Current applications of self-supervised learning to wireless channel representation often borrow paradigms developed for text and image processing, without fully addressing the unique characteristics and constraints of wireless communications. To bridge this gap, we introduce ContraWiMAE, Wireless Contrastive Masked Autoencoder, a transformer-based foundation model that unifies masked reconstruction and masked contrastive learning for wireless channel representation. Our key innovation is a new wireless-inspired contrastive objective that exploits the inherent characteristics of wireless environment, including noise, fading, and partial observability, as natural augmentation. Through extensive evaluation on unseen scenarios and conditions, we demonstrate our method’s effectiveness in multiple downstream tasks, including cross-frequency beam selection, line-of-sight detection, and channel estimation. ContraWiMAE exhibits superior linear separability and adaptability in diverse wireless environments, demonstrating exceptional data efficiency and competitive performance compared with supervised baselines under challenging conditions. Comparative evaluations against a state-of-the-art wireless channel foundation model confirm the superior performance and data efficiency of our approach, highlighting its potential as a powerful baseline for future research in self-supervised wireless channel representation learning. To foster further work in this direction, we release the model weights and training pipeline for ContraWiMAE.

Sixth-generation (6G) networks are rapidly becoming a focal point of global technological innovation, driven by the need to support hyper-reliable, low-latency, and intelligent connectivity for applications such as immersive extended reality, autonomous systems, and ubiquitous sensing. While 6G promises transformative advancements in wireless communication, achieving its ambitious goals poses significant fundamental challenges. One natural direction is to scale multiple-input multiple-output (MIMO) technology to unprecedented levels; however, doing so introduces substantial hardware complexity and power consumption. To overcome these limitations, recent research has explored antenna reconfigurability as a novel degree of freedom (DoF) at the physical (PHY) layer. Among these efforts, the fluid antenna system (FAS) has emerged as a compelling concept, offering reconfigurability in both spatial positioning and physical structure. This idea has inspired related innovations, including movable antennas, flexible-position MIMO, reconfigurable MIMO architectures, and adaptive antenna arrays, collectively referred to as next-generation reconfigurable antenna (NGRA) systems. While prior work has primarily focused on spatial flexibility, this article introduces a generalized model of FAS that incorporates both structural and morphological fluidity, enabling the vision of “shapeless and formless” antennas in future wireless systems. We analyze FAS’s potential to enhance key performance metrics such as coverage, energy efficiency, reliability, and spectral capacity. In addition, we outline implementation challenges and explore synergies with key 6G enablers, including reconfigurable intelligent surfaces (RIS), non-terrestrial networks (NTN), integrated sensing and communication (ISAC), and artificial intelligence (AI). This survey provides a comprehensive overview of NGRA systems and identifies promising directions for future research in reconfigurable wireless technologies.

Deep learning models for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems often suffer from performance degradation under fast-fading channels and low-SNR scenarios. To address these limitations, we introduce the Adaptive Fortified Transformer (AdaFortiTran), a novel model specifically designed to enhance channel estimation in challenging environments. Our approach employs convolutional layers that exploit locality bias to capture strong correlations between neighboring channel elements, combined with a transformer encoder that applies the global Attention mechanism to channel patches. This approach effectively models both long-range dependencies and spectro-temporal interactions within single OFDM frames. We further augment the model's adaptability by integrating nonlinear representations of available channel statistics SNR, delay spread, and Doppler shift as priors. A residual connection is employed to merge global features from the transformer with local features from early convolutional processing, followed by final convolutional layers to refine the hierarchical channel representation. Despite its compact architecture, AdaFortiTran achieves up to 6 dB reduction in mean squared error (MSE) compared to state-of-the-art models. Tested across a wide range of Doppler shifts (200-1000 Hz), SNRs (0 to 25 dB), and delay spreads (50-300 ns), it demonstrates superior robustness in high-mobility environments.

We introduce a novel mathematical framework for optimizing cellular network deployments, providing robust coverage and capacity for heterogeneous 3D user distributions. We establish necessary conditions and propose an iterative algorithm to fine-tune critical base station (BS) parameters, including location, horizontal bearing, vertical antenna tilt, and transmit power. In our case study, we optimize both existing and newly deployed BSs to support ground users and uncrewed aerial vehicles (UAVs) along designated corridors. Results indicate that the framework significantly enhances UAV connectivity while preserving near-optimal performance for ground users.

In a reconfigurable intelligent surface (RIS)-assisted communication system, optimizing the transmit beamforming of the active antenna array and the reflect beamforming of the passive phase shifters at the RIS require knowledge of the channel. Accurately estimating channels in such systems is a challenging task. We propose an algorithm that eliminates the need for channel estimation in these systems. The goal is to maximize the achievable rate at a single-antenna receiver in a system with a multiple-antenna transmitter. The problem is nonconvex, but closed-form solutions in systems with full channel state information can be found by using alternating optimization. We provide a low-complexity algorithm for active beamforming at the transmitter using rounds of single-bit feedback and passive beamforming at the RIS using tree-structured vector quantizers. Our results show that these algorithms asymptotically achieve the performance of a system with full channel state information available at both the transmitter and the RIS controller.

This work considers a hybrid laser-battery powered uncrewed aerial vehicle (UAV) data collection system serving a passive Internet of Things deployment via monostatic backscatter communications. In this paper, we highlight the merits of the hybrid scheme over the laser only or battery only powered devices UAVs in terms of improved reach and durability. In addition, we study the laser energy consumption - destination battery level retention tradeoff optimization problem. Throughout this process, we optimize the single-rotor UAV’s three-dimensional trajectory, the UAV’s and the laser’s radiated power profiles, and the temporal battery usage profile while adopting path discretization. The resulting non-convex problem is solved via single-block successive convex approximation, for which novel bounds for the UAV propulsion energy, harvested energy, and collected data assuming a probabilistic line-of-sight channel model are derived. Finally, the simulation results show significant data collection gains, battery energy savings, and laser energy consumption reductions compared with a baseline scheme and highlight the complexity-optimality tradeoff.

Recent advances in federated learning have shown that asynchronous variants can be faster and more scalable than their synchronous counterparts. However, their design does not include quantization, which is necessary in practice to deal with the communication bottleneck. To bridge this gap, we develop a novel algorithm, Quantized Asynchronous Federated Learning (QAFeL), which introduces a hidden-state quantization scheme to avoid the error propagation caused by direct quantization. QAFeL also includes a buffer to aggregate client updates, ensuring scalability and compatibility with techniques such as secure aggregation. Furthermore, we prove that QAFeL achieves an${\mathcal {O}}\left ({{1/\sqrt {T}}}\right)$ergodic convergence rate for stochastic gradient descent on non-convex objectives, which is the optimal order of complexity, without requiring bounded gradients or uniform client arrivals. We also prove that the cross-term error between staleness and quantization only affects the higher-order error terms. We validate our theoretical findings on standard benchmarks.

Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in 6G networks due to their versatility and adaptability. One potential application for UAVs is wildfire coverage, as they can carry various sensors, including cameras and antennas. This study focuses on the multi-UAV trajectory optimization for wildfire coverage while satisfying multiple constraints, including the UAV dynamics, network connectivity, and limited energy batteries. The resulting complex optimization problem is time-varying and non-convex. To address this challenge, reinforcement learning, specifically the twin-delayed deep deterministic policy gradient algorithm, is adopted. A distributed learning procedure is devised to allow parallelization and significant reduction of the training time. The result is high coverage at standard flying altitudes with finite energy batteries.

Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs) can be instrumental for wildfire prediction, tracking, and monitoring, provided that uninterrupted connectivity can be guaranteed even if some of the ground access points (APs) are damaged by the fire itself. A cell-free network structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in terms of resilience. This work proposes a trajectory optimization framework for a UAV swarm tracking a wildfire while maintaining cell-free connectivity with ground APs. Such optimization entails a constant repositioning of the multiplicity of UAVs as the fire evolves to ensure that the best possible view is acquired and transmitted reliably, while respecting altitude limits, avoiding collisions, and proceeding to recharge batteries as needed. Given the complexity and time-varying nature of this multi-UAV trajectory optimization, reinforcement learning is leveraged, specifically the twin-delayed deep deterministic policy gradient algorithm. The approach is shown to be highly effective for wildfire tracking and coverage and could be likewise applicable to survey other natural and man-made phenomena, including weather events, earthquakes, or chemical spills.

We consider the problem of decentralized optimization in networks with communication delays. To accommodate delays, we need decentralized optimization algorithms that work on directed graphs. Existing approaches require nodes to know their out-degree to achieve convergence. We propose a novel gossip-based algorithm that circumvents this requirement, allowing decentralized optimization in networks with communication delays. We prove that our algorithm converges on non-convex objectives, with the same main complexity order term as centralized Stochastic Gradient Descent (SGD), and show that the graph topology and the delays only affect the higher order terms. We provide numerical simulations that illustrate our theoretical results.

The expansion of mobile sensors, like robots and uncrewed aerial vehicles (UAVs), across diverse applications such as remote sensing, monitoring, and communication relay, has been exponential. Yet, ensuring their safe and successful operation depends crucially on optimized deployment tailored to the application requirements while constrained by various limitations. This study focuses on the optimization of robot/UAV trajectories under these constraints. However, implementing constraints poses considerable challenges. To this end, a framework for constrained deployment optimization of wireless robotic swarms is proposed. This framework formulates as a quadratic-programming problem which utilizes Bézier curves to model trajectories and predict their states over a time horizon. Constraints are systematically categorized and embedded in the Bézier curve formulation. This framework offers ease of adoption to various scenarios and flexibility in accommodating different mobile sensor dynamics, constraints, and deployment strategies.

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

The next-generation multiple access (NGMA) serves as an umbrella term encompassing transmission schemes distinct from conventional orthogonal methods. As a prominent candidate of NGMA, nonorthogonal multiple access (NOMA) emerges as a promising solution, enhancing connectivity by allowing multiple users to concurrently share time, frequency, and space. However, NOMA faces challenges in practical implementation, particularly in canceling interuser interference (IUI). In this article, first, we discuss the principles behind NOMA and review the conventional NOMA methods and results. Then, to address the above challenges, we present asynchronous transmission and interference-aware modulation techniques, leading to decoding free from successive interference cancellation (SIC). The goal is to design constellations that dynamically adapt to interference, minimizing bit error rates (BERs) and enhancing user throughput in the presence of IUI, intercarrier interference, and intercell interference (ICI). The traditional linkage between minimizing BER and increasing spectral efficiency is addressed, with the exploration of deep autoencoders (AEs) for end-to-end (E2E) communication as a new concept with significant potential for improving BERs. Interference-aware modulation techniques can revolutionize constellation design and communication over nonorthogonal channels. rate-splitting multiple access (RSMA) is another promising interference management technique in multiuser systems. Beyond addressing existing challenges and misconceptions in finite-alphabet NOMA, this article offers fresh insights into the field and provides an overview of code-domain NOMA (C-NOMA) schemes, trellis-coded NOMA (TC-NOMA), and RSMA as other potential candidates for NGMA. Additionally, we discuss the evolution of channel coding toward low-latency communication and examine the modulation and coding schemes (MCSs) in fifth-generation (5G) cellular networks. Finally, we examine future research avenues and challenges, highlighting the importance of addressing them for the practical realization of NOMA from a theoretical concept to a functional technology.

We study a heterogeneous Rayleigh fading wireless sensor network (WSN) in which sensor nodes surveil a field of interest and communicate their sensory data with base stations with the aid of access points as relays. With the goal of improving the energy efficiency of the network, we consider both large-scale and small-scale signal propagation effects in our system model and aim to optimize the node deployment as an effective measure to reduce the wireless communication power consumption of the WSN. We propose a new framework, in which hard deterministic connectivity constraints on communication links are replaced with realistic limitations on outage due to severe stochastic fading. We also consider a radio energy model that reflects the exponential dependence of the transmission power on the rate. We derive the necessary conditions for the optimal deployment that not only minimize the power consumption, but also guarantee all wireless links to have an outage probability below the given threshold. Our theoretical findings are accompanied by simulations that indicate significant performance gains compared to existing node deployment algorithms in the literature.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s (i) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of (i) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a number of new opportunities to further improve wireless networks. However, cell-free UAV networks present major challenges related to the wireless nature of access and fronthaul links. This manuscript studies the uplink of cell-free systems where users connect to UAVs, the latter devices forwarding the information to a processing point through imperfect wireless fronthaul links. Three multiple access alternatives are considered for the fronthaul, namely frequency division multiples access, spatial division multiple access, and combinations thereof. Deterministic equivalent expressions for the spectral efficiency under these fronthaul schemes and minimum mean-square error reception are derived. Then, the optimization subproblems of (a) the 3D deployment of the UAVs, (b) the user transmit powers, and (c) the UAV transmit powers, are investigated. The joint optimization of these subproblems yields superior performance, with the 3D deployment being the main source of improvement.

We present a new framework based on quantization theory to design cellular networks optimized for both legacy ground users and uncrewed aerial vehicle (UAV) corridors, dedicated aerial highways for safe UAV flights. Our framework leverages antenna tilts and transmit power at each base station to enhance coverage and quality of service among users. We develop a comprehensive mathematical analysis and optimization algorithms for multiple system-level performance metrics, including received signal strength and signal-to-interference-plus-noise ratio. Realistic antenna radiation patterns and propagation channel models are considered, alongside a generic 3D user distribution that allows for performance prioritization on the ground, along UAV corridors, or a desired tradeoff between the two. We demonstrate the efficacy of the proposed framework through case studies, showcasing the non-trivial combinations of antenna tilts and power levels that improve coverage and signal quality along UAV corridors while incurring only a marginal impact on the ground user performance compared to scenarios without UAVs.

The design of feedback channels in frequency division duplex (FDD) systems is a major challenge because of the limited available feedback bits. We consider non-orthogonal multiple access (NOMA) systems that incorporate reconfigurable intelligent surfaces (RISs). In limited feedback RIS-aided NOMA systems, the RIS-aided channel and the direct channel gains should be quantized and fed back to the transmitter. This paper investigates the rate loss of the overall RIS-aided NOMA systems suffering from quantization errors. We first consider random vector quantization for the overall RIS-aided channel and identical uniform quantizers for the direct channel gains. We then obtain an upper bound for the rate loss, due to the quantization error, as a function of the number of feedback bits and the size of RIS. Our numerical results indicate the sum rate performance of the limited feedback system approaches that of the system with full CSI as the number of feedback bits increases.

In this article, we introduce a new mathematical framework for the analysis and design of UAV corridors in cellular networks, while considering a realistic network deployment, antenna radiation pattern, and propagation channel model. By leveraging quantization theory, we optimize the electrical tilts of existing ground cellular base stations to maximize the coverage of both legacy ground users and UAVs flying along specified aerial routes. Our practical case study shows that the optimized network results in a cell partitioning that significantly differs from the usual hexagonal pattern, and that it can successfully guarantee coverage all over the UAV corridors without degrading the perceived signal strength on the ground.

This paper presents flexible storage codes, a class of error-correcting codes that can recover information from a flexible number of storage nodes. As a result, one can make better use of the available storage nodes in the presence of unpredictable node failures and reduce the data access latency. Assume a storage system encodes$k\ell $information symbols over a finite field$\mathbb {F}$into$n$nodes, each of size$\ell $symbols. The code is parameterized by a set of tuples$\{(R_{j},\ell _{j}): 1 \le j \le a\}$, satisfying$\ell _{1} < \ell _{2} < {\dots } < \ell _{a} = \ell $and$R_{1}>R_{2}> {\dots }>R_{a}$, such that the information symbols can be reconstructed from any$R_{j}$nodes, each node accessing$\ell _{j}$symbols, for any$1 \le j \le a$. In other words, the code allows a flexible number of nodes for decoding to accommodate the variance in the data access time of the nodes. Code constructions are presented for different storage scenarios, including LRC (locally recoverable) codes, PMDS (partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze the latency of accessing information and perform simulations on Amazon clusters to show the efficiency of the presented codes.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

The distributed matrix multiplication problem with an unknown number of stragglers is considered, where the goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across$N$servers. There are up to$N - R$stragglers but the exact number is not known a priori. Motivated by reducing the computation load of each server, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing task for each server is separated into several subtasks, constructed based on Entangled Polynomial codes by Yu et al. The final results can be obtained from either a larger number of servers with a smaller amount of computation completed per server or a smaller number of servers with a larger amount of computation completed per server. The required finite field size of the proposed solution is less than$2N$. Moreover, the optimal design parameters such as the partitioning of the input matrices are discussed. Our constructions can also be generalized to other settings such as batch distributed matrix multiplication and secure distributed matrix multiplication.

