make use of the spatial dimension of the channel to provide considerable capacity gain even when only partial channel state information (CSI) is available to the transmitter. A limited feedback linear preceding technique is proposed for temporally correlated MIMO channels where the receiver has perfect channel knowledge but the transmitter only receives quantized information regarding the channel instantiation. For this set-up, we first analyze capacity performance of a general rotation based limited feedback MIMO system in a Rayleigh flat fading channel. It can be shown that the minimum distance of the rotation codebook is related to the capacity performance of the system. Then, we present a framework for rotation based differential feedback by constructing the rotation codebook to adapt to the temporal correlation structure. Monte Carlo simulation results are presented to show the capacity performance of the proposed codebook design.

Recent research has demonstrated that by utilizing channel state information at the transmitter, the physical layer can be optimized to provide higher link capacity and throughput, more efficiently share the channel with multiple users, increase range by exploiting diversity due to spatial and frequency selectivity, and simplify multi-user receivers through known interference cancellation. Unfortunately, acquiring channel state information at the transmitter is difficult. In most systems, the only opportunity for the transmitter to learn about the channel is through a feedback control channel. Because feedback information is control overhead, the rate of the feedback channel is limited. This motivates the study of limited feedback techniques where only partial or quantized information from the receiver is conveyed back to the transmitter.

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

In this paper, we consider the downlink of a multiuser wireless communication system with multiple antennas at the base station and users each with a single receive antenna. It is known that when channel state information (CSI) is available at the transmitter a large performance gain can be achieved. In a system employing time-division duplexing (TDD), CSI can be obtained at the base station if there is reciprocity between the forward and reverse channels. CSI can also be conveyed from the users to the base station via a limited-rate feedback channel in a frequency-division duplexing (FDD) system. In any case, channel estimation errors are inevitable due to the presence of background noise in the estimated signal and due to the finite number of feedback bits used in a limited-rate feedback system model. In this paper, we first consider the general case when partial CSI is available at the transmitter. We derive an MMSE based precoding technique that considers channel estimation errors as an integral part of the system design. Using rate-distortion theory and the generalized Lloyd vector quantization algorithm, we then specialize our results for the more practical limited-rate feedback system model. Compared to previously proposed precoding techniques such as channel inversion and regularized channel inversion, it is shown that the proposed precoding technique significantly improves the average bit error rate (BER) in the system. Furthermore, the performance of the proposed technique is investigated in the high signal-tonoise ratio (SNR) regime. It is shown that the proposed technique suffers from a ceiling effect that asymptotically limits the system performance.

When a spread spectrum contention-based feedback channel is employed, it has been shown that most of the multiuser diversity gain can be maintained without any delay constraint. To address the delay in sending packets, we investigate how a spread spectrum contention-based feedback channel affects the delay and throughput performance. Using large deviations techniques, we show that most of the multiuser diversity gain can be maintained while satisfying a delay constraint in which the probability that the maximum delay of any bits in any users' queues is less than a specified value. In addition, we show that the spectral efficiency improves as the number of users increases while maintaining a fixed normalized maximum delay (which is defined as the ratio of the maximum delay and the number of users) as the number of users increases.

The theory of multiple-input-multiple-output (MIMO) technology has been well developed to increase fading channel capacity over single-input-single-output (SISO) systems. This capacity gain can often be leveraged by utilizing channel state information at the transmitter and the receiver. Users make use of this channel state information for transmit signal adaptation. In this correspondence, we derive the capacity region for the MIMO multiple access channel (MIMO MAC) when partial channel state information is available at the transmitters, where we assume a synchronous MIMO multiuser uplink. The partial channel state information feedback has a cardinality constraint and is fed back from the basestation to the users using a limited rate feedback channel. Using this feedback information, we propose a finite codebook design method to maximize the sum rate. In this correspondence, the codebook is a set of transmit signal covariance matrices. We also derive the capacity region and codebook design methods in the case that the covariance matrix is rank one (i.e., beam- forming). This is motivated by the fact that beamforming is optimal in certain conditions. The simulation results show that when the number of feedback bits increases, the capacity also increases. Even with a small number of feedback bits, the performance of the proposed system is close to an optimal solution with the full feedback.

Limited feedback systems that simultaneously transmit to multiple users are noise power limited at low SNR and interference power limited at high SNR. Specifically, there is a sum rate upper bound such that increasing the transmit power alone cannot surpass. In this paper, we propose a dual-mode system that selects between single user mode and multiuser mode. The mobile determines its preferred transmission mode by a simple threshold rule of its SINR. Judiciously combining mobiles' feedback, the base station with MTtransmit antennas chooses the better communication mode. It is shown that this proposed system asymptotically achieves unbounded capacity growth when increasing the number of mobiles or when increasing the SNR, without requiring extra feedback bits. We show the asymptotically preferable mode as the number of users and/or SNR increases. Lastly, we use computer simulations to verify our findings.

In downlink multiuser systems, multiple-input multiple-output (MIMO) technology can be leveraged to increase capacity in fading channels. Knowledge of channel state information at the transmitter (CSIT) is important to obtaining this capacity gain. It is unrealistic to obtain perfect CSIT, but partial CSIT can often be made available by using some practical feedback method. Based on partial CSIT, codebook techniques have been shown to improve performance of MIMO multiuser systems. Unfortunately, the codebook design problem has not been fully investigated. In this paper, we consider a multiuser multiple antenna downlink model with unitary precoding using a codebook consisting of 2B unitary matrices. The appropriate precoding matrix is chosen from this codebook, which is known to both the basestation and the receivers. By defining and using a carefully chosen distance function, the minimum distance of the codebook is shown to relate to the average sum-rate of the unitary precoded system. From our analysis, a codebook design criterion is developed based on maximizing the minimum distance of the codebook. Then, a framework for constructing the codebook is presented to maximize this minimum distance. Monte Carlo simulation results are given to show the average sum-rate capacity performance of the proposed codebook design.

In this paper, we present a user selection scheme with a fairness constraint when zero-forcing beamforming (ZFBF) transmission is employed in a multiple-input multiple-output (MIMO) broadcast channel. This problem can be reduced to the maximization of a weighted sum rate with a power constraint. In this paper, we first derive a lower bound on the weighted sum rate. Roughly speaking, this bound is inversely proportional to the Frobenius norm of the inverse of the weighted composite channel matrix. We then present a greedy algorithm to select the users that maximize this lower bound, using the same technique in J. Wang et al. Finally, we show that this approach achieves the same scaling law as DPC does as presented in M. Sharif and B. Hassibi. Simulation suggests that this method achieves comparable or better performance than most existing schemes in the moderate to high SNR realm with comparable complexity.

Multiuser diversity gain is an effective technique for improving the performance of wireless networks. This gain can be exploited by scheduling the users with the best current channel conditions. However, this kind of scheduling requires that the base station (or access point) knows some kind of channel quality indicator (CQI) information for every user in the system. When the wireless link lacks channel reciprocity, each user must feed back this CQI information to the base station. The required feedback load makes exploiting multiuser diversity extremely difficult when the number of users becomes large. To alleviate this problem, this paper considers a contention-based CQI feedback where only users whose channel gains are larger than a threshold are allowed to transmit their CQI information through a spread-spectrum based contention channel. Considering the capture effect in this contention channel, it is shown that i) the multiuser diversity gain can be exploited regardless of the number of transmit antennas at the base station and ii) the total system throughput exponentially approaches that of the full feedback scheme as the spreading code length of the contention channel linearly increases. In addition, it is also shown that multiuser diversity can be maintained with the feedback delay of time-variant channels. We also consider the issue of differentiated rate scheduling, in which the base station gives different rates to different subsets of mobiles. In this scenario, mobiles feed back their CQI with some access probability, and we show this technique causes only a negligible throughput loss compared to the case without supporting differentiated rate.