We study a heterogeneous wireless sensor network (WSN) where$N$heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to$M$heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

Cell-free (CF) structures are expected to be a game changer for beyond-5G wireless networks. With every user potentially communicating with every base station, cooperation at a central processing point is poised to provide much higher spectral efficiencies. At the same time, the growing interest in unmanned aerial vehicles (UAVs) makes CF-UAV networks an appealing scenario. This paper investigates the uplink of a CF network where UAVs serve as flying base stations. The optimization of the UAV locations is shown to markedly increase the minimum local-average signal-to-interference-plus-noise ratio, which in turn increases the spectral efficiency. The improvements are associated to pilot contamination and to geometry.

In this paper, we consider a RIS-assisted mmWave UAV wireless cellular network, where a UAV is serving several users with the help of multiple RIS. We jointly optimize the deployment, user scheduling, beamforming vector and RIS phases to maximize the sum-rate, with the constraints of the minimum rate, the UAV movement, the analog beamforming and the RIS phases. To solve this complex problem, we use an iterative method, in which when we optimize one variable, we fix the other three variables. When optimizing the deployment, we find the optimal position for the UAV by a sphere search. Then, we formulate an integer linear programming to find the best scheduling. We also design the analog beamforming vector by compensating the phases of the channel which combines the direct path and the RIS paths. When optimizing the RIS phases, we formulate a semi-definite programming to find the best phases. The proposed joint optimization outperforms the system without RIS assistance and the system without deployment optimization.

The distributed matrix multiplication problem with unknown number of stragglers is considered, where the goal is to allow a master to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across$N$servers. We assume there are at most$N-R$stragglers but the exact number is not known a priori. Motivated by reducing the latency, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing job for each server is separated into 2 layers, constructed based on Entangled Polynomial (EP) codes by Yu el al. The final results can be obtained when a larger number of servers complete the task from the first layer or a smaller number of servers complete the tasks from both 2 layers. The required finite field size of the proposed solution is less than$2N$. Moreover, the optimal partitioning of the input matrices is discussed. Our constructions can also be generalized to batch matrix multiplication.

In this paper, we study the joint user and power scheduling for downlink NOMA over fading channels. Specifically, we focus on a stochastic optimization problem to maximize the weighted average sum rate while ensuring given minimum average data rates of users. To address this problem, we first develop an opportunistic user and power scheduling algorithm (OUPS) based on the duality and stochastic optimization theories. By OUPS, the stochastic problem is transformed into a series of deterministic ones for the instantaneous weighted sum rate maximization for each slot. Thus, we additionally develop a heuristic algorithm with very low computational complexity, called user selection and power allocation algorithm (USPA), for the instantaneous weighted sum rate maximization problem. Via simulation results, we demonstrate that USPA provides near-optimal performance with very low computational complexity, and OUPS well guarantees given minimum average data rates.

In this work, Generalized Space-Time Super-Modulation (GSTSM) is introduced which enables the transmission of an additional flexible-rate and highly-reliable information stream concurrently with the conventionally transmitted symbols, without the need for increasing the corresponding packet length. This is attained by jointly exploiting the spatial and temporal dimensions of multiple-antenna systems, which enables efficient detection for conventional and additional information subchannels even in highly correlated channel conditions or AWGN channels. In the context of machine-type communications, GSTSM enables grant-free medium access without transmitting additional headers to convey each machine's signature information. Hence, it is shown that even at an extreme case where the data packets of two users are always colliding, GSTSM offers throughput gains of up to 33% compared to the best examined header-based scheme. For the same scenario, it is shown that GSTSM based on joint multi-user detection provides throughput gains of up to 2.5× compared with the case where users' signals are detected independently. In addition, it yields over 90% improvement in achievable rates compared with the schemes that require centralized medium-access coordination. For both joint and independent signal detection schemes, it is also shown that adopting an iterative detection/decoding approach allows to further improve the throughput gains.

The use of directional transmission in millimeter-Wave (mmWave) frequencies results in limited channel coherence time. In this paper, we take the limited channel coherence time into account for non-orthogonal multiple access (NOMA) in mmWave hybrid beamforming systems. Due to the limited coherence time, the beamwidth of the hybrid beamformer affects the beam-training time, which in turn directly impacts the data transmission rate. To investigate this trade-off, we utilize a combined beam-training algorithm. Then, we formulate a sum-rate expression which considers the channel coherence time and beam-training time as well as users' power and other system parameters. Further, a joint power and beamwidth optimization problem is solved by iterating between the power allocation and the beamwidth optimization. When allocating the power, we use the log-exponential reformulation and the sequential parametric convex approximation (SPCA) methods to solve the non-convex problem. Since beamwidth optimization involves too many variables, we propose an algorithm which iterates between clusters of users. Numerical results show that the optimized mmWave hybrid beamforming-NOMA system can achieve much higher sum-rates compared to NOMA with analog beamforming and traditional multiple access techniques.

In this work, we thoroughly analyze the rate-region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users' channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate. Furthermore, we provide practical transceiver designs to approach the capacity-region. The memory induced by asynchronous transmission enables the use of the trellis-based detection methods which improves the performance. In addition, we propose a transceiver design, based on channel diagonalization to exploit the frequency-selectivity introduced by timing offsets. The proposed transceiver design, joint with the turbo principle, enables us to achieve a rate pair that is not achievable by the synchronous transmission.

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.

Unmanned Aerial Vehicle (UAV) is a promising technology to solve many new challenging problems. It provides high maneuverability and control, low manufacturing cost with respect to other flying technologies and many other features. In particular, there is an increasing interest in UAVs in the field of wireless communications, due to their capacity to carry on transceivers and establish communication among UAVs, or between UAVs and ground base stations/users. In this work, we investigate a UAV deployment in which each flying vehicle serves a set of users, carrying directional antennas. To do so, we maximize the minimum downlink rate among the users that a UAV serves. Due to the non-convexity of the problem, we will divide it into four sub-problems. Afterwards, an iterative algorithm is proposed to optimize the four sub-problems by using the block coordinate descent method, successive convex approximation and sequential quadratic programming. Simulation results show that the addition of directional antennas results in a better performance in terms of throughput compared with omni-directional benchmarks.

We study a wireless ad-hoc sensor network (WASN) where N sensors gather data from the surrounding environment and transmit their sensed information to M fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs that minimizes a Lagrangian combination of sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd-like algorithm to optimize node deployment. Simulation results show that our proposed algorithm outperforms the existing deployment algorithms, on average.

We propose a novel analytical framework for evaluating the coverage performance of a millimeter wave (mmWave) cellular network where idle user equipments (UEs) act as relays. In this network, the base station (BS) adopts either the direct mode to transmit to the destination UE, or the relay mode if the direct mode fails, where the BS transmits to the relay UE and then the relay UE transmits to the destination UE. To address the drastic rotational movements of destination UEs in practice, we propose to adopt selection combining at destination UEs. New expression is derived for the signal-to-interference-plus noise ratio (SINR) coverage probability of the network. Using numerical results, we first demonstrate the accuracy of our new expression. Then we show that ignoring spatial correlation, which has been commonly adopted in the literature, leads to severe overestimation of the SINR coverage probability. Furthermore, we show that introducing relays into a mmWave cellular network vastly improves the coverage performance. In addition, we show that the optimal BS density maximizing the SINR coverage probability can be determined by using our analysis.

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of N UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment, by simulations. We also derive in closed-form the asymptotic optimal common flight height of N UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent.

In this paper, we propose a generalized millimeter-Wave (mmWave) reconfigurable antenna multiple-input multiple-output (RA-MIMO) architecture that takes advantage of lens antennas. The considered antennas can generate multiple independent beams simultaneously using a single RF chain. This property, together with RA-MIMO, is used to combat small-scale fading and shadowing in mmWave bands. To this end, first, we derive a channel matrix for RA-MIMO. Then, we use rate-one space-time block codes (STBCs), together with phase-shifters at the receive reconfigurable antennas, to suppress the effect of small-scale fading. We consider two kinds of phase shifters: i) ideal which is error-free and ii) digital which adds quantization error. The goal of phase-shifters is to convert a complex-valued channel matrix into real-valued. Hence, it is possible to use rate-one STBCs for any dimension of RA-MIMO. We investigate diversity gain and derive an upper bound for symbol error rate in cases of ideal and digital phase-shifters. We show that RA-MIMO achieves the full-diversity gain with ideal phase-shifters and the full-diversity gain for digital phase-shifters when the number of quantization bits is higher than one. We investigate RA-MIMO in the presence of shadowing. Our analysis demonstrates that, by increasing the dimension of RA-MIMO, the outage probability decreases which means the effect of shadowing decreases. Numerical results verify our theoretical derivations.

In this paper, we analyze the benefits of intentionally adding timing mismatch in the downlink transmit beamforming for wireless transmission. Transmit beamforming enables the so-called space-division multiple access (SDMA), where multiple spatially separated users are served simultaneously. The optimal beamforming vectors can be found to minimize the average transmit power under each user's Quality-of-Service (QoS) constraint. We show that intentionally adding timing offsets between the transmitted signals can significantly reduce the average transmission power compared with the conventional optimal beamforming method while providing the same QoSs for users. The frequency-selectivity in communication channels provides the opportunity to exploit intelligent design for performance improvement. The frequency-selectivity is limited in environments with line-of-sight links or little scattering. In such environments, we propose the use of intentional time delays to induce frequency-selectivity that can be exploited. We provide three different methods exploiting the artificially induced frequency-selectivity which improve the performance with a computational complexity similar to that of the optimal synchronous beamforming. We derive the expressions for the achievable rates using the proposed methods and then provide efficient algorithms to solve the minimum power optimization. We show analytically and numerically that our proposed methods can provide the same QoS while serving more users, utilizing a fewer number of transmit antennas and using reduced power compared with the conventional beamforming methods.

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between the mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature. In addition, we also propose a robust joint transceiver design for imperfect channel state information.

We investigate practical aspects of a recently introduced blind (noncoherent) communication scheme, called modulation on conjugate-reciprocal zeros (MOCZ). MOCZ is suitable for a reliable transmission of sporadic and short-packets at ultra-low latency and high spectral efficiency via unknown multipath channels, which are assumed to be static over the receive duration of one packet. The information is modulated on the zeros of the transmitted discrete-time baseband signal's z- transform. Because of ubiquitous impairments between the transmitter and receiver clocks, a carrier frequency offset occurs after down-conversion to the baseband. This results in a common rotation of the zeros. To identify fractional rotations of the base angle in the zero-pattern, we propose an oversampled direct zero-testing decoder to identify the most likely one. Integer rotations correspond to cyclic shifts of the binary message, which we determine by cyclically permutable codes (CPC). Additionally, the embedding of CPCs into cyclic codes, enables additive error-correction which reduces the bit-error-rate tremendously. Furthermore, we exploit the trident structure in the signal's autocorrelation for an energy based detector to estimate timing offsets and the effective channel delay spread. We finally demonstrate how this joint data and channel estimation can be largely improved by receive antenna diversity at low SNR.

Recent studies have demonstrated the superiority of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in cooperative communication networks. In this paper, we propose a novel half-duplex cooperative asynchronous NOMA (C-ANOMA) framework with user relaying, where a timing mismatch is intentionally added in the broadcast signal. We derive the expressions for the throughputs of the strong user (acts as relay) which employs the block-wise successive interference cancellation (SIC) and the weak user which combines the symbol-asynchronous signal with the interference-free signal. We analytically prove that in the C-ANOMA systems with a sufficiently large block length, the strong user attains the same throughput to decode its own message while both users can achieve a higher throughput to decode the weak user's message compared with those in the cooperative NOMA (C-NOMA) systems. Besides, we obtain the optimal timing mismatch when the block length goes to infinity. Furthermore, to exploit the trade-off between the power consumption of the base station and that of the relay user, we solve a weighted sum power minimization problem under quality of services (QoS) constraints. Numerical results show that the C-ANOMA system can consume less power compared with the C-NOMA system to satisfy the same QoS requirements.

In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are additively and multiplicatively weighted for each reproduction point resulting in a heterogeneous quantization problem, as used for example in deployment problems of sensor networks. Whereas, normally in such problems, the average distortion is minimized for given weights (parameters), we will optimize the quantization problem over all weights, i.e., we tune or control the distortion functions in our favor. For a uniform source distribution in one-dimension, we derive the unique minimizer, given as the uniform scalar quantizer with an optimal common weight. By numerical simulations, we demonstrate that this result extends to two-dimensions where asymptotically the parameter optimized quantizer is the hexagonal lattice with common weights. As an application, we will determine the optimal deployment of unmanned aerial vehicles (UAVs) to provide a wireless communication to ground terminals under a minimal communication power cost. Here, the optimal weights relate to the optimal flight heights of the UAVs.

In this work, we investigate the characteristics of spectrally efficient frequency division multiplexing (SEFDM). We prove that as the number of sub-carriers goes to infinity, the system model becomes rank-deficient and the number of zero eigenvalues is proportional to the frequency compression factor. We propose to transmit the superimposed symbols through the non-zero eigenvalues using proper power allocation. At the receiver side, we apply the block-wise zero forcing with successive interference cancellation (ZF-SIC) detection method and an approximation of the symbol error rate performance is provided. We compare the performance of the proposed method with that of the orthogonal frequency division multiplexing (OFDM) method in additive white Gaussian noise (AWGN) and frequency selective channels.

Recent studies have demonstrated that asynchronous non- orthogonal multiple access (ANOMA) outperforms conventional (synchronous) NOMA under the condition of perfect channel state information (CSI). In this paper, we investigate a downlink ANOMA system with imperfect CSI. It is analytically proved that the ANOMA system with a relatively large frame length outperforms the NOMA system in terms of the outage probability. To this end, we derive the analytical expressions for the individual throughput of each user and simplify them in the asymptotic case of infinite frame length. Besides, we show that with channel estimation error, the optimal timing mismatch converges to half of a single symbol length as the frame length goes to infinity.

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature.

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi diagrams as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd-like algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average.

Minimizing the repair bandwidth, i.e., the amount of information from the helper nodes needed for recovering the content of one failed node in an erasure-coded distributed storage system, has been the focus of many works in the literature. We investigate another important performance metric, namely the I/O cost, which specifies the amount of information that needs to be read by the helper nodes during the repair process of one failed node. We analyze the I/O costs of a few known repair schemes for Reed-Solomon codes of various lengths, in contrast to the previous works in this direction, which only studied the I/O costs in repairing full-length Reed-Solomon codes.

We investigate the effect of time asynchrony in non- orthogonal multiple access (NOMA) for downlink transmission. First, we analyze the benefits of adding intentional timing offsets to the conventional power domain-NOMA (P-NOMA). The resulting method, called Asynchronous-Power Domain-NOMA (AP-NOMA), reduces the mutual interference which results in enlarging the achievable rate-region of the conventional P-NOMA. We also propose another multiple access scheme, called time domain-NOMA (T-NOMA), which provides higher degrees of freedom for users compared with the conventional P-NOMA or even the modified AP-NOMA. T-NOMA adopts a precoding at the base station and a linear preprocessing scheme at the receiving user which decomposes the broadcast channel into parallel channels circumventing the need for Successive Interference Cancellation (SIC). Numerical results show that T-NOMA outperforms AP-NOMA and both outperform the conventional P-NOMA.

Slow fading broadcast channels can model a wide range of applications in wireless networks. Due to delay requirements and the unavailability of the channel state information at the transmitter (CSIT), these channels for many applications are non-ergodic. The appropriate measure for designing signals in non-ergodic channels is the outage probability. In this paper, we provide a method to optimize space-time block codes (STBCs) based on the outage probability at moderate SNRs. Multilevel polar coded-modulation is a new class of coded-modulation techniques that benefits from low-complexity decoders and simple rate matching. In this paper, we derive the outage optimality condition for multistage decoding and propose a rule for determining component code rates. We also derive an upper bound on the outage probability of STBCs for designing the set-partitioning-based labeling. Finally, due to the optimality of the outage-minimized STBCs for long codes, we introduce a novel method for the joint optimization of short-to-moderate length polar codes and STBCs.

We consider the ad-hoc networks consisting of n wireless nodes that are located on the plane. Any two given nodes are called neighbors if they are located within a certain distance (communication range) from one another. A given node can be directly connected to any one of its neighbors, and picks its connections according to a unique topology control algorithm that is available at every node. Given that each node knows only the indices (unique identification numbers) of its one and two-hop neighbors, we identify an algorithm that preserves connectivity and can operate without the need of any synchronization among nodes. Moreover, the algorithm results in a sparse graph with at most 5n edges and a maximum node degree of 10. Existing algorithms with the same promises further require neighbor distance and/or direction information at each node. We also evaluate the performance of our algorithm for random networks. In this case, our algorithm provides an asymptotically connected network with n(1+o(1)) edges with a degree less than or equal to 6 for 1-o(1) fraction of the nodes. We also introduce another asynchronous connectivity-preserving algorithm that can provide an upper bound as well as a lower bound on node degrees.

Reed-Solomon (RS) codes are widely used in distributed storage systems. In this paper, we study the repair bandwidth and sub-packetization size of RS codes. The repair bandwidth is defined as the amount of transmitted information from surviving nodes to a failed node. The RS code can be viewed as a polynomial over a finite field GF(qI) evaluated at a set of points, where I is called the sub-packetization size. Smaller bandwidth reduces the network traffic in distributed storage, and smaller I facilitates the implementation of RS codes with lower complexity. Recently, Guruswami and Wootters proposed a repair method for RS codes when the evaluation points are the entire finite field. While the sub-packetization size can be arbitrarily small, the repair bandwidth is higher than the minimum storage regenerating (MSR) bound. Tamo, Ye, and Barg achieved the MSR bound but the sub-packetization size grows faster than the exponential function of the number of the evaluation points. In this paper, we present code constructions and repair schemes that extend these results to accommodate different sizes of the evaluation points. In other words, we design schemes that provide points in between. These schemes provide a flexible tradeoff between the sub-packetization size and the repair bandwidth. In addition, we generalize our schemes to manage multiple failures.

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms.

We aim to design an analog-only beamforming scheme for downlink multi-user mm-wave systems to optimize the beamforming gain and the inter-user interference at the same time. Traditional analog beamforming schemes, such as the beam selection method, use the array response vector corresponding to the strongest path of the channel to generate a beam pointing to the user. In multi-user systems, such schemes will lead to large inter-user interference, especially when the users are closely located. In this paper, we formulate a multi-objective problem to strike a balance between the beamforming gain and the inter-user interference. To solve the problem, we first use the weighted-sum method to transform the multi-objective problem into a single-objective problem. Then, we use the semi-definite programing technique to make the analog beamforming with constant-magnitude constraints tractable. Furthermore, to alleviate the effects of the channel estimation and feedback quantization errors, we design a robust beamforming scheme to provide robustness against imperfect channel information. We first develop a channel error model for the scattering clustered channel model, which can serve as a general channel error model for the mm-wave channels. Then, we formulate a multi-objective problem using the stochastic approach to suppress the interference and enhance the beamforming gain at the same time. The simulation results show that our proposed non-robust multi-user analog beamformer outperforms the traditional analog beamforming method when the SNR is high and our proposed robust beamformer can provide up to 109% improvement in the sum-rate compared with the beam selection method.

In this paper, the outage behavior and diversity order of the mixture transceiver architecture for multiple-input single-output broadcast channels are analyzed. The mixture scheme groups users with closely-aligned channels and applies superposition coding and successive interference cancellation decoding to each group composed of users with closely-aligned channels, while applying zero-forcing beamforming across semi-orthogonal user groups. In order to enable such analysis, closed-form lower bounds on the achievable rates of a general multiple-input single-output broadcast channel with superposition coding and successive interference cancellation are newly derived. By employing channel-adaptive user grouping and proper power allocation, which ensures that the channel subspaces of user groups have an angle larger than a certain threshold, it is shown that the mixture transceiver architecture achieves full diversity order in multiple-input single-output broadcast channels and opportunistically increases the multiplexing gain while achieving full diversity order. Furthermore, the achieved full diversity order is the same as that of the single-user maximal ratio transmit beamforming. Hence, the mixture scheme can provide reliable communication under channel fading for ultra-reliable low latency communication. The numerical results validate our analysis and show the outage superiority of the mixture scheme over conventional transceiver designs for multiple-input single-output broadcast channels.

Recent studies have numerically demonstrated the possible advantages of the asynchronous non-orthogonal multiple access (ANOMA) over the conventional synchronous non-orthogonal multiple access (NOMA). The ANOMA makes use of the oversampling technique by intentionally introducing a timing mismatch between symbols of different users. Focusing on a two-user uplink system, for the first time, we analytically prove that the ANOMA with a sufficiently large frame length can always outperform the NOMA in terms of the sum throughput. To this end, we derive the expression for the sum throughput of the ANOMA as a function of signal-to-noise ratio, frame length, and normalized timing mismatch. Based on the derived expression, we find that users should transmit at full powers to maximize the sum throughput. In addition, we obtain the optimal timing mismatch as the frame length goes to infinity. Moreover, we comprehensively study the impact of timing error on the ANOMA throughput performance. Two types of timing error, i.e., the synchronization timing error and the coordination timing error, are considered. We derive the throughput loss incurred by both types of timing error and find that the synchronization timing error has a greater impact on the throughput performance compared with the coordination timing error.

The large spectrum available in the millimeter- Wave (mmWave) band has emerged as a promising solution for meeting the huge capacity requirements of the 5th generation (5G) wireless networks. However, to fully harness the potential of mmWave communications, obstacles such as severe path loss, channel sparsity and hardware complexity should be overcome. In this paper, we introduce a generalized reconfigurable antenna multiple-input multiple-output (MIMO) architecture that takes advantage of lens-based reconfigurable antennas. The considered antennas can support multiple radiation patterns simultaneously by using a single RF chain. The degrees of freedom provided by the reconfigurable antennas are used to, first, combat channel sparsity in MIMO mmWave systems. Further, to suppress high path loss and shadowing at mmWave frequencies, we use a rate- one space-time block code. Our analysis and simulations show that the proposed reconfigurable MIMO architecture achieves full-diversity gain by using linear receivers and without requiring channel state information at the transmitter. Moreover, simulations show that the proposed architecture outperforms traditional MIMO transmission schemes in mmWave channel settings.

There has been extensive research on large scale multi-user multiple-input multiple-output (MU-MIMO) systems recently. However, there are many obstacles to achieve full potential of using large number of receive antennas. One of the main issues, which will be investigated thoroughly in this paper, is timing asynchrony among signals of different users. Most of the works in the literature, assume that received signals are perfectly aligned which is not practical. We develop a mathematical model that explicitly accounts for the timing mismatch among the received signals and shows the detrimental impact of asynchrony on the MRC receiver.

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Two key issues in MWSNs - energy consumption, which is dominated by sensor movement, and sensing coverage - have attracted plenty of attention, but the interaction of these issues is not well studied. To take both sensing coverage and movement energy consumption into consideration, we model the sensor deployment problem as a constrained source coding problem. Our goal is to find an optimal sensor deployment to maximize the sensing coverage with specific energy constraints. We derive necessary conditions to the optimal sensor deployment with (i) total energy constraint and (ii) network lifetime constraint. Using these necessary conditions, we design Lloyd-like algorithms to provide a trade-off between sensing coverage and energy consumption. Simulation results show that our algorithms outperform the existing relocation algorithms.

The highly sparse nature of propagation channels and the restricted use of radio frequency (RF) chains at transceivers limit the performance of millimeter wave (mmWave) multiple-input multiple- output (MIMO) systems. Introducing reconfigurable antennas to mmWave can offer an additional degree of freedom on designing mmWave MIMO systems. This paper provides a theoretical framework for studying the mmWave MIMO with reconfigurable antennas. We present an architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital beamformers and reconfigurable antennas at both the transmitter and the receiver. We show that employing reconfigurable antennas can provide throughput gain for the mmWave MIMO. We derive the expression for the average throughput gain of using reconfigurable antennas, and further simplify the expression by considering the case of large number of reconfiguration states. In addition, we propose a low-complexity algorithm for the reconfiguration state and beam selection, which achieves nearly the same throughput performance as the optimal selection of reconfiguration state and beams by exhaustive search.

Recent studies have shown that asynchronous non- orthogonal multiple access (ANOMA) outperforms conventional synchronous non-orthogonal multiple access (NOMA) by taking advantage of artificial timing mismatch with oversampling. For the first time, we comprehensively study the impact of timing errors on the performance of uplink ANOMA systems in this paper. We consider two types of timing errors, which are the synchronization timing error and the coordination timing error. We analyze how the timing errors affect ANOMA systems, and derive the throughput loss of ANOMA systems incurred by both the synchronization timing error and the coordination timing error. An interesting finding is that the synchronization timing error has a larger impact on the throughput performance of ANOMA systems compared with the coordination timing error.

Multi-tier networks have many applications in different fields. We define a novel two-tier quantizer that can be applied to different node deployment problems including the energy conservation in two-tier wireless sensor networks consisting of N access points (APs) and M fusion centers (FCs). We aim at finding an optimal deployment of APs and FCs to minimize the average weighted total, or Lagrangian, of sensor and AP powers. For one FC, M = 1, we show that the optimal deployment of APs is simply a linear transformation of the optimal N-level quantizer for density f, and the sole FC should be located at the geometric centroid of the sensing field. We also provide the exact expression of the AP-Sensor power function and prove its convexity. For more than one FC, M > 1, we provide a necessary condition for the optimal deployment. Furthermore, to numerically optimize the AP and FC deployment, we propose three Lloyd-like algorithms and analyze their convergence. Simulation results show that our algorithms outperform the existing algorithms.

The performance of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is limited by the sparse nature of propagation channels and the restricted number of radio frequency chains at transceivers. The introduction of reconfigurable antennas offers an additional degree of freedom on designing mmWave MIMO systems. This paper provides a theoretical framework for studying the mmWave MIMO with reconfigurable antennas. Based on the virtual channel model, we present an architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital beamformers and reconfigurable antennas at both the transmitter and the receiver. We show that employing reconfigurable antennas can provide throughput gain for the mmWave MIMO. We derive the expression for the average throughput gain of using reconfigurable antennas in the system, and further derive the expression for the outage throughput gain for the scenarios where the channels are (quasi) static. Moreover, we propose a low-complexity algorithm for reconfiguration state selection and beam selection. Our numerical results verify the derived expressions for the throughput gains and demonstrate the near-optimal throughput performance of the proposed low-complexity algorithm.

For physical layer security with multiple antennas over wireless channels, we consider an artificial noise-aided secure beamforming system. A transmitter can send the confidential information to the legitimate user more securely without eavesdropping when an artificial jamming signal interfering eavesdroppers is transmitted with the confidential information signal. The transmitter splits its transmit power for both the information and jamming signals with a power splitting factor. Under such a system model, we investigate the impacts on secrecy performance of a power splitting factor, the numbers of antennas and eavesdroppers, and noise variance at the legitimate receivers and eavesdroppers by analyzing the expected secrecy rate. The optimal power splitting factor and limiting secrecy rate with large number of antennas are also derived. Moreover, we examine the expected secrecy rate loss caused by channel variation over time. It is shown that the secrecy rate loss is independent of system parameters like a power splitting factor, the numbers of antennas and eavesdroppers in a high signal to interference plus noise ratio (SINR) region. In a low SINR region, on the other hand, the secrecy rate loss changes with system parameters. Simulation results verify these observations on ergodic secrecy rate and its loss due to time-varying channels.

In this paper, we study covert communication in wireless networks consisting of a transmitter, Alice, an intended receiver, Bob, a warden, Willie, and a Poisson field of interferers. Bob and Willie are subject to uncertain shot noise due to the ambient signals from interferers in the network. With the aid of stochastic geometry, we analyze the throughput of the covert communication between Alice and Bob subject to given requirements on the covertness against Willie and the reliability of decoding at Bob. We consider non-fading and fading channels. We analytically obtain interesting findings on the impacts of the density and the transmit power of the concurrent interferers on the covert throughput. That is, the density and the transmit power of the interferers have no impact on the covert throughput as long as the network stays in the interference-limited regime, for both the non-fading and the fading cases. When the interference is sufficiently small and comparable with the receiver noise, the covert throughput increases as the density or the transmit power of the concurrent interferers increases.

We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes, where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario, where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmit antennas, a short-term power constraint P , and target data rate p. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1bits per channel state and via training R2transmit antennas on average, where R1and R2are independent oft, and depend only on p and P . In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme.

We study a two-tiered wireless sensor network (WSN) consisting of N access points (APs) and M base stations (BSs). The sensing data, which is distributed on the sensing field according to a density function f, is first transmitted to the APs and then forwarded to the BSs. Our goal is to find an optimal deployment of APs and BSs to minimize the average weighted total, or Lagrangian, of sensor and AP powers. For M = 1, we show that the optimal deployment of APs is simply a linear transformation of the optimal N-level quantizer for density f, and the sole BS should be located at the geometric centroid of the sensing field. Also, for a one-dimensional network and uniform f, we determine the optimal deployment of APs and BSs for any N and M. Moreover, to numerically optimize node deployment for general scenarios, we propose one-and two-tiered Lloyd algorithms and analyze their convergence properties. Simulation results show that, when compared to random deployment, our algorithms can save up to 79% of the power on average.

In this paper, we analyze downlink non-orthogonal multiple access (NOMA) networks with limited feedback. Our goal is to derive appropriate transmission rates for rate adaptation based on distributed channel feedback information from two receivers. We propose an efficient quantizer with variable-length encoding that approaches the best performance of the case where perfect channel state information is available everywhere. We prove that in the typical application with two receivers, the loss in the minimum rate decays at least exponentially with the minimum feedback rate. Numerical simulations are presented to demonstrate the efficiency of our proposed quantizer and the accuracy of the analytical results.

Space-time block codes (STBCs) have been designed and used to achieve the diversity and multiplexing gains in multiple antenna systems. STBCs have been typically designed based on rank and determinant criteria which can provide good performance at high signal-tonoise ratios (SNRs). Later, STBCs are designed based on mutual information to provide good performance at a specific SNR corresponding to the forward error correction (FEC) code rate. However, once the FEC code and STBC are concatenated, to achieve the best performance, STBC should be designed by considering the structure of the FEC code and the corresponding decoder in addition to the code rate. Polar codes are a new class of FEC codes that benefit from a variety of low complexity decoders and simple rate matching. Polar codes can be efficiently designed for a specific channel and STBC. Therefore, by changing the parameters of a specific STBC and optimizing the polar code for each new STBC, the best match between polar codes and STBCs can be found. Throughout this paper, we introduce a simple method for joint optimization of polar codes and STBCs and show that it can substantially improve the performance of the concatenated scheme.

We consider ad-hoc networks consisting of n wireless nodes that are located on the plane. Any two given nodes are called neighbors if they are located within a certain distance (communication range) from one another. A given node can be directly connected to any one of its neighbors and picks its connections according to a unique topology control algorithm that is available at every node. Given that each node knows only the indices (unique identification numbers) of its one- and two-hop neighbors, we identify an algorithm that preserves connectivity and can operate without the need of any synchronization among nodes. Moreover, the algorithm results in a sparse graph with at most 5n edges and a maximum node degree of 10. Existing algorithms with the same promises further require neighbor distance and/or direction information at each node. We also evaluate the performance of our algorithm for random networks. In this case, our algorithm provides an asymptotically connected network with n(1 + 0(1)) edges with a degree less than or equal to 6 for 1-0(1) fraction of the nodes. Numerical results confirm our analytical findings.

In this paper, we present an asynchronous analog network coding (AANC) scheme for multiuser cooperative communications. In particular, we consider a relay network comprised of multiple sources and one relay. With conventional analog network coding, i.e., synchronous analog network coding (SANC), perfect timing synchronization is assumed. In contrast, in AANC, the signals from the sources arrive at the relay and destination with different time delays. Then the relay amplifies the received asynchronous combination of the transmitted signals and forwards it to the destination. We present a simple decoding strategy at the destination and examine the diversity order by investigating the asymptotic performance at a high signal to noise ratio. Our results show that AANC achieves a greater diversity order compared to that of SANC with the same linear decoding complexity. In addition, we present the Viterbi decoding for AANC and use simulations to compare its performance with that of SANC with maximum likelihhood decoding. We show that AANC has a better symbol error rate performance compared to that of SANC.

We consider a slowly fading multicast channel with one T-antenna transmitter and K single-antenna receivers with the goal of minimizing channel outage probability using quantized beamforming. Our focus is on a distributed limited feedback scenario where each receiver can only quantize and send feedback information regarding its own receiving channels. A classical result in point-to-point quantized beamforming is that a necessary and sufficient condition for full diversity is to have ⌈log2T⌉ bits from the receiver with an appropriate quantizer. We first generalize this result to multicast beamforming systems and show that a necessary and sufficient condition to achieve full diversity for all receivers is to have ⌈log2T⌉ bits from each receiver with an appropriate quantizer. Achievable diversity gains with a long-term power constraint are also discussed. Moreover, for a two-receiver system and with R feedback bits per receiver, we show that the outage performance with quantized R beamforming is within O(2-R/32T2)dBs to the performance with full channel state information at the transmitter (CSIT). This constitutes, in the context of multicast channels, the first example of a distributed limited feedback scheme whose performance can provably approach the performance with full CSIT. Numerical simulations confirm our analytical findings.