In multiple antenna wireless systems, beamforming is a simple technique for guarding against the negative effects of fading. Unfortunately, beamforming requires the transmitter to have knowledge of the forward-link channel which is often not available a priori. One way of overcoming this problem is to design the beamforming vector using a limited number of feedback bits sent from the receiver to the transmitter. In limited feedback beamforming, the beamforming vector is restricted to lie in a codebook that is known to both the transmitter and receiver. Random vector quantization (RVQ) is a simple approach to codebook design that generates the vectors independently from a uniform distribution on the complex unit sphere. This correspondence presents performance analysis results for RVQ limited feedback beamforming

We consider the downlink of a multiuser communication system with multiple antennas at the transmitter and at each receiver. A new transmission scheme that employs dirty-paper coding (DPC) with partial interference cancellation is proposed. It is shown that given any user ordering the achievable throughput is asymptotically optimal in the high signal-to-noise ratio (SNR) regime and for all values of the SNR it improves over the throughput of the zero-forcing dirty-paper coding (ZFDPC) scheme [1]. Due to its implementation complexity issues, we also consider a low complexity linear transmission scheme. A precoding technique where interference is canceled successively at the base station is proposed. In this case, users treat the partial interference at the receiver as an independent noise, and it is shown that compared to other linear transmission schemes such as the block-diagonalization (BD) and the zero-forcing schemes, the achievable throughput of the proposed precoding scheme is improved for all SNR values.

In this paper, we consider limited feedback for multiple antenna broadcast channels: each user quantizes its channel vector according to a rotated codebook which is optimal in the sense of mean square error and feeds back the codeword index. The paper is focused on characterizing the sum rate performance of zero-forcing dirty paper coding (ZFDPC) systems under limited feedback. We derive sum rate upper bound for systems employing Gaussian random codebooks and minimum distance decoding and link the throughput performance to some basic properties of the quantization codebook. Interestingly, we find that limited feedback employing a fixed codebook leads to a sum rate ceiling for asymptotically high SNR.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is a spectrally efficient modulation scheme which combines the advantages of having multiple antennas at both transmitter and receiver and the ease of equalization afforded by OFDM. When channel state information (CSI) is available at the transmitter a simple technique called linear preceding can be used to improve the error rate performance in both beamforming and spatial multiplexing systems. However, in frequency division duplex (FDD) systems there is typically a lack of channel reciprocity and precoder matrices have to be designed at the receiver and sent back through a limited feedback channel with the help of codebooks. By constraining the precoders to lie on the Grassmann manifold optimal codebook design for the single carrier MIMO channel has been well studied. If these codebooks are used in a MIMO-OFDM system and precoder information for all subcarriers is sent back, the amount of feedback can get prohibitively large. In this paper we use simple geometrical ideas on the Grassmann manifold to further reduce this feedback requirement. The performance of these algorithms are shown to provide improvement over existing schemes.

In this paper, we investigate the subcarrier allocation problem in multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems (also known as MIMO-OFDMA) with spatial multiplexing. Due to the numerous advantages of OFDM schemes, this type of system is becoming increasingly popular for deployment in next generation wireless systems. We first consider the problem of maximizing the capacity of the downlink channel, which results in a joint optimal subcarrier allocation and power loading problem. However, the computational complexity involved in this joint optimization problem is of order 0(KN), where K is the total number of users and N is the total number of subcarriers in the system. We next propose a new suboptimal scheme of low complexity and extend our framework by incorporating spatial multiplexing. Finally, we evaluate the performance in terms of bit error rate (BER) and capacity of the system via simulation and give some analytical bounds.

In this paper, we study the limited feedback model for partial CSI at the basestation (BS) for multiple antenna broadcast channels: the BS has the knowledge of quantized CSI of each user. We first give a practical limited feedback scheme designed for multiple antenna broadcast channels. Then, we study the sum rate performance of zero-forcing dirty paper coding under the proposed limited feedback scheme. An upper bound is also derived to get some insight about the impact of the use of limited feedback. Interestingly, we find that the systems experience a ceiling effect on the sum rate for a fixed feedback rate.