In this paper, we analyze downlink non-orthogonal multiple access (NOMA) networks with limited feedback. Our goal is to derive appropriate transmission rates for rate adaptation and minimize outage probability of minimum rate for the constant-rate data service, based on distributed channel feedback information from receivers. We propose an efficient quantizer with variable-length encoding that approaches the best performance of the case where perfect channel state information is available everywhere. We prove that in the typical application with two receivers, the losses in the minimum rate and outage probability decay at least exponentially with the minimum feedback rate. We analyze the diversity gain and provide a sufficient condition for the quantizer to achieve the maximum diversity order. For NOMA with K receivers where K > 2, we solve the minimum rate maximization problem within an accuracy of ϵ in time complexity of O (K log 1/ϵ), and then, we apply the previously proposed quantizers for K = 2 to the case of K > 2. Numerical simulations are presented to demonstrate the efficiency of our proposed quantizers and the accuracy of the analytical results.

We introduce the concept of space-time super-modulation according to which additional low-rate and highly reliable information can be transmitted on top of traditionally modulated and space-time encoded information, without increasing the transmitted block length or degrading their error-rate performance. This is achieved by exploiting the temporal redundancy introduced by the space-time block codes and, specifically, by efficiently mapping transmission patterns to specific information content. We show that space-time super-modulation can be efficiently used in the context of machine-type communications to enable one-shot grant-free joint medium access and rateless data transmission while reducing or even eliminating the need for transmitting preamble sequences. As a result, compared with traditional approaches that use correlatable preamble sequences or encoded preambles to transmit the signature information of transmitted packets, space-time super-modulation can achieve significant throughput gains. For example, we show up to 35% throughput gains from the second best examined preamble-based scheme when transmitting blocks of 200 bits.

In this paper, we present an asynchronous network coding (ANC) transmission strategy for multiuser co-operative networks. In particular, we consider a relay network comprised of multiple sources and mutiple relays. The sources all transmit simultaneously. The relay receives a sum of the signals with different delays. The decoded signals at the relay are network-coded by asynchronous delays and transmitted to the destination. We first present a decoding strategy for our ANC. We also compare our proposed ANC to complex field network coding and show the superiority of our proposed scheme in terms of decoding complexity and bit error rate (BER) performance. Next, we develop three relay selection strategies with different complexity in conjunction with the proposed ANC scheme. The proposed full selection and joint selection (JS) schemes consider both channel quality and delay effect in the selection. But the proposed individual selection scheme only considers channel quality in the selection. We derive an end-to-end (E2E) BER expression in terms of worst E2E SNR for the JS scheme. We also theoretically show that the proposed schemes achieve full diversity. In addition, we compare the performance of the three proposed relay selection schemes by simulations.

In quantization theory, one typically works with a unique distortion function (e.g. the squared-error distortion function) that quantifies the cost of quantizing a given source sample to any given reproduction point of the quantizer. Many applications, however, induce quantization problems where different distortion functions should be associated with different reproduction points. In this paper, we consider the case where the distortion of a given reproduction point is the squared distance to the source sample weighted by a factor that varies from one reproduction point to another. For a uniform distribution of source samples, we determine the corresponding optimal scalar quantizers and their distortions. We also find upper and lower bounds on the distortion of optimal vector quantizers. For non-uniform distributions, we provide a high resolution analysis of the minimum possible distortion. As a byproduct of our analysis, we show that for certain distributions of weights, a tessellation of non-congruent quantization cells can outperform tessellations of congruent polytopes. This suggests that Gersho's conjecture cannot be extended to the case of squared-error distortion functions with weighted reproduction points.

In this paper, we show that by investigating inherent time delays between different users in a multiuser scenario, we are able to cancel interference more efficiently. Time asynchrony provides another tool to cancel interference which results in preserving other resources like frequency, time and code. Therefore, we can save the invaluable resource of frequency band and also increase spectral efficiency. A sampling method is presented which results in independent noise samples and obviates the need for the complex process of noise whitening. By taking advantage of this sampling method and its unique structure, we implement maximum-likelihood sequence detection which outperforms synchronous maximum-likelihood detection. We also present successive interference cancellation with hard decision passing, which gives rise to a novel forward-backward belief propagation method. Next, the performance of zero forcing detection is analyzed. Simulation results are also presented to verify our analysis.

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both homogeneous and heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. Traditionally, coverage is the area covered by the sensor nodes. However, when the communication range is limited, a WSN may be divided into several disconnected sub-graphs. Under such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate distortion measure, we propose a Restrained Lloyd (RL) algorithm and a Deterministic Annealing (DA) algorithm to optimize sensor deployment in both homogeneous and heterogeneous WSNs. Our simulation results show that both DA and RL algorithms outperform the existing Lloyd algorithm when communication range is limited.

We introduce the concept of Space-Time Super- Modulation according to which additional low rate and highly reliable information can be transmitted by further super-modulating blocks of traditionally modulated and space-time encoded information. This is achieved by exploiting the redundant information introduced by the space-time block codes and, specifically, by efficiently mapping transmission patterns to specific information content. It is shown that Space-Time Super-Modulation can be efficiently used in the context of machine-type communications to enable joint medium access and rateless data transmission while minimizing or even eliminating the need for transmitting preamble sequences. Compared with traditional approaches that use encoded preambles or preambles based on Zadoff-Chu sequences to transmit the signature information of transmitted packets, Space-Time Super-Modulation can achieve throughput gains of more than 35% when transmitting blocks of 200 symbols.

Pilot contamination has been regarded as the bottleneck in time division duplexing (TDD) multi- cell massive multiple-input multiple-output (MIMO) systems. The pilot contamination problem cannot be addressed with large-scale antenna arrays. We provide a novel asynchronous channel training scheme to reduce the impact of pilot contamination without the cooperation of base stations. The scheme takes advantage of sampling diversity by inducing intentional timing mismatch. Then, the optimal linear minimum mean square error (LMMSE) estimator is designed to minimize the channel matrix estimation error. Finally, simulation results demonstrate that our scheme can provide significant performance improvement compared with the conventional synchronous systems that suffer from pilot contamination.

We consider a slow-fading multicast channel with one T-antenna transmitter and K single-antenna receivers with the goal of minimizing channel outage probability using quantized beamforming. Our focus is on a distributed limited feedback scenario where each receiver can only quantize and send feedback information regarding its own receiving channels. A classical result in point-to-point quantized beamforming is that a necessary and sufficient condition for full diversity is to have ⌈log2T⌉ bits from the receiver. We first generalize this result to multicast beamforming systems and show that a necessary and sufficient condition to achieve full diversity for all receivers is to have ⌈log2T⌉ bits from each receiver. Also, for a two-receiver system and with R feedback bits per receiver, we show that the outage performance with quantized beamforming is within O(2−R/32T2)dBs of the performance with full channel state information at the transmitter (CSIT). This constitutes, in the context of multicast channels, the first example of a distributed limited feedback scheme whose performance can provably approach the performance with full CSIT.

We consider a fixed data rate slow-fading MIMO channel with a long-term power constraint P at the transmitter. A relevant performance limit is the delay-limited capacity, which is the largest data rate at which the outage probability is zero. It is well known that if both the transmitter and the receiver have full channel state information (CSI) and if either of them has multiple antennas, the delay-limited capacity is non-zero and grows logarithmically with P. Achieving even a positive delay-limited capacity, however, becomes a difficult task when the CSI at the transmitter (CSIT) is imperfect. In this context, the standard partial CSIT model where the transmitter has a fixed finite bit of quantized CSI feedback for each channel state results in zero delay-limited capacity. We show that by using a variable-length feedback scheme that utilizes a different number of feedback bits for different channel states, a non-zero delaylimited capacity can be achieved if the feedback rate is greater than 1 bit per channel state. Moreover, we show that the delaylimited capacity loss due to finite-rate feedback decays at least inverse linearly with respect to the feedback rate. We also discuss the applications to ergodic MIMO channels.

Full-reference (FR) video quality estimators (QEs) resize either the distorted input video or the reference video to compute the quality when these videos have different spatial resolutions. This resizing operation causes several limitations. Multiscale Image Quality Estimator (MIQE) overcomes those limitations for images, but it does not consider the temporal characteristics of video. In this paper, we develop an FR video QE that integrates MIQE with the motion information to estimate the quality of the distorted video without resampling the reference or the test videos. We also perform subjective tests to compare the proposed algorithm with the existing QEs. In these tests, the reference and the input videos are displayed at their native resolutions. The test results show that the proposed algorithm outperforms other QEs when the reference video and the input video have different spatial resolutions. We have also evaluated the performance of the approach using the Scalable Video Database.

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both the homogeneous and the heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. However, when the communication range is limited, a WSN is divided into several disconnected sub-graphs under certain conditions as we will discuss in this paper. In such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate sensing performance measure, we propose a Restrained Lloyd (RL) algorithm and a deterministic annealing (DA) algorithm to optimize sensor deployment in both the homogeneous and heterogeneous WSNs. Our simulation results show that both the DA and the RL algorithms outperform the existing algorithms when communication range is limited.

We study the channel quantization problem for amplify-and-forward (AF) relay networks and our target is to design a quantizer to minimize the outage probability. It is priorly known that any fixed-length quantizer with a finite-cardinality codebook cannot attain the same minimum outage probability as the case where all nodes in the AF relay networks have access to perfect channel state information (CSI). We propose variable-length quantizers with random infinite-cardinality codebooks for the sum and individual power constraints. We provide theoretical proofs and numerical simulations to validate that the proposed quantizers can achieve the full-CSI outage probabilities with finite average feedback rates.

We study the connectivity of wireless networks consisting of n nodes that are located independently and uniformly at random on the unit square. Our focus is on an orthogonal multiple access scenario where there are M orthogonal resources (e.g. time slots and/or frequency bands) that are to be assigned to each connection in the network. Correspondingly, we consider a disk-interference model where two nodes can be connected over resource m if (i) they are within communication range R, and (ii) no other node within distance R to either one of the two nodes uses the resource m. In such a scenario, it is known that if one is allowed to optimally choose (depending on the node locations) the node connections and the associated resources, the conditions R2∈ Θ(logn/n) and M ∈ Θ(log n) are necessary and sufficient to ensure asymptotically almost sure connectivity as n → ∞. We propose a distributed resource allocation scheme where each node, unaware of its (and other nodes') geographical location(s), decides on its connections and the associated resources by communicating with its neighboring nodes only. Our scheme provides a connected network under the best-possible conditions R2∈ O(log n/n) and M ∈ O(logn).

We study the channel quantization problem for amplify- and-forward (AF) relay networks with a sum power constraint for the relay nodes. Our target is to design a quantizer to minimize the outage probability. It is priorly known that any fixed-length quantizer with a finite- cardinality codebook cannot attain the same minimum outage probability as the case where all nodes in the AF relay networks have access to perfect channel state information (CSI). We propose a variable- length quantizer with a random infinite- cardinality codebook, and we prove that the proposed quantizer is able to achieve the full-CSI outage probability with a finite average feedback rate. Numerical simulations validate our theoretical analysis.

We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmitter antennas, a short-term power constraint P, and target data rate ρ. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1bits per channel state and via training R2transmitter antennas on average, where R1and R2are independent of t, and depend only on ρ and P.

We introduce cooperative quantizers for two-user interference channels where interference signals are treated as noise. Compared with the conventional quantizers where each receiver quantizes its own channel independently, the proposed cooperative quantizers allow multiple rounds of feedback communication in the form of conferencing between receivers. For both time-sharing and concurrent transmission strategies, we propose different cooperative quantizers to achieve the full-channel-state-information (full-CSI) network outage probability of sum rate and the full-CSI network outage probability of minimum rate, respectively. Our proposed quantizers only require finite average feedback rates, whereas the conventional quantizers require infinite rate to achieve the full-CSI performance. For the minimum rate, we also design cooperative quantizers for a joint time-sharing and concurrent transmission strategy that can approach the previously established optimal network outage probability with a negligible gap. Numerical simulations confirm that our cooperative quantizers based on conferencing outperform the conventional quantizers.

Cooperative communication is a promising path to recover from performance anomaly in IEEE 802.11 networks. However, a simple solution for employing multiple relays to enhance the relay link quality has not been proposed. The main obstacle for multiple relay utilization in distributed networks is that synchronizing relay transmissions requires huge signaling overhead. In this paper, we investigate the problem from both a physical-layer and MAC-layer point of view. In the physical layer, a simple, practical solution that provides diversity gain from asynchronous relay transmissions is introduced. In the MAC layer, a rate adaptation algorithm, RA-ARF, that takes the extra relay path into account is discussed, and R-MAC is designed to utilize relays in IEEE 802.11 networks. Our simulation results show considerable improvement in network performance using R-MAC.

Differential distributed space-time coding (D-DSTC) is a cooperative transmission technique that can improve diversity in wireless relay networks in the absence of channel information. Conventionally, it is assumed that channels are flat-fading and relays are perfectly synchronized at the symbol level. However, due to the delay spread in broadband systems and the distributed nature of relay networks, these assumptions may be violated. Hence, inter-symbol interference (ISI) may appear. This paper proposes a new differential encoding and decoding process for D-DSTC systems with multiple relays over slow frequency-selective fading channels with imperfect synchronization. The proposed method overcomes the ISI caused by frequency-selectivity and is robust against synchronization errors while not requiring any channel information at the relays and destination. Moreover, the maximum possible diversity with a decoding complexity similar to that of the conventional D-DSTC is attained. Simulation results are provided to show the performance of the proposed method in various scenarios.

We investigate the channel quantization problem for two-user multicast networks where the transmitter is equipped with multiple antennas and either receiver is equipped with only a single antenna. Our goal is to design a global quantizer to minimize the outage probability. It is known that any fixed-length quantizer with a finite-cardinality codebook cannot obtain the same minimum outage probability as the case where all nodes in the network know perfect channel state information (CSI). To achieve the minimum outage probability, we propose a variable-length global quantizer that knows perfect CSI and sends quantized CSI to the transmitter and receivers. With a random infinite-cardinality codebook, we prove that the proposed quantizer is able to achieve the minimum outage probability with a low average feedback rate. We also extend the proposed quantizer to the multicast networks with more than two users. Numerical simulations validate our theoretical analysis.

We propose several differential decoding schemes for asynchronous multi-user MIMO systems based on orthogonal space-time block codes (OSTBCs), where neither the transmitters nor the receiver has knowledge of the channel. First, we derive novel low complexity differential decoders by performing interference cancellation in time and employing different decoding methods. The decoding complexity of these schemes grows linearly with the number of users. We then present additional differential decoding schemes that perform significantly better than our low complexity decoders and outperform the existing synchronous differential schemes but require higher decoding complexity compared to our low complexity decoders. The proposed schemes work for any square OSTBC, any constant amplitude constellation, any number of users, and any number of receive antennas. Furthermore, we analyze the diversity of the proposed schemes and derive conditions under which our schemes provide full diversity. For the cases of two and four transmit antennas, we provide examples of PSK constellations to achieve full diversity. Simulation results show that our differential schemes provide good performance. To the best of our knowledge, the proposed differential detection schemes are the first differential schemes for asynchronous multi-user systems.

Differential distributed space-time coding (D-DSTC) has been considered to improve both diversity and data-rate in cooperative communications in the absence of channel information. However, conventionally, it is assumed that relays are perfectly synchronized in the symbol level. In practice, this assumption is easily violated due to the distributed nature of the relay networks. This paper proposes a new differential encoding and decoding process for D-DSTC systems with two relays. The proposed method is robust against synchronization errors and does not require any channel information at the destination. Moreover, the maximum possible diversity and symbol-by-symbol decoding are attained. Simulation results are provided to show the performance of the proposed method for various synchronization errors and the fact that our algorithm is not sensitive to synchronization error.

We introduce conferencing-based distributed channel quantizers for two-user interference networks where interference signals are treated as noise. Compared with the conventional distributed quantizers where each receiver quantizes its own channel independently, the proposed quantizers allow multiple rounds of feedback communication in the form of conferencing between receivers. We take the network outage probabilities of sum rate and minimum rate as performance measures and consider quantizer design in the transmission strategies of time sharing and interference transmission. First, we propose distributed quantizers that achieve the optimal network outage probability of sum rate for both time sharing and interference transmission strategies with an average feedback rate of only two bits per channel state. Then, for the time sharing strategy, we propose a distributed quantizer that achieves the optimal network outage probability of minimum rate with finite average feedback rate; conventional quantizers require infinite rate to achieve the same performance. For the interference transmission strategy, a distributed quantizer that can approach the optimal network outage probability of minimum rate closely is also proposed. Numerical simulations confirm that our distributed quantizers based on conferencing outperform the conventional ones.