Multiple-input-multiple-output (MIMO) communication systems can provide large capacity gains over traditional single-input-single-output (SISO) systems and are expected to be a core technology of next generation wireless systems. Often, these capacity gains are achievable only with some form of adaptive transmission. In this paper, we study the capacity loss (defined as the rate loss in bits/s/Hz) of the MIMO wireless system when the covariance matrix of the transmitted signal vector is designed using a low rate feedback channel. For the MIMO channel, we find a bound on the ergodic capacity loss when random codebooks, generated from the uniform distribution on the complex unit sphere, are used to convey the second order statistics of the transmitted signal from the receiver to the transmitter. In this case, we find a closed-form expression for the ergodic capacity loss as a function of the number of bits fed back at each channel realization. These results show that the capacity loss decreases at least as O(2/sup -B/(2MMt-2)/) where B is the number of feedback bits, M/sub t/ is the number of transmit antennas, and M=min{M/sub r/,M/sub t/} where M/sub r/ is the number of receive antennas. In the high SNR regime, we present a new bound on the capacity loss that is tighter than the previously derived bound and show that the capacity loss decreases exponentially as a function of the number of feedback bits.

In this paper, a multiple-input multiple-output (MIMO) communication system employing a quantized feedback channel is considered. Because of their capacity gain benefits, MIMO communication systems are expected to be a core technology of next generation wireless systems. We study the capacity loss of such systems when the transmitted signal covariance matrix is designed using a low rate feedback channel. For the MIMO channel, we find a universal bound on the instantaneous capacity loss, and we derive a bound on the average capacity loss in the particular case when random codebooks, generated from the uniform distribution on the complex unit sphere, are used to design the transmitted signal covariance matrix. In this case, we find a closed-form expression for the capacity loss as a function of the number of feedback bits used at each channel realization, and it is that the capacity loss decreases exponentially as a function of the number of feedback bits

When a base-station with multiple antennas is transmitting to a single user with one antenna, feeding back the normalized channel vector (channel subsapce feedback) to the transmitter can achieve the same capacity as if the exact channel state information is available at the transmitter. For the multiuser scenario, however, perfect channel knowledge is usually required at the transmitter to obtain the sum rate advantage provided by the multiple transmit antennas. In this paper, we consider the sum rate of a multi-antenna broadcast channel when each user only feeds back the normalized channel vector. We show that in such situation the zero-forcing dirty paper (ZFDP) encoding scheme designed using channel subspace feedback achieves the throughput of full channel knowledge ZFDP encoding without optimal power allocation. It is shown to be asymptotically optimal for high signal-to-noise ratios. The closed-form expression of the ergodic sum rate for an independent Rayleigh fading channel is derived. The throughput performance of the regularized channel inversion technique under channel subspace feedback is also analyzed. We show that channel subspace feedback in the multi-antenna broadcast channel enables the system to achieve most of the sum rate advantage provided by the multiple antennas.

In this paper, a STBC/BF hybrid technique is proposed for outdoor multipath multi-input multi-output (MIMO) channel. Depending on the amount of partial channel state information (CSI), this system evolves from pure diversity waveform signalling to pure beamforming. The performance of this technique, in terms of signal vs noise ratio (SNR), is investigated in various multi-input single-output (MISO) scenarios. Though the method is evaluated in MISO, we give a simple way to extend it to MIMO case. In addition, a beamforming scheme based on sub-space approximation is also discussed which can reduce the amount of feedback from the receiver to transmitter. Simulation results are given at the end of this paper

We derive a hybrid of beam-forming (BF) and space-time block coding (STBC), where the space-time code is transmitted over the beams generated by the steering vectors corresponding to the channel path directions. This is for the practical case where the transmit array may have adequate information on the departure angles of the dominant paths between transmitter and receiver, but unreliable information on the associated complex path gains. We compute analytically the signal-to-noise ratio (SNR) of the proposed hybrid for the specific case of a two-path channel model and using the orthogonal Alamouti code, and compare the result to the SNR of optimal linear precoding (LP) and the theoretically possible SNR of orthogonal STBC (OSTBC). Simulation results show that the performance of the BF/STBC hybrid can be very close to LP /sup n/der certain conditions - or even better in the practical case where there are phase estimation errors in the path gain estimates employed at the transmitter.