We investigate the channel quantization problem for two-user multicast networks where the transmitter is equipped with multiple antennas and either receiver is equipped with only a single antenna. Our goal is to design a global quantizer to minimize the outage probability. It is known that any fixed-length quantizer with a finite-cardinality codebook cannot achieve the same minimum outage probability as the case where all nodes in the network know perfect channel state information (CSI). To achieve the minimum outage probability, we propose a variable-length global quantizer that knows perfect CSI and sends quantized CSI to the transmitter and receivers. With a random infinite-cardinality codebook, we prove that the proposed quantizer is able to achieve the minimum outage probability with a low average feedback rate. Numerical simulations also validate our theoretical analysis.

We propose a differential encoding scheme and several differential decoding schemes for asynchronous multiuser MIMO systems based on orthogonal space-time block codes (OSTBCs) where neither the transmitters nor the receiver has knowledge of the channel. We derive novel low complexity differential decoders by performing interference cancelation in time and employing different decoding methods. The decoding complexity of these schemes grows linearly with the number of users. The proposed schemes achieve full diversity and work for any square OSTBC, any number of users, and any number of receive antennas. Simulation results show that our differential schemes provide good performance. To the best of our knowledge, the proposed differential detection schemes are the first differential schemes for asynchronous multi-user systems.

We consider in this paper relay assignment for cooperative systems with mutiple source-destination pairs. The objective here is to assign the relays to the source-destination pairs in a such way that all pairs would achieve the maximum diversity. Normally, for a network with mutiple source-destination pairs, none of the destinations can acquire the channel state information (CSI) of the entire network without feedback. To this end, we design a practical limited feedback strategy in conjunction with two relay assignment schemes, i.e., fullset selection and subset selection, which are based on maximizing the minimum end to end (E2E) signal to noise ratio (SNR) among all pairs. In this strategy, each destination acquires its signal-to-noise ratios (SNRs), quantizes them, and feeds them back to the relays. The relays construct the end-to-end (E2E) SNR matrix and select the relay assignment choice from all possible relay assignment permutations or only a subset of these permutations. We analyze the performance of these schemes over independent Rayleigh fading channels in terms of the worst E2E SNR. We investigate the asymptotic performance of the proposed schemes at high SNR. We show that relay assignment with quantized CSI can achieves the same first-order diversity as that of full CSI, but there is a second-order diversity loss. We also demonstrate that increasing the quantization levels yields performance that is close to that of having full knowledge of the CSI.

Full reference video quality estimators (QEs) either resize the input video or the reference video to compute the quality when these videos have different spatial resolutions. This resizing operation causes several limitations. Multiscale Image Quality Estimator (MIQE) [1] overcomes those limitations for images but it does not consider the temporal characteristics of video. In this work, we develop a video quality estimator that integrates MIQE with the motion information to estimate the quality. We also perform subjective tests to compare the proposed algorithm with the existing QEs. Test results show that the proposed algorithm outperforms other QEs.

We consider a fixed data rate slow-fading MIMO channel with a long-term power constraint P at the transmitter. A relevant performance limit is the delay-limited capacity, which is the largest data rate at which the outage probability is zero. It is well-known that if both the transmitter and the receiver have full channel state information (CSI) and if either of them has multiple antennas, the delay-limited capacity is non-zero and grows logarithmically with P. Achieving even a positive delay-limited capacity however becomes a difficult task when the CSI at the transmitter (CSIT) is imperfect. In this context, the standard partial CSIT model where the transmitter has a fixed finite bits of quantized CSI feedback for each channel state results in zero delay-limited capacity. We show that by using a variable-length feedback scheme that utilizes different number of feedback bits for different channel states, a non-zero delay-limited capacity can be achieved if the feedback rate is greater than 1 bit per channel state. Moreover, we show that the delay-limited capacity loss due to finite-rate feedback decays at least inverse linearly with respect to the feedback rate.

Multimedia communication is becoming pervasive because of the progress in wireless communications and multimedia coding. Estimating the quality of the visual content accurately is crucial in providing satisfactory service. State of the art visual quality assessment approaches are effective when the input image and reference image have the same resolution. However, finding the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. To solve this problem, we develop a quality estimator (QE), which computes the quality of the input image without resampling the reference or the input images. In this paper, we begin by identifying the potential weaknesses of previous approaches used to estimate the quality of experience. Next, we design a QE to estimate the quality of a distorted image with a lower resolution compared with the reference image. We also propose a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. When the input and test images have different resolutions, the subjective tests demonstrate that in most cases the proposed method works better than other approaches. In addition, the proposed algorithm also performs well when the reference image and the test image have the same resolution.

We study a multiple-input single-output fading channel, where we would like to minimize the channel outage probability or symbol error rate (SER) by employing beamforming via quantized channel state information at the transmitter (CSIT). We consider a variable-length limited feedback scheme where the quantized CSIT is acquired through feedback binary codewords of possibly different lengths. We design and analyze the performance of the associated variable-length quantizers (VLQs) and compare their performance with the previously studied fixed-length quantizers (FLQs). For the outage probability performance measure, we construct VLQs that can achieve the full-CSIT performance with finite rate. Moreover, as the signal-to-noise ratio P tends to infinity, we show that VLQs can achieve the full-CSIT outage probability performance with asymptotically zero feedback rate. For the SER performance measure, we show that while the SER with full-CSIT is not achievable at any finite feedback rate, the diversity and array gains with full-CSIT can be achieved using VLQs with asymptotically zero feedback rate as P → ∞. Our results show that VLQs can significantly improve upon the traditional FLQs that require infinite feedback rate to achieve the outage probability or the diversity and array gains with full-CSIT.

We consider in this paper relay assignment for cooperative systems with mutiple source-destination pairs. The objective here is to assign relays to the source-destination pairs in such a way that all pairs achieve the maximum diversity. In networks with mutiple source-destination pairs, it is normally difficult for destinations to acquire the channel state information (CSI) of the entire network without feedback. To this end, we design a practical limited feedback strategy in conjunction with two relay assignment schemes, i.e., fullset selection and subset selection, which are based on maximizing the minimum end-to-end (E2E) signal to noise ratio (SNR) among all pairs. In this strategy, each destination acquires its SNR, quantizes it, and feeds it back to the relays. The relays then construct the E2E SNR table and select the relay assignment permutation from all possible relay assignment permutations or only a subset of these permutations. We analyze the performance of these schemes over independent Rayleigh fading channels in terms of the worst E2E SNR. We derive closed-form expressions for the E2E bit error rate (BER) and investigate the asymptotic performance at high SNR. We show that relay assignment with quantized CSI can achieve the same first-order diversity as that of the full CSI case, but there is a second-order diversity loss. We also demonstrate that increasing the quantization levels yields performance that is close to that of having full knowledge of the CSI.

We identify a practical vector quantizer design problem where any fixed-length quantizer (FLQ) yields non-zero distortion at any finite rate, while there is a variable-length quantizer (VLQ) that can achieve zero distortion with arbitrarily low rate. The problem arises in a t × 1 multiple-antenna fading channel where we would like to minimize the channel outage probability by employing beam forming via quantized channel state information at the transmitter (CSIT). It is well-known that in such a scenario, finite-rate FLQs cannot achieve the full-CSIT (zero distortion) outage performance. We construct VLQs that can achieve the full-CSIT performance with finite rate. In particular, with P denoting the power constraint of the transmitter, we show that the necessary and sufficient VLQ rate that guarantees the full-CSIT performance is Θ(1/P). We also discuss several extensions (e.g. to precoding) of this result.

This paper explores the use of selection diversity for interference alignment systems. Multiple distinct alignment modes exist for certain types of interference network. By selecting the best alignment mode to support the signal-to-noise rate (SNR) of the worst user, a diversity gain improvement can be observed. This paper shows that in a 3-user double-antenna interference channel, a system switching between two alignment modes achieves a diversity gain of 2 in the fixed-rate regime and the maximum degree-of-freedom (DoF) gain of 3 in the variable-rate regime. Consequently, we disprove a previous feasibility condition for diversity in alignment systems, which requires a trade-off between the DoF gain and the diversity gain.

State of the art visual quality assessment methods are effective when the input image and the reference image have the same resolution. However, estimating the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. In this work, we design a quality estimator (QE) to estimate the quality of a distorted image with a lower resolution compared to the reference image. We also present a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. The subjective tests demonstrate that the proposed method works better than other approaches.

We consider a t × 1 multiple-antenna fading channel with quantized channel state information at the transmitter (CSIT). Our goal is to maximize the diversity and array gains that are associated with the symbol error rate (SER) performance of the system. It is well-known that for both beamforming and precoding strategies, finite-rate fixed-length quantizers (FLQs) cannot achieve the full-CSIT diversity and array gains. In this work, for any function f(P) ϵ ω(1), we construct variable-length quantizers (VLQs) that can achieve these full-CSIT gains with rates 1 + (/(P) log P)/P and 1 + f(P)/Ptfor the beamforming and precoding strategies, respectively, where P is the power constraint of the transmitter. We also show that these rates are the best possible up to o(l) multipliers in their P-dependent terms. In particular, although the full-CSIT SER is not achievable at any (even infinite) feedback rate, the full-CSIT diversity and array gains can be achieved with a feedback rate of 1 bit per channel state asymptotically as P → ∞.

This paper proposes a differential encoding scheme and several differential decoding schemes for a two-user MIMO system based on orthogonal space-time block codes (OSTBCs) when neither the transmitter nor the receiver knows the channel. We derive low complexity differential decoders for users with two transmit antennas based on three different decoding methods. We deploy dynamic programming in our methods to maintain a low decoding complexity. Simulations demonstrate that our differential schemes using the proposed low complexity decoders provide full transmit diversity. We also present additional differential decoding schemes that achieve full diversity, perform significantly better than the existing schemes, and work for any square OSTBC, but need higher decoding complexity. The proposed schemes work for any number of receive antennas. Moreover, we analyze the diversity of the proposed schemes. To the best of our knowledge, our low complexity schemes are the first low complexity differential schemes for multi-user MIMO systems.

Interference alignment (IA) was shown to be effective for interference management in improving transmission rate in terms of the degree of freedom (DoF) gain. On the other hand, orthogonal space-time block codes were widely used in point-to-point multiantenna channels to enhance transmission reliability in terms of the diversity gain. In this paper, we connect these two ideas, i.e., IA and space-time block coding, to improve the designs of alignment precoders for multiuser networks. Specifically, we consider the use of Alamouti codes for IA because of their rate-one transmission and achievability of full diversity in point-to-point systems. The Alamouti codes protect the desired link by introducing orthogonality between the two symbols in one Alamouti codeword, and create alignment at the interfering receiver. We show that the proposed alignment methods can maintain the maximum DoF gain and improve the ergodic mutual information in the long-term regime, while increasing the diversity gain to 2 in the short-term regime. The presented examples of interference networks have two antennas at each node and include the two-user X channel, the interfering multiaccess channel, and the interfering broadcast channel.

We explore the feasibility of linear interference alignment using finite signaling dimensions and symbol extensions. In the non-zero total intersection regime, we show that the number of sources is upperbounded by a function of channel diversity. The available diversity places a fundamental constraint on the number of signal spaces that can overlap at one destination (where they are undesired) while maintaining the resolvability of a subset of those signals (desired signals) at another destination. Specifically, the number of such signal spaces cannot be larger than the channel diversity. This is the diversity constraint that we identify in this paper. Not only is the proposed method applicable for X channels, interference channels, and their rank-deficient counterparts, it can also be used for both circular symmetric signaling (CSS) and asymmetric complex signaling (ACS) over a combination of frequency and MIMO channels with arbitrary symbol extensions.

Multi-user detection is an efficient approach proposed to boost the spectral efficiency of a wireless communication system. While multi-user detection in synchronous systems or in flat fading environments has been successfully addressed, it is still an open and challenging problem in the practical case of asynchronous MIMO systems employing space-frequency (time) block coding and operating in frequency selective environments. In this paper, we show how the concept of multi-user detection can be efficiently extended to the latter case with a low complexity overhead and a small performance loss compared to the synchronous case.

In this paper, we present UAV-aided Cross-Layer Routing Protocol (UCLR) that aims at improving the routing performance of a ground MANET network with aid from an Unmanned Aerial Vehicle (UAV). The UAV is added to a connected backbone formed by a collection of designated nodes in order to combat link failures and wireless link effects detected at PHY/MAC layers before the routing table adapts to the changes. In the context of the UCLR protocol, we introduce a UAV-aided cross-layer routing scheme, an associated cross-layer routing metric, and a UAV load-balancing algorithm. We implement UCLR using Linux Quagga routing suite along with OSPF MANET Designated Routing (MDR) and demonstrate its performance improvements compared to the original MDR through emulation studies.

Aside from the introduction of many new schemes, the use of TCP-based AQM schemes and in specific RED is anticipated to continue in foreseeable future as the de-facto standard of network congestion control. Therefore, conducting extra research work aiming at improving the performance of RED is still a topic of high interest. In this paper, we present an analytical study aiming at the fine tuning of the RED parameters. Utilizing a statistical analysis approach, we formulate an optimization problem aimed at addressing the loss and delay tradeoff of the RED queuing discipline. We provide a two-phase iterative solution to the problem in order to identify the settings of the RED parameters. We discuss the convergence characteristics of our solution and investigate its low complexity characteristics. Through extensive NS2 experiments, we illustrate the advantages of our proposed optimization approach by comparing its results to those of adaptive RED as well as standard RED with recommended parameter settings.

We determine necessary conditions on the structure of symbol error rate (SER) optimal compact quantizer codebooks for limited feedback beamforming in wireless networks with one transmitter-receiver pair andRparallel amplify-and-forward relays. We call a codebook “small” if its cardinality is less thanR, and “large” otherwise. A “d-codebook” depends on the power constraints and can be optimized accordingly, while an “i-codebook” remains fixed. It was previously shown that any i-codebook that contains the single-relay selection (SRS) codebook achieves the full-diversity order,R. We prove the following: Every full-diversity i-codebook contains the SRS codebook, and thus is necessarily large. In general, as the power constraints grow to infinity, the limit of an SER-optimal large d-codebook contains an SRS codebook, provided that it exists. For small codebooks, the maximal diversity is equal to the codebook cardinality. Every diversity-optimal small i-codebook is an orthogonal multiple-relay selection (OMRS) codebook. Moreover, the limit of an SER-optimal small d-codebook is an OMRS codebook. We observe that SRS is nothing but a special case of OMRS for codebooks with cardinality equal to R. As a result, we call OMRS as "the universal necessary condition" for codebook optimality. Finally, we confirm our analytical findings through simulations.

We study fixed data rate communication schemes for wireless relay-interference networks with any number of transmitters, relays, and receivers. The transmitters and the relays have individual short-term power constraints. We analyze both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies with a two channel use quantized network beamforming protocol. We design the quantizer of the channel state information to minimize the probability that at least one receiver incorrectly decodes its desired symbol(s). Correspondingly, we introduce a generalized diversity measure that encapsulates the conventional one as the first-order diversity. Additionally, it incorporates the second-order diversity, which is concerned with the transmitter power dependent logarithmic terms that appear in the error rate expression. We first show that for AF relays, the maximal achievable diversity in the presence of interference is strictly less than the transmit diversity bound in terms of the second-order diversity. We then prove that it is possible to achieve the transmit diversity bound using DF relays as if there is no interference and as if coding over an arbitrary number of channel uses is allowed. Relay selection provides the best possible diversity gain for both relaying strategies. Finally, we show that all the aforementioned diversity gains can be achieved using distributed decision making with asymptotically zero feedback rate per receiver. Such a performance is made possible by a special distributed quantizer design method we have called localization .

This paper considers a scenario for multi-user multi-input multi-output (MIMO) communication systems when perfect channel state information at the transmitter (CSIT) is given while the equivalent channel state information at the receiver (CSIR) is not available. Such an assumption is valid for the downlink multi-user MIMO systems with linear precoders that depend on channels to all receivers. We propose a concept called dual systems with zero-forcing design based on the duality principle, originally proposed to relate the Gaussian multi-access channel (MAC) and the Gaussian broadcast channel (BC). For the K-user N×2 MIMO BC with N antennas at the transmitter and two antennas at each of the K receivers, we design a downlink interference cancellation (IC) transmission scheme by obtaining the dual of uplink MAC systems employing IC methods. The transmitter simultaneously sends K precoded Alamouti codes, one for each user. Each receiver zero-forces the unintended user's Alamouti codes and decouples its own data streams using two simple linear operations independent of CSIR. Analysis shows that the proposed scheme achieves a diversity gain of 2(N - K + 1) for equal energy constellations with short-term power and rate constraints. Power allocation among K users can also be performed to improve the array gain but not the diversity gain. Numerical results demonstrate that the bit error rate of the downlink IC scheme has a diversity gain improvement compared to the block diagonalization (BD) method, which requires global channel information at each node.

The use of multiple packet reception (MPR) can alleviate congestion in multi-hop networks and improve network Goodput. However, a sophisticated design is required in order to enable MPR in such networks considering the heterogeneity of transiting packets, both in size and arrival times. In this paper, we introduce a cross-layer MPR-PHY/MAC design suitable for multi-hop ad-hoc networks. Our design identifies three phases of operation tied to the network offered load. Simulation results show that the saturation Goodput of a network using our proposed MPR-PHY/MAC design is better than that of a network using standard IEEE 802.11 PHY and MAC by a factor of at least 50%.

Alamouti codes have been widely recognized for their full-diversity performance, while they provide a symbol rate of one with symbol-by-symbol decoding complexity. To decode an Alamouti code, the receiver requires the channel state information (CSI), while the transmitter operates blindly without CSI. This paper investigates the dual scenario when CSI is available only at the transmitter and not at the receiver. Such an assumption is valid for time-division duplex (TDD) systems where the transmitter obtains the CSI from the reciprocal channel and the receiver does not want to estimate the channel because of complexity. We propose dual Alamouti codes that do not require CSI at the receiver and exploit transmit CSI to achieve the performance of full-diversity with rate-one and symbol-by-symbol decoding complexity. Moreover, the dual codes provide the same bit error rate (BER) performance as the original Alamouti codes for equal-energy constellations. The dual Alamouti codes have potential applications in TDD systems to save the cost of training and simplify receiver design.

Relying on physical layer security is an attractive alternative of utilizing cryptographic algorithms at upper layers of protocol stack for secure communications. In this paper, we consider a two-hop wireless relay network in the presence of an eavesdropper. Our scenario of interest spans over a four-node network model including a source, a destination, a trusted relay, and an untrusted eavesdropper in which the relay forwards the source message in a decode-and-forward (DF) fashion. The source and relay are allowed to use some of their available power to transmit jamming signals in order to create interference at the eavesdropper. The relay and destination are assumed to have the knowledge of the jamming signals. An important question is how to allocate the transmission power of the message signal and that of the jamming signal. First, we propose an optimal power allocation solution in which the knowledge of global channel state information (CSI) is required. To facilitate practical system design, two simple yet sub-optimal power allocation solutions are proposed which do not rely on eavesdropper's channels. For the purpose of performance comparisons, power allocation problems for two benchmark schemes without jamming are also analyzed.

During wireless video transmission, when an error is detected at the receiver, the packet is dropped immediately and error concealment is performed. However, this procedure does not always provide satisfactory results, especially when the motion is complicated or scene change occurs. Moreover, there are often uncorrupted macroblocks in the distorted packet. Hence, it can be important to utilize the information in the distorted packet. In this work, our aim is to explore which produces better quality: discarding or decoding the corrupted packet. First, we examine the effect of an error in each syntax element on the visual quality. Second, we compare the visual degradation caused by an error in each syntax element with that caused by discarding the whole packet. We also evaluate the effects of motion and quantization.

We determine necessary conditions on the structure of symbol error rate optimal quantizers for limited feedback beamforming in wireless networks with one transmitter-receiver pair and R parallel amplify-and-forward relays. It was previously shown that any quantizer codebook that contains the single-relay selection (SRS) codebook achieves full-diversity. We prove the converse: Every full-diversity codebook contains the SRS codebook. We also determine the structure and the achievable diversity of codebooks with cardinality less than R.

Interference alignment increases transmission rate in terms of multiplexing gain for X channel. In this paper, we propose a fixed-rate transmission scheme over a two-user X channel where each of the two double-antenna transmitters has independent messages for each of the two double-antenna receivers. Each transmitter encodes symbols using Alamouti codes followed by beamformers that align interference at unintended receivers. The receiver removes the aligned interference and decouples symbols using interference cancellation followed by symbol-by-symbol decoding. Our analysis shows that the proposed scheme achieves a diversity gain of 2 at the maximum node-to-node symbol-rate of 2 over 3.

In this paper, we consider distributed estimation of an unknown random vector by using wireless sensors and a fusion center (FC). We adopt a linear model for distributed estimation of a vector source where both observation models and sensor operations are linear and the multiple access channel (MAC) is coherent. The sensors are designed to minimize the mean square error (MSE) at the fusion center without considering the noise at the fusion center. Subsequently, a filter is designed to cancel out the effect of the noise at the fusion center. We present a closed form solution. When the number of unknown parameters increases, an approximate closed form solution is provided that can be implemented distributively. Since there is no power constraint imposed on transmit power of each sensor, we investigate the average transmit power of each sensor. We show that as the number of unknown parameters increases, the sensor power is inversely proportional to the number of unknown parameters of interest. Finally, simulations are provided to verify the analysis and present the performance of the proposed scheme.

In multiple access channels, when users know each other's channels, precoders can be designed utilizing channel information to cancel the interference at the receiver without sacrificing the diversity or the complexity of the system. Recently, it was shown that when there are only two users, a receiver can completely cancel the interference of the two users and provide full diversity for each user. Unfortunately, the scheme only works for two users. In this paper, we extend the scheme to more than 2 users. In other words, we propose a system to achieve interference cancellation and full diversity with low complexity for any number of users. Then, we extend the results to any number of users with any number of transmit and receive antennas. Our main idea is to design precoders, using the channel information, to make it possible for different users to transmit over orthogonal directions. Then, using the orthogonality of the transmitted signals, the receiver can separate them and decode the signals independently. We also analytically prove that our system provides full diversity to each user. Simulation results confirm our analytical proof and show that our proposed scheme outperforms other existing interference cancellation schemes.

The use of multiple-packet reception (MPR) in wireless networks is known to improve throughput especially in high-traffic conditions. The lack of synchronization among the nodes in random access systems introduces significant challenges toward the adoption of MPR in the PHY and the MAC design for systems using MPR. In this paper, we propose an asynchronous MPR method for the PHY and also design a compatible random access MAC for wireless local area networks (WLANs). Relying on space-time coding techniques, our MPR method detects multiple asynchronous packets while providing diversity and low bit error rates at the PHY layer. Extending the design of IEEE 802.11, our MPR MAC design encourages simultaneous packet transmissions and handles multiple packet receptions. Simulation results show that the throughput of a WLAN significantly improves in many scenarios of operation using our proposed PHY/MAC MPR framework.

Coping with collisions is one of the biggest challenges in the design of MAC algorithms for wireless networks. Recent advances in MIMO communications have provided the possibility of simple detection of colliding packets. In this paper, we introduce a new MIMO/MPR-aware cross-layer MAC/PHY design that is capable of combating collisions through the use of a multiple packet reception technique. Analytical and simulation results show that the proposed MAC design can considerably improve the throughput of a WLAN operating over lossy links.

This letter considers two-relay networks with Rician fading channels. It is assumed that the receiver has full channel information while the relays know the channel means and covariances only. To optimize network performance, we combine distributed space-time coding (DSTC) with relay power allocation. For the high signal-to-noise ratio (SNR) regime, we analytically find the relay power allocation that minimizes an upper bound on the pairwise error probability (PEP). Simulation shows that the proposed scheme largely improves network reliability. In some cases, lack of power allocation causes diversity loss.

When two users transmit signals to a common receiver, one can design precoders to cancel the interference for each user, if each user knows all the channel information perfectly. Also the diversity for each user is full. However, in practice, perfect channel information is not available. In this paper, we design precoders for two users with two transmit antennas and one receiver with two receive antennas using quantized feedback. We propose to construct codebook using Grassmannian line packing. By choosing precoders from the codebook properly, our proposed scheme can cancel the interference for each user. Also we analytically prove that our system can achieve full diversity for each user. Then we extend our scheme to any number of transmit and receive antennas. Simulation results confirm our analytical proof and show that our scheme can serve as a bridge between a system with no feedback and a system with perfect feedback.

We study multi-user transmission and detection schemes for a multi-access relay network (MARN) with linear constraints at all nodes. In a (J, Ja, Ra, M) MARN, J sources, each equipped with Jaantennas, communicate to one M-antenna destination through one Ra-antenna relay. A new protocol called IC-Relay-TDMA is proposed which takes two phases. During the first phase, symbols of different sources are transmitted concurrently to the relay. At the relay, interference cancellation (IC) techniques, previously proposed for systems with direct transmission, are applied to decouple the information of different sources without decoding. During the second phase, symbols of different sources are forwarded to the destination in a time division multi-access (TDMA) fashion. At the destination, the maximum-likelihood (ML) decoding is performed source-by-source. The protocol of IC-Relay-TDMA requires the number of relay antennas no less than the number of sources, i.e., Ra≥ J. Through outage analysis, the achievable diversity gain of the proposed scheme is shown to be min {Ja(Ra- J + 1), RaM}. When M ≤ Ja(1- J-1/Ra), the proposed scheme achieves the maximum interference-free (int-free) diversity gain RaM. Since concurrent transmission is allowed during the first phase, compared to full TDMA transmission, the proposed scheme achieves the same diversity, but with a higher symbol rate.

We introduce a systematic distributed quantizer design method, called {\it{localization}}, in which, out of an existing centralized (global) quantizer, one synthesizes the distributed (local) quantizer using high-rate scalar quantization combined with entropy coding. The general localization procedure is presented, along with a practical application to a quantized beamforming problem for multiple-input multiple-output broadcast channels. For our particular application, not only localization provides high performance distributed quantizers with very low feedback rates, but also reveals an interesting property of finite rate feedback schemes that might be of theoretical interest: For single-user multiple-input single-output systems, one can achieve the performance of almost any quantized beamforming scheme with an arbitrarily low feedback rate, when the transmitter power is sufficiently large.

When two users transmit signals to a common receiver, one can design precoders to cancel the interference for each user if each user knows all the channel information perfectly. Also the diversity for each user is full. However, in practice, perfect channel information is not available. In this paper, we design precoders for two users with two transmit antennas and one receiver with two receive antennas using quantized feedback. We propose to construct a codebook using Grassmannian line packing. By choosing precoders from the codebook properly, our proposed scheme can cancel the interference and achieve full diversity for each user. Simulation results confirm our analytical proof.

This paper considers multi-access relay networks (MARNs) where two single-antenna users communicate to one N-antenna receiver via two hops of transmissions through one R-antenna relay. Nodes in the network are under two linear constraints. The relay linearly maps its received signal to generate its forwarding signal without decoding; the receiver has linear decoding complexity in the number of users. Since the relay-receiver link has more transmission paths than the link from each user to the relay, a two-step protocol, called TDMA-ICRec, is proposed. In the first step, both users timeshare the user-relay link. Linear combining is then performed at the relay to maximize the signal-to-noise-ratio (SNR) for each user. In the second step, the relay forwards both users' symbols concurrently to the receiver to enhance the transmission rate. At the receiver, an interference cancellation (IC) technique is used to decouple the users and ML decoding is conducted to recover each user's symbols. Two network scenarios are studied when the relay has two antennas and four antennas. Through analysis and simulation, when the receiver has more than two antennas, TDMA-ICRec achieves the same diversity as the full-TDMA-DSTC interference-free (int-free) scheme, yet with higher symbol rate.

Coping with collisions is one of the biggest challenges in the design of MAC algorithms for wireless networks. Recent advances in MIMO communications have provided the possibility of decoding colliding packets. In this paper, we introduce a new MIMO cross-layer MAC/PHY design that is capable of combating collisions through the use of a multiple packet detection technique. Analytical and simulation results show that the proposed MAC design can considerably improve the throughput of a WLAN operating over lossy links.

In multiple access channels, when users know each other channels, precoders can be designed utilizing channel information to cancel the interference at the receiver without sacrificing the diversity or the complexity of the system. In a recent work, it was shown that when there are only two users, a receiver can completely cancel the interference and provide full diversity for each user. Unfortunately, the scheme only works for two users. In this paper, we extend the scheme to 4 users. In other words, we propose a system to achieve interference cancellation and full diversity with low complexity for 4 users. We also provide simulation results that confirm our analysis.

This paper is on interference cancellation (IC) schemes for a two-user relay network where users are allowed to communicate simultaneously. The considered networks have one double-antenna half-duplex relay, single-antenna receiver but three scenarios on both users: single-antenna, double-antenna, and four-antenna. We apply the IC scheme, which was originally proposed for multi-antenna multi-user direct communication systems, to multi-user relay networks and propose a protocol called IC-Relay-TDMA, in which the relay cancels user interference and then amplifies and forwards the interference-free (int-free) user information to the receiver in TDMA. The maximum likelihood (ML) decoding at the receiver can be conducted symbol by symbol for both networks with single-antenna users and double-antenna users. Compared to the full TDMA scheme in which each user is allocated different time slots from end to end to avoid interference, IC-Relay-TDMA achieves the same diversity with a higher symbol rate when both users have two or four antennas.

We analytically model the distortion of a scalable video bitstream containing a Base Layer (BL) and one or more Enhancement Layers (ELs). Utilizing our distortion model, we propose a pair of low complexity Unequal Error Protection (UEP) methods for transmitting BL and EL bitstreams over a wireless channel. Our one dimensional Forward Error Correction (FEC) UEP methods protect each bitstream against both temporally correlated bit errors caused by fading and packet erasures caused by network buffering. Our methods use optimal symbol interleaving to combat tandem loss effects. We illustrate the performance advantage of our methods over a baseline Equal Error Protection (EEP) and a number of UEP methods for different available budgets and channel conditions.

This work investigates high-SNR outage behavior of slow fading channels with quantized feedback and partial power control, when feedback indices are error-prone. We propose a quantizer structure with continuous Voronoi regions and derive its optimality conditions using probability analysis. Our design involves construction of a novel bit-mapping scheme. The optimized power control codebook resembles channel optimized scalar quantizers (COSQs). Also, the diversity gain of the communication system under study is characterized with both nonzero and vanishing feedback error probabilities.

In this letter, we propose two full-rate space-time-frequency trellis code (STFTC) designs for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The first proposal based on rotating constellation is called extended super-orthogonal STFTC (Ex-SOSTFTC). The second proposal, called Quasi-Orthogonal STFTCs (QOSTFTCs), combines set partitioning and the structure of quasi-orthogonal space-frequency designs in a systematic way. In addition to spatial diversity, the proposed codes provide multipath diversity and achieve high-coding gain over a frequency selective fading channel. Simulation results show the proposed codes significantly outperform the existing STFTC designs.

Two rate-one differential quasi-orthogonal space-frequency trellis codes (DQOSFTCs) for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channels are proposed. The DQOSFTCs are systematically constructed within an OFDM symbol period, by combining unitary quasi-orthogonal trellis codes and differential modulation over the frequency domain (DF-QOSFTC) or time domain (DT-QOSFTCs). Besides multipath diversity, our DQOSFTCs achieve high-coding gain and simple decoding when the channel state information is not available at both the transmitter and the receiver. Simulation results show that our proposed codes significantly outperform the existing differential space-frequency trellis codes.

We introduce an analytical expression for the expected distortion of a single layer encoded video bit-stream. Based on the expected distortion model, we propose a distortion optimal Unequal Error Protection (UEP) technique to transmit such bit-stream over a wireless tandem channel. The proposed method allocates the total transmission budget unequally to different frames of a video bit-stream in order to protect the bit-stream against both bit errors caused by fading and packet erasures caused by network buffering. We compare this technique with another UEP technique as well as a one-dimension equal length protection technique. The evaluation results for different choices of packet sizes, available budgets, and channel conditions show that the proposed method outperforms the other alternative schemes.

This paper is on quantized beamforming in wireless amplify-and-forward relay interference networks with multiple transmitter-receiver pairs. We design the quantizer of the feedback information specifically to optimize the union bound on the bit error rate performance. Two different quantization schemes are considered. First, using a global quantizer structure, we analytically show that a simple feedback scheme based on relay selection can achieve full diversity. Then, we design a local quantization scheme with distributed quantizer encoders, one at each receiver. We show that, with only a few feedback bits, high diversity gains can be obtained with the local quantizer structure as well. Simulations are also provided, confirming our analytical results. We observe that our designs guarantee an equal high diversity gain for each transmitter-receiver pair.

The tradeoff between diversity, interference alignment and rate for a K user multiple-antenna interference network is analyzed. It is assumed that the sources employ a space-time code in combination with linear preceding, while the receiving nodes use linear detectors. We show that interference alignment is needed if the system is operating at or close to the maximum achievable rate. For low rates the preferred strategy is to utilize all antennas in order to achieve high diversity gains, rather than using some of the antennas to align the interference. For the case of K = 3 users, an exact characterization of the tradeoff is provided. We also investigate the impact of channel estimation errors on the diversity of the system. It turns out that small channel estimation errors can be tolerated, while larger errors reduce the diversity gain significantly.