Space-time transmit structures for multi-antenna systems have received considerable interest. Circulant structures were among the first space-time coding techniques ever used for multiple-input multiple-output (MIMO) systems due to their simplicity and full rate. The fact that a circulant matrix is diagonalized by the discrete Fourier transformation matrix suggests that the circulant structure can be combined with an inverse fast Fourier transform (IFFT) at the transmitter and a fast Fourier transform (FFT) at the receiver. Using this method, the spatial mixing effect of the MIMO channel is decoupled but the diversity gain is lost. To recover the diversity advantage, we propose to spread the transmitted symbols over the diagonalized channel using the constellation rotation matrix for signal diversity designs. After spreading, every symbol experiences all the components of the frequency counterpart of the channel vector which makes our scheme provide full diversity. The proposed scheme is full rate and can be easily applied to any number of transmit antennas. Our simulation results show that the performance of our scheme is close to the performance of the ideal orthogonal space-time code and much better than the conventional circulant space-time code.

Orthogonal space-time block codes (OSTBC) are among the most practical space-time codes due to their simplicity and optimal decoding. When information about the channel is available at the transmitter, the performance of OSTBC can be significantly improved by exploiting the array gain. However, providing full knowledge of channel state at the transmitter may not he affordable in many practical cases. Thus exploiting partial channel knowledge to improve the performance of OSTBC seems to be attractive. In this work we investigate the design of linear precoders with partial channel knowledge at the transmitter combined with an orthogonal space time code. We derive the condition for optimal precoder subject to a power constraint on the whole transmission block. We also propose a technique for precoder codebook design with limited feedback.

Orthogonal space-time block codes (OSTBCs) can obtain full diversity advantage with a simple, but optimal, receiver. Unfortunately, OSTBCs lack in array gain compared with beamforming techniques and suffer a rate loss for more than two transmit antennas. One simple method for improving the array gain and adapting OSTBCs to any number of transmit antennas is antenna-subset selection, where the OSTBC is transmitted on a subset of the transmit antennas. In this letter, we analyze the symbol-error rate performance of antenna-subset selection combined with OSTBCs.

Multiple-input multiple-output (MIMO) wireless systems use antenna arrays at both the transmitter and receiver to provide communication links with substantial diversity and capacity. Spatial multiplexing is a common space-time modulation technique for MIMO communication systems where independent information streams are sent over different transmit antennas. Unfortunately, spatial multiplexing is sensitive to ill-conditioning of the channel matrix. Precoding can improve the resilience of spatial multiplexing at the expense of full channel knowledge at the transmitter-which is often not realistic. This correspondence proposes a quantized precoding system where the optimal precoder is chosen from a finite codebook known to both receiver and transmitter. The index of the optimal precoder is conveyed from the receiver to the transmitter over a low-delay feedback link. Criteria are presented for selecting the optimal precoding matrix based on the error rate and mutual information for different receiver designs. Codebook design criteria are proposed for each selection criterion by minimizing a bound on the average distortion assuming a Rayleigh-fading matrix channel. The design criteria are shown to be equivalent to packing subspaces in the Grassmann manifold using the projection two-norm and Fubini-Study distances. Simulation results show that the proposed system outperforms antenna subset selection and performs close to optimal unitary precoding with a minimal amount of feedback.