This paper considers the interference cancellation (IC) problem in multi-user wireless relay networks. First, it is shown that using distributed space-time coding (DSTC), the multiple antenna IC scheme previously proposed for systems with direct transmissions can be applied to relay networks. The ML decoding after full IC can be performed symbol by symbol. Then, by allowing IC at relays, a new degree of freedom in relay network design is discovered. With this new idea, the required number of antennas at the receiver for full IC can be reduced and a balance between diversity and delay can be obtained.

We consider interference cancellation for a system with two users when users know each other channels. The goal is to utilize channel information to cancel the interference without sacrificing the diversity or the complexity of the system. Before, in the literature, it was shown how a receiver with 2 receive antennas can completely cancel the interference of two users and provide a diversity of 2 for users with 2 transmit antennas. We propose a system to achieve the maximum possible diversity of 4 with low complexity. Our main idea is to design pre-coders, using the channel information, to make it possible for different users to transmit over orthogonal spaces. Then, using the orthogonality of the transmitted signals, the receiver can separate them and decode the signals independently. We analytically prove that the system provides full diversity to both users. In addition, we provide simulation results that confirm our analytical proof.

MIMO multi-hop channel plays an important role in wireless ad hoc networks. In this paper, we investigate the resource allocation optimization problem in order to achieve throughput maximization with given resource constraints. We propose algorithms with low complexity to achieve maximum capacity. The main idea is to determine the rank of the optimal transmit covariance matrix and the optimal power allocation of each node separately using our low-complexity algorithm. Further, we reduce the complexity of our algorithm by adding another pre-processing algorithm. Using our algorithms, we find that while dynamical allocation of time and power could increase channel capacity, equal time and power allocation among different nodes may not cause much capacity loss.

We design a conceptual transmission scheme that adjusts rate and power of data codewords to send them over a slowly fading channel, when quantized and possibly erroneous channel state information (CSI) is available at the transmitter. The goal is to maximize the data throughput or the expected data rate using a multi-layer superposition coding technique and temporal power control at the transmitter. The main challenge here is to design a CSI quantizer structure for a noisy feedback link. This structure resembles conventional joint source and channel coding schemes, however, with a newly introduced quasi-gray bit-mapping. Our results show that with proper CSI quantizer design, even erroneous feedback can provide performance gains. Also, with an unreliable feedback link, superposition coding provides significant gains when feedback channel is poorly conditioned and channel uncertainty at the transmitter is severe, whereas power control is more effective with more reliable feedback.

The goal of combining beamforming and spacetime coding is to obtain full-diversity order and to provide additional received power (array gain) compared to conventional space-time codes. In this work, a class of code constellations is proposed, called generalized partly orthogonal designs (PODs) and both high-rate and low-rate feedback information is incorporated with possible feedback errors. A binary symmetric channel (BSC) model characterizes feedback errors. Two cases are studied: first, when the BSC bit error probability is known a priori to the transmission ends, and second, when it is not known exactly. Based on a minimum pairwise error probability (PEP) design criterion, we design a channel optimized vector quantizer (COVQ) for feedback information and a precoder matrix codebook to adjust the transmission codewords. The attractive property of our combining scheme is that it converges to conventional space-time coding with low-rate and erroneous feedback and to directional beamforming with high-rate and error-free feedback. This scheme also shows desirable robustness against feedback channel modeling mismatch.

This paper deals with beamforming in wireless relay networks with perfect channel information at the relays, receiver, and transmitter if there is a direct link between the transmitter and receiver. It is assumed that every node in the network has its own power constraint. A two-step amplify-and-forward protocol is used, in which the transmitter and relays not only use match filters to form a beam at the receiver but also adaptively adjust their transmit powers according to the channel strength information. For networks with no direct link, an algorithm is proposed to analytically find the exact solution with linear (in network size) complexity. It is shown that the transmitter should always use its maximal power while the optimal power of a relay ca.n take any value between zero and its maxima. Also, this value depends on the quality of all other channels in addition to the relay's own. Despite this coupling fact, distributive strategies are proposed in which, with the aid of a low-rate receiver broadcast, a relay needs only its own channel information to implement the optimal power control. Then, beamforming in networks with a direct link is considered. When the direct link exists during the first step only, the optimal power control is the same as that of networks with no direct link. For networks with a direct link during the second step only and both steps, recursive numerical algorithms are proposed. Simulation shows that network beamforming achieves the maximal diversity order and outperforms other existing schemes.

Reconfigurable antennas offer a promising solution to the problem of space limitation in high-performance wireless communication over handset devices. In this paper, we propose a novel transmission scheme capable of extracting the maximum diversity gains of a Multiple-Input Multiple-Output (MIMO) communication system employing reconfigurable antennas at the transmitter, the receiver or at both ends of the wireless link, without the need for feedback. For a MIMO communication system using reconfigurable antennas at the transmitter, we propose a state-switching scheme in combination with an appropriate three-dimensional block code in order to extract the maximum possible level of diversity. When reconfigurable antennas are placed at the receiver end, we propose a state-selection scheme and prove that the proposed scheme is capable of achieving maximum diversity gains. Moreover, the state-selection method is able to obtain selection gain in addition to diversity gain. As shown, both state-switching and state-selection schemes can be combined together when reconfigurable antennas are used at both ends of the wireless link. Furthermore, we discuss and quantify the effects of switching delay on the performance of both state-switching and state-selection schemes. Finally, we provide simulation results to validate the performance of the proposed schemes. To demonstrate the gains of our schemes in realistic propagation conditions, we also conduct simulations using an actual reconfigurable antenna known as the PIXEL antenna.

This paper is on relay selection schemes for wireless relay networks. First, we derive the diversity of many single-relay selection schemes in the literature. Then, we generalize the idea of relay selection by allowing more than one relay to cooperate. The SNR-optimal multiple relay selection scheme can be achieved by exhaustive search, whose complexity increases exponentially in the network size. To reduce the complexity, several SNR-suboptimal multiple relay selection schemes are proposed, whose complexity is linear in the number of relays. They are proved to achieve full diversity. Simulation shows that they perform much better than the corresponding single relay selection methods and very close to the SNR-optimal multiple relay selection scheme. In addition, for large networks, these multiple relay selection schemes require the same amount of feedback bits from the receiver as single relay selection schemes.

We identify an analytical expression for the distortion of a scalable video bitstream. Relying on the distortion expression, we propose a low complexity distortion-optimal unequal error protection (UEP) method for the transmission of such video bitstream over wireless tandem channels. Utilizing a one-dimensional forward error correction (FEC) coding scheme, our proposed transmission method protects the bitstream against both bit errors caused by fading and packet erasures caused by network buffering. Our coding scheme also leverages symbol interleaving to better cope with the temporally correlated loss observed over the tandem channels of interest. We evaluate the performance of our proposed scheme by comparing its results against those of UEP product codes as well as those of optimized version of equal error protection (EEP). For a variety of choices of fixed packet sizes, available budgets, and channel conditions, we illustrate the performance advantage of our scheme over other schemes. Our experiments also show that our scheme works more efficiently for channels with a higher probability of bit errors than those with a lower probability of bit errors.

In this paper, we propose a novel Multiple-Input Multiple-Output (MIMO) wireless communication system employing reconfigurable PIXEL antennas at the receiver. We design a Space-Time-State Block Coding scheme for the PIXEL-based reconfigurable MIMO system and propose optimal and ad hoc state-selection algorithms that achieve maximum diversity gains. Moreover, we prove that in state-selection schemes, contrary to antenna selection, there is no benefit in selecting more than one state. Furthermore, we discuss the gains of state-selection over the state-switching scheme proposed in [1]. Finally, we evaluate the theoretical findings through simulations. These simulations are conducted using a realistic channel model, that takes into account the radio-electric characteristics (radiation pattern, gain, efficiency, etc.) of the PIXEL antennas.

We consider the problem of joint routing, scheduling and power control in multi-hop wireless networks. We use a linear relation between link capacity and signal to interference noise ratio in our formulation. In a previous work, using a duality approach, the optimal link scheduling and power control that minimizes the total average transmission power is found. We formulate this problem as a linear programming problem with exponential number of constraints. To cope with the exponential number of constraints, we propose an iterative algorithm based on the cutting plane method. The separation oracle for the cutting plane algorithm turns out to be an element-wise concave optimization problem that can be effectively solved using branch and bound algorithm. We extend the same method to find the optimal routing scheduling and power control. Simulation results show that this methodology is more efficient and scalable compare to the previously proposed algorithm.

This paper is on beamforming in networks whose relays know the channel means and covariances. The question we answer is: To optimize the network performance, how much power should each relay use? Instead of the widely used aggregate relay power constraint, we use the more practical assumption that each relay has a separate power constraint. We generalize the distributed space-time coding scheme so that each relay can adapt its transmit power according to the channel information. For two-relay networks, we analytically solve the relay power control problem at high transmit powers using the pairwise error probability (PEP) minimization. Simulation shows that appropriate relay power control can largely improve the reliability, especially when the qualities of relay paths are far apart.

Achieving minimal loss while satisfying an acceptable delay profile remains to be an open problem under the RED queuing discipline. In this paper, we present a framework targeted at optimal fine tuning of the RED parameters in order to address such problem. For a given traffic pattern and utilizing a statistical analysis of finite-state Markov chains, we formulate an optimization problem aimed at addressing the loss and delay tradeoff of the RED queuing discipline. Our two-step iterative solution to the problem identifies the optimal settings of the RED parameters. We prove the convergence of our solution and investigate its low complexity characteristics. We apply our framework to a number of generic queuing and TCP scenarios in order to capture loss and delay performance of our algorithms versus buffer capacity and service rate. Based on our results, we argue that our model is capable of optimally addressing the loss-delay tradeoff of RED queues accommodating time-varying traffic profiles.

In this paper, we present an open-loop reconfigurable multiple-input multiple-output system, employing ORIOL antennas at both ends of the communication system. The ORIOL is a compact dual-polarized reconfigurable antenna introduced in [1]. First, we provide a realistic model for our communication system based on measurements conducted using ORIOL antennas. Then, we analytically discuss the performance limits of the practical system and derive the maximum achievable diversity gains. Furthermore, we design a specific space-time block code that is capable of achieving such maximum diversity gains, without any feedback from the receiver. Finally, we evaluate the theoretical findings through measurements and simulations.

This paper is on quantized beamforming in wireless amplify-and-forward (AF) relay networks. We use the generalized Lloyd algorithm (GLA) to design the quantizer of the feedback information and specifically to optimize the bit error rate (BER) performance of the system. Achievable bounds for different performance measures are derived. First, we analytically show that a simple feedback scheme based on relay selection can achieve full diversity. Unlike the previous diversity analysis on the relay selection scheme, our analysis is not aided by any approximations or modified forwarding schemes. Then, for highrate feedback, we find an upper bound on the average signalto- noise ratio (SNR) loss. Using this result, we demonstrate that both the average SNR loss and the capacity loss decay at least exponentially with the number of feedback bits. In addition, we provide approximate upper and lower bounds on the BER, which can be calculated numerically.We observe that our designs can achieve both full diversity as well as high array gain with only a moderate number of feedback bits. Simulations also show that our approximate BER is a reliable estimation on the actual BER. We also generalize our analytical results to asynchronous networks, where perfect carrier level synchronization is not available among the relays.

In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.

We consider layered transmission of a successively refinable source over a quasi-static fading channel. We establish a duality relationship between this problem and that of packet transmission over erasure channels and use it to share solution techniques in both domains. For a Gaussian source and the fading channel, a low-complexity, optimal algorithm is proposed, and it is shown that the corresponding dual for packet erasure channels has a linear complexity as opposed to the quadratic complexity of the best known optimal algorithms in the literature. For non-Gaussian sources, the optimal rate allocation problem for fading channels is solved using the dual solution for erasure channels. It is also shown that a single-layer system is optimal for fading channels if the goal is to maximize the rate. Numerical results for multiple antenna Rayleigh fading channels are presented for Gaussian sources and practical image coders. It is shown that a few number of layers significantly improves the performance. Finally, we numerically show that for practical operating conditions, optimizing the asymptotic measure of distortion exponent is not enough when there are more than one transmit or receive antennas.

Distributed space-time coding is a cooperative transmission scheme for wireless relay networks. With this scheme, antennas of the distributive relays work as transmit antennas of the sender and generate a space-time code at the receiver. It achieves the maximum diversity. Although the scheme needs no channel information at relays, it does require full channel information, both the channels from the transmitter to relays and the channels from relays to the receiver, at the receiver. In this paper, we propose a differential transmission scheme, which requires channel information at neither relays nor the receiver, for wireless relay networks. As distributed space-time coding can be seen as the counterpart of space-time coding in the network setting, this scheme is the counterpart of differential space-time coding. Compared to coherent distributed space-time coding, the differential scheme is 3dB worse. In addition, we show that Alamouti, square real orthogonal, and Sp(2) codes can be used differentially in networks with corresponding numbers of relays. We also propose distributed differential space-time codes that work for networks with any number of relays using circulant matrices.

We consider J transmitter units each equipped with N transmit antennas over wireless Rayleigh fading channels. Previously in [1], it was proved that when each transmitter unit has TV transmit antennas, using (J - 1)N + r receive antennas for any r ges 1, the receiver can completely separate the signals of J users. The provided diversity to each user was shown to be Nr if the units employ space-time trellis codes even if the units transmit asynchronously. Here, we consider the case when all units are synchronized and employ quasi-orthogonal space-time block codes (N > 2). It is proved that in this case a receiver with J + r - 1 antennas with r ges 1 can separate the transmitted signals of all units and provide each unit with a diversity order of Nr. Based on our interference cancellation technique, we then offer an array processing scheme which provides trade-off between diversity and spatial multiplexing. It is shown via simulations that this array processing scheme performs better than well-known modulation schemes, e.g. space-time block codes and BLAST, for a moderate number of receive antennas.

We combine space-time coding and transmit beamforming over multiple-antenna quasi-static fading channels using limited-rate channel state information at the transmitter (CSIT). The combining is performed using a class of constellation sets inspired from orthogonal designs and directional beamforming or in general, unitary preceding with quantized feedback. This constellation class is called partly orthogonal designs (PODs). The conventional way of combining coding and quantized feedback beamforming is through precoded space-time block codes (PSTBCs) that provide full-diversity order with all system configurations in terms of the number of feedback regions and the number of transmit antennas. PODs maintain the same advantage. However, they require less decoding and quantization complexity compared to PSTBCs. We develop POD structures, analyze their performance, and optimize them based on pairwise error probability analysis. PODs create a larger combining space compared to PSTBCs. We can show that with certain system configurations, PODs outperform PSTBCs. Using PODs, we also propose a combined coding, beamforming, and spatial multiplexing scheme over multiple-antenna multi-user channels that enables a low-complexity joint interference cancellation scheme.

In this paper, we propose a novel class of Space-Frequency and Space-Time-Frequency block codes based on Quasi-Orthogonal designs, over a frequency selective Rayleigh fading channel. The proposed Space-Frequency code is able to achieve rate-one and full space and multipath diversity gains available in the MIMO-OFDM channel. As simulation results demonstrate, the code outperforms the existing Space-Frequency block codes in terms of bit error rate performance. By coding across the three dimension of space, time and frequency, we propose a Quasi-Orthogonal Space-Time-Frequency code that is capable of achieving rate-one and exploiting all of the spatial, multipath and temporal diversity gains offered by the channel. In case of a channel which is quasi-static over adjacent OFDM symbol durations, we propose a Space-Time-Frequency code that benefits from a reduced maximum likelihood decoding complexity.

We present a theoretical analysis on the gains offered by a reconfigurable multiple-input multiple-output (MIMO) system using orthogonal space-time block codes (OSTBCs) and reconfigurable antennas at the receiver only. The proposed system using reconfigurable antennas selects the optimal radiation state of the receive antennas in which the receive signal-to-noise ratio (SNR) is maximized. The theoretical findings show that under certain channel propagation conditions and using an appropriate codification and power allocation of the transmitted signal, the diversity order of the proposed reconfigurable MIMO system is given by the product of transmit and receive antennas as well as the number of reconfigurable radiation states of the receive antennas. The impact of correlated radiation states and imperfect channel estimation are also considered. The geometrical and physical constraints of reconfigurable antennas are also taken into account through the concept of antenna group. Assuming an ideally scattered non-line-of-sight (NLOS) flat Rayleigh channel, the antenna design criteria is given. Finally, we evaluate the theoretical findings through simulations on the array gain, bit error rate and ergodic capacity, where the detrimental impact of channel estimation is observed.