Transmit beamforming and receive combining are low complexity, linear techniques that make use of the spatial diversity advantage provided by transmitters and/or receivers employing multiple antennas. There has been a growing interest in designing beamforming schemes for frequency division duplexing systems that use a limited amount of feedback from the receiver to the transmitter. This limited feedback conveys a beamforming vector chosen from a finite set known to both the transmitter and receiver. These techniques often use a set of beamforming vectors where the probability of error expression can not be easily formulated or bounded. It is of utmost importance to guarantee that the sets of beamforming and combining vectors are chosen such that full diversity order is achieved. For this reason, necessary and sufficient conditions on the sets of possible beamformers and combiners are derived that guarantee full diversity order in correlated Rayleigh fading.

Multiple-antenna wireless systems are of interest because they provide increased capacity over single-antenna systems. Several space-time signaling schemes have been proposed to make use of this increased capacity. Space-time techniques, such as space-time block coding and spatial multiplexing, can all be viewed as signaling with a multidimensional constellation. Because of the large capacity of multiple-input multiple-output (MIMO) channels, these multidimensional constellations often have large cardinalities. For this reason, it is impractical to perform optimal maximum-likelihood (ML) decoding for space-time systems, even for a moderate number of transmit antennas. In this paper, we propose a modified version of the classic Chase decoder for multiple-antenna systems. The decoder applies successive detection to yield an initial estimate of the transmitted bit sequence, constructs a list of candidate symbol vectors using this initial estimate, and then computes bit likelihood information over this list. Three algorithms are presented for constructing the candidate vector list. This decoder can be adjusted to have a fixed or variable complexity, while maintaining performance close to that of an ML decoder.

The diversity gains available from multiple-input multiple-output (MIMO) wireless systems are well documented. These gains are realizable through the use of transmit beamforming and receive combining. Transmit beamforming relies on the assumption of channel knowledge at the transmitter, an assumption that is often unrealistic. Limited feedback beamformers have been designed over the past few years, but they concentrate primarily on the assumption of a spatially uncorrelated Rayleigh fading channel matrix. The paper addresses the design of limited feedback beamformers for transmit and receive correlated MIMO channels. In particular, we use a technique where the receiver chooses the beamforming vector from a codebook of possible vectors and conveys this vector over a limited feedback channel. We show how this method obtains full diversity order. Monte Carlo simulations show performance close to optimal beamforming.

Orthogonal space-time block codes (OSTBCs) are an efficient solution for obtaining full diversity order in Rayleigh fading channels. Unfortunately, OSTBCs exist for only certain numbers of transmit antennas and do not provide array gain as diversity techniques that exploit transmit channel information do. When channel state information is available at the transmitter, linearly precoded space-time codes can he used to support different numbers of transmit antennas and to improve array gain. Precoding generally requires complete channel knowledge, thus motivating limited feedback methods such as channel quantization or antenna subset selection. The paper investigates limited feedback precoding that uses a codebook of matrices known a priori to both the transmitter and receiver. Using the codebook, the receiver chooses a matrix based on current channel conditions and conveys the optimal codebook matrix to the transmitter over an error-free, zero-delay feedback channel A precoder matrix selection criterion is proposed that relates directly to minimizing the probability of symbol error of the precoded system. Low average distortion codebooks based on Grassmannian subspace packing are derived for the optimal codeword selection criterion. It is shown that codebooks designed by this method provide full diversity order in Rayleigh fading channels.

Multiple-input multiple-output (MIMO) wireless systems obtain large diversity and capacity gains by employing multi-element antenna arrays at both transmitter and receiver. The theoretical performance benefits, however, are irrelevant unless low error rate, high spectral efficiency spatio-temporal signaling techniques are found. Most work in space-time coding concentrates on either designing low error rate codes or high data-rate codes but not both simultaneously. This paper proposes a new method for designing high data-rate spatio-temporal signals with low error rates. The basic idea is to use transmitter channel information in the form of limited feedback to adaptively vary the transmission scheme for a fixed data-rate. This adaptation is done by varying the number of substreams and the rate of each substream in a precoded spatial multiplexing system. We show how this method can be implemented in a limited feedback scenario where only finite sets, or codebooks, of possible precoding configurations are known to both the transmitter and receiver. Monte Carlo simulations show substantial performance gains over beamforming and spatial multiplexing.

Spatial multiplexing multiple-input multiple-output (MIMO) wireless systems are of both theoretical and practical importance because they can achieve high spectral efficiencies by demultiplexing the incoming bit stream into multiple substreams. It has been shown that sending fewer substreams than the number of transmit antennas by linear precoding can provide improved error rate performance. Methods for designing linear precoders using perfect channel knowledge have previously been proposed. In many wireless systems, the assumption of complete channel knowledge is unrealistic because of the lack of forward and reverse channel reciprocity. To overcome this difficulty, we propose a precoding scheme that does not require transmit channel knowledge. The precoder is designed at the receiver and conveyed to the transmitter using a limited number of bits. The limited feedback represents an index within a finite set, or codebook, of precoding matrices. The receiver selects one of these codebook matrices using a modified version of a previously proposed full channel knowledge precoder selection criterion. A precoder codebook design method for maximizing the average effective channel power is shown to relate to chordal distance Grassmannian subspace packing. Simulation results show this technique outperforms antenna subset selection spatial multiplexing.

Multiple-input multiple-output (MIMO) wireless systems provides capacity much larger than that provided by traditional single-input single-output (SISO) wireless systems. Beamforming is a low complexity technique that increases the receive signal-to-noise ratio (SNR), however, it requires channel knowledge. Since in practice channel knowledge at the transmitter is difficult to realize, we propose a technique where the receiver designs the beamforming vector and sends it to the transmitter by transmitting a label in a finite set, or codebook, of beamforming vectors. A codebook design method for quantized versions of maximum ratio transmission, equal gain transmission, and generalized selection diversity with maximum ratio combining at the receiver is presented. The codebook design criterion exploits the quantization problem's relationship with Grassmannian line packing. Systems using the beamforming codebooks are shown to have a diversity order of the product of the number of transmit and the number of receive antennas. Monte Carlo simulations compare the performance of systems using this new codebook method with the performance of systems using previously proposed quantized and unquantized systems.

Multiple-input multiple-output (MIMO) wireless systems are of interest due to their ability to provide substantial gains in capacity and quality. The paper proposes equal gain transmission (EGT) to provide diversity advantage in MIMO systems experiencing Rayleigh fading. The applications of EGT with selection diversity combining, equal gain combining, and maximum ratio combining are addressed. It is proven that systems using EGT with any of these combining schemes achieve full diversity order when transmitting over a memoryless, flat-fading Rayleigh matrix channel with independent entries. Since, in practice, full channel knowledge at the transmitter is difficult to realize, a quantized version of EGT is proposed. An algorithm to construct a beamforming vector codebook that guarantees full diversity order is presented. Monte-Carlo simulation comparisons with various beamforming and combining systems illustrate the performance as a function of quantization.

Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector; both are hard to realize. In this article, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using the distribution of the optimal beamforming vector in independent and identically distributed Rayleigh fading matrix channels, the codebook design problem is solved and related to the problem of Grassmannian line packing. The proposed design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the codebook size are derived to guarantee full diversity order. Results on the density of Grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or SNR loss. Monte Carlo simulations are presented that compare the probability of error for different quantization strategies.

Wireless systems with multiple-transmit and multiple-receive antennas (MIMO systems) are of interest due to their ability to provide substantial gains in capacity and quality. In this paper we propose equal gain transmission (EGT) as a technique to provide diversity advantage in MIMO systems. Using bounds on the error rate we show that EGT obtains a diversity gain on the order of the product of the number of transmit and receive antennas. Since in practice full channel knowledge at the transmitter is difficult to realize, we propose a technique using quantized phase information at the transmitter that achieves a full diversity advantage given enough bits of feedback. Monte Carlo simulation comparisons with other systems show the performance as a function of quantization.