We study the problem of maximizing the expected rate over a slowly fading channel with quantized channel state information at the transmitter (CSIT). This problem has been recently studied in the literature assuming a noiseless feedback link. In this work, we consider a more realistic model, where the feedback link suffers from fading, as well as the limited power allocated to the feedback signals. Our scheme considers a finite-state model to capture the fading in the feedback link. We solve the rate maximization problem with different power control strategies at the transmitter. A channel optimized sealer quantizer (COSQ) is designed to incorporate feedback in our transmission scheme. Unlike the conventional COSQs where the objective is to reconstruct the source, our proposed quantizer is designed to optimize the expected rate of the forward link. For a high quality feedback channel, the proposed system performs close to the noiseless feedback case, while its performance converges to the no-feedback scenario as the feedback channel quality degrades

The problem of transmitting an analog source over a quasi-static fading channel is considered. This problem is motivated by the recent demand for multimedia content over wireless channels. The goal is to minimize the expected distortion of the received signal. An efficient numerical technique is proposed to explicitly solve the rate and power allocation problems at a finite SNR. The proposed algorithm iteratively optimizes the rates for a given power allocation and vice versa, and is guaranteed to converge

In this paper, we propose an analytical model to capture the dynamics of the RED algorithm. We first develop a system of recursive equations that describes the packet dropping behavior of the RED algorithm. Using a notion from the theory of random walks, we then derive an exact-closed form expression that characterizes the loss characteristics of a RED queue. We validate the derived formula by a numerical comparison with the recursive equations.

We investigate the outage behavior of multiple-antenna slowly fading channels with resolution constrained feedback and partial power control. A fixed-rate communication scheme is considered. It is known from the literature that with error-free feedback, the power control codebook that minimizes the outage probability with an average power constraint at the transmitter has a circular quantizer structure. Moreover, the diversity gain of the system increases polynomially with the cardinality of the power control codebook. Here, we study a similar system, but within a noisy feedback channel framework. We show that the optimal quantizer structure in this scenario is still circular. Furthermore, the optimal quantization codebook resembles a channel optimized scalar quantizer (COSQ). With noisy feedback, using the new power control codebook, the outage performance of the system is superior to that of a no-feedback system. However, we show through asymptotic analysis that the diversity gain is the same as a no-feedback scheme.

This paper is on beamforming in wireless relay networks with perfect channel information at the receiver and relays. It is assumed that every node in the network has its own power constraint. An amplify-and-forward protocol is used. Relays use not only the channel direction information to form a beam at the receiver but also the channel strength information to adaptively adjust their transmit powers. Our results show that the optimal power used at a relay is not a binary function. It can take any value between zero and its maximal transmit power. Also, surprisingly, this value depends on the quality of all other channels in addition to the relay's own channels.

We consider the transmission of a Gaussian source over a single-input multiple-output (SIMO) quasi-static fading channel. The goal is to minimize the expected distortion of the reconstructed signal at the receiver. We consider a delay-limited scenario where channel coding is restricted to a single realization of the channel. Channel state information (CSI) is assumed to be known perfectly at the receiver, and a zero-delay, noiseless, fixed-rate feedback link provides a quantized version of the CSI to the transmitter. An upper bound on the performance is derived and it is shown that for practical values of the channel signal to noise ratio (SNR), this bound can be achieved with a very limited knowledge of the channel quality. We show that unlike the rate maximization problems, temporal power adaptation at the transmitter provides significant gains, and the amount of the gain heavily depends on the bandwidth expansion ratio. For asymptotically high SNRs, we derive the distortion exponent of the system, defined as the slope of the expected distortion with respect to the channel SNR. We show that the distortion exponent of limited feedback is equivalent to that of superposition coding without feedback, so long as the number of quantization levels in the feedback scheme is equal to the number of the layers in the superposition coding scheme. For the finite-SNR regime, we propose an optimal and efficient numerical technique to design the feedback scheme. Numerical results for a Rayleigh fading channel are also presented.

In this paper, the authors derive an improved sub-optimal differential detector for space-time block codes based on the maximum-likelihood detection when neither the transmitter nor the receiver has the knowledge of the channel. The proposed detection method is general enough to be applied to orthogonal space-time block codes, quasi-orthogonal space-time block codes and quasi-orthogonal space-time block codes with minimum decoding complexity. Compared with the existing sub-optimal schemes, the improved scheme can provide better error performance with acceptably increased decoding complexity. Simulation results show that the performance of the proposed detector is close to that of the maximum-likelihood detector without the knowledge of the received SNR. In addition, the performance improvement over the existing schemes becomes more significant as the transmission rate increases.

We consider layered transmission of a Gaussian source over a quasi-static fading channel. A broadcast strategy is used in which multiple layers of source data are superimposed, and each layer is allocated a different power and transmission rate. For a multiple-antenna system where either the transmitter or the receiver has a single antenna, we propose a low-complexity algorithm for minimizing the expected end-to-end distortion of the received signal. Numerical results for a Rayleigh fading channel are presented, and the performance gain over a time-sharing strategy is quantified. It is numerically shown that for a wide range of operating conditions, equal rate allocation is as good as unequal rate allocation.

Distributed space–time coding was proposed to achieve cooperative diversity in wireless relay networks without channel information at the relays. Using this scheme, antennas of the distributive relays work as transmit antennas of the sender and generate a space–time code at the receiver. It achieves the maximal diversity when the transmit power is infinitely large. This paper is on the design of practical distributed space–time codes (DSTCs). We use orthogonal and quasi-orthogonal designs which are originally used in the design of space–time codes for multiple-antenna systems. It is well known that orthogonal space–time codes have full diversity and linear decoding complexity. They are particularly suitable for transmissions in the network setting using distributed space–time coding since their "scale-free" property leads to good performance. Our simulations show that they achieve lower error rates than the random code. We also compare distributed space–time coding to selection decode-and-forward using the same orthogonal designs. Simulations show that distributed space–time coding achieves higher diversity than selection decode-and-forward (DF) when there is more than one relay. We also generalize the distributed space–time coding scheme to wireless relay networks with channel information at the relays. Although our analysis and simulations show that there is no improvement in the diversity, in some networks, having channel information at the relays saves both the transmission power and the transmission time.

Super-orthogonal space-time trellis codes (SOSTTCs) designed by hand can significantly improve the performance of space-time trellis codes. This paper introduces a new representation of SOSTTCs based on a generator matrix that allows a systematic and exhaustive search of all possible codes. This will verify that some of the known codes are optimal, and provides a means to easily implement encoders and decoders with a large number of states without relying on a graphical representation. New codes with up to 256 states that outperform previously known codes are presented

In this paper, we focus on the design and evaluation of new transmit beamforming algorithms for the multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) systems. The OFDM technique is very effective in combating frequency selective fading since it converts the broadband channel into several parallel flat fading channels. However, to implement transmit beamforming, the channel state information on each individual subcarrier has to be conveyed from the receiver to transmitter. In order to reduce the channel feedback requirement in the OFDM system, we take the time and frequency domain correlations of the channel fading into consideration. Based on our successive beamforming (SBF) technique, we develop several classes of feedback algorithms for the OFDM systems. These new algorithms use the knowledge from the previous frame or neighboring subcarrier to aid the beamforming codebook design for the current subcarrier. Through numerical simulations, we demonstrate that the proposed SBF algorithms require very little channel feedback, yet they provide better performance than that of the other existing OFDM beamformers

In this paper, we propose punctured super-orthogonal space-time trellis codes. First, considering a system with two transmit antennas, we present the basic design criteria for the punctured super-orthogonal codes. Then, staged decoding, a sub-optimal decoding technique used in single-input single-output systems, is extended to the proposed punctured codes. To reduce the complexity of staged decoding, we propose two complementary design rules of the punctured super-orthogonal codes. After that, we discuss the constraints under which the punctured codes can be designed without metric distortion. Compared with the conventional codes, the punctured super-orthogonal codes can provide lower decoding complexity. Also, they allow an easy implementation of various spectral efficiencies with a single encoder/decoder. Furthermore, the puncturing scheme can be generalized to the case of more than two transmit antennas. In addition, we implement differential modulation schemes based on the punctured codes. Such implementations do not require channel estimation at the transmitter and the receiver. Simulation results demonstrate that the performance of the punctured super-orthogonal space-time trellis codes is close to that of the corresponding conventional codes in the literature.

Combining the benefits of diversity/coding gain from space-time coding and array gain from beamforming over quasi-static block fading channels is accomplished in a suitable way for erroneous feedback conditions. For this purpose, we utilize multi-dimensional space-time constellation sets or vector codes combined with linear precoders which are designed to minimize the error probability. Furthermore, the design of a feedback link based on maximum received signal-to-noise ratio criterion is considered. In practice, the transmitter channel state information is degraded by several factors in the feedback link, e.g. bandwidth limitation, delay, and noise which is the main problem. The defective information at the transmitter leads to drastic performance degradation unless the system is robustly designed against the feedback errors and mismatches. Therefore, we employ quantized feedback strategies optimized for noisy channels. The most attractive property of our scheme is that it converges to space-time coding in extremely poor feedback cases and to pure beamforming in relatively high quality feedback situations. Simulation results confirm the good performance of our scheme.

Distributed space-time coding was proposed to achieve cooperative diversity in wireless relay networks, in which antennas of relays work as transmit antennas of the sender and generate a space-time code at the receiver in a distributive way. It was proved that this scheme achieves the maximum diversity R when the total transmit power is infinitely large, where R is the number of relays in the network. This paper is on the design of practical distributed space-time codes for wireless relay networks. We use orthogonal and quasi-orthogonal designs which are originally used in the design of space-time codes for multiple-antenna systems. It is well known that orthogonal space- time codes have full diversity and linear decoding complexity. They are particularly suitable for transmissions in the network setting using distributed space-time coding since their 'scale-free' property leads to good performance. Our simulations show that they achieve lower error rates than the random code. We also compare the performance of distributed space-time coding to that of selection decode-and-forward using the same orthogonal designs. Simulations show that distributed space-time coding achieves higher diversity than selection decode-and-forward when there are more than one relay node.

In this paper, we propose a novel class of Space-Frequency and Space-Time-Frequency block codes based on Quasi-Orthogonal designs, over a frequency selective Rayleigh fading channel. The proposed codes are able to achieve rate one and full diversity by exploiting the space and multipath diversity gains available in the MIMO-OFDM channel. As the simulation results show, our codes outperform the existing Space-Frequency block codes in terms of bit error rate performance. Additionally, the proposed Space-Time-Frequency code benefits from a reduced maximum likelihood decoding complexity, which is a huge simplification compared to the existing codes. We also discuss the conditions under which the maximum likelihood decoding for our proposed Quasi-Orthogonal Space-Frequency code is simplified as well.

We study the effects of physical layer parameters on the connectivity of fading ad-hoc networks. Relying on a symbol error rate connectivity metric for such networks, we assume a pair of nodes are connected if their bi-directional measure of connectivity satisfies a given threshold. We investigate the effects of three parameters on the connectivity phenomenon. First and assuming the nodes are distributed over a fixed finite area, we study the effects of the changes in nodes' density. Next, we measure the connectivity effects of the interference coefficients, i.e., the portion of power an interfering node contributes to other links. Finally, we experiment with the threshold of link quality for connectedness. For each parameter, we also provide an intuitive explanation of the phenomena observed in our experiments. Our simulation results show that (1) depending on the value of interference coefficient, an increase in node density may increase or decrease, and (2) increasing the interference coefficients and thresholds of link quality will decrease the connectivity of fading ad-hoc networks.

Transmit beamforming has been widely adopted for wireless systems with multiple transmit antennas. For an independent block fading channel, the Grassmannian beamformer has been shown to provide very good performance using limited amount of feedback. However, the original Grassmannian beamformer does not take the time domain correlation of the channel fading into consideration. In this work, based on a first order auto-regressive (AR1) dynamic fading model, we develop two new classes of beamforming algorithms that exploit the interframe correlations in the channel fading. The first algorithm is based on a predictive vector quantization (PVQ) approach, and the resulting PVQ beamformer accomplishes very good SNR performance. In addition, to simplify the implementation complexity, we also develop a successive beamforming (SBF) algorithm. The new SBF scheme uses the knowledge of the previous fading blocks to aid the beamforming codebook design of the current fading block. Through numerical simulations, we demonstrate that the proposed PVQ beamformer and successive beamformer outperform several other previously proposed beamformers at various fading scenarios.

In this paper, we focus on the design and evaluation of new transmit beamforming algorithms for the multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) systems. In the MISO-OFDM systems, out transmit beamforming requires a huge amount of channel feedback, this is because the channel state information on each subcarrier has to be conveyed from the receiver to transmitter. In order to reduce the channel feedback requirement in the OFDM system, we take the time and frequency domain correlations of the channel fading into consideration. Based on our successive beamforming (SBF) technique, we develop several classes of feedback algorithms for the OFDM systems. These new algorithms use the knowledge from the previous frame or neighboring subcarrier to aid the beamforming codebook design for the current subcarrier. Through numerical simulations, we demonstrate that the proposed algorithms require a small amount of channel feedback, yet they outperform the other existing finite rate OFDM beamformers.

We propose a simple, low-complexity coding scheme for the transmission of a packetized progressive bitstream. We consider tandem channels introducing correlated bit errors and packet erasures. We show that our proposed technique transforms the hybrid channel into a channel with a single impairment for which various optimization techniques exists. Numerical results show that our proposed scheme outperforms existing solutions over the useful operating regions of the channel.

We consider layered transmission of a successively refinable complex Gaussian source over a quasi-static fading channel. For a given number of source coding layers, we propose an efficient algorithm to calculate the optimal rate assignment for each layer, as well as the optimal size of each layer. The optimality of the algorithm is proved and numerical results for a multiple antenna Rayleigh fading channel are presented. It is numerically shown that a small number of layers is usually sufficient to achieve most of the layering gain

We consider J transmitter units each equipped with N transmit antennas over a wireless Rayleigh fading channel. Previously, it was proved that when each transmitter unit has N transmit antennas, using JN receive antennas, the receiver can completely separate the signals of J users. The provided diversity to each user was shown to be N. Here, we consider the case when all units are synchronized and employ quasi-orthogonal space-time block codes with N = 4 transmit antennas. It is proved that in this case a receiver with J receive antennas can separate the transmitted signals of all units and provide each unit with the same diversity order of N = 4

In this paper, we first introduce a quasi-orthogonal space-time block coding scheme over multiple antenna block fading channels. The proposed code exploits temporal diversity due to the mobile nature of the block fading channel as well as spatial diversity due to multiple antennas. We then propose a beamforming strategy over temporally correlated channel blocks. It is assumed that the channel undergoes Doppler frequency shift and an AR1channel model is adopted to track the channel temporal correlation coefficients at the transmitter. Using the combination of transmit beamforming and quasi-orthogonal block coding over adjacent channel blocks, we accomplish to achieve the available array gains on top of notable diversity gains and coding gains, compared to the standard solutions in the conventional wireless communication schemes. In our model, the knowledge of the channel statistics at the transmitter side is based on the approximation of the Jakes' model and does not require frequent feedback updates from the receiver. Simulation results show the superiority of our scheme to the existing coding schemes with knowledge of the channel statistics at the transmitter

In this article, we introduce a novel group of space-time-frequency codes based on multiple trellis coded modulation (MTCM). These codes are originally designed without accounting for the channel spectral and temporal correlation conditions. We improve the code performance by utilizing an augmented distance spectrum enhancement technique to serve for the wireless channels that undergo mobility and multi path fading. The performance of the new group of codes, with enhanced distance spectrum characteristics is investigated though numerical simulations

In this paper, we propose super-pseudo-orthogonal space-time trellis codes. Compared with super-orthogonal space-time trellis codes, the proposed codes provide lower decoding complexity, lower peak-to-average power ratio and better performance. Furthermore, unlike super-quasi-orthogonal space-time trellis codes, it is easy to implement a differential modulation scheme based on the proposed codes when the channel state information is not available at the transmitter and the receiver. Simulation results demonstrate the good performance of our codes

In this paper, we propose punctured super-orthogonal space-time trellis codes. We present the basic design criteria and the constraints under which the designed punctured codes can be decoded without performance loss. Compared with the conventional super-orthogonal space-time trellis codes, such punctured codes can provide simpler decoding and allow an easy implementation of various spectral efficiencies with a single encoder/decoder. Our simulation results demonstrate that the performance of the proposed punctured super-orthogonal space-time trellis codes for two transmit antennas is close to that of the corresponding conventional codes in the literature. The general idea is extended to more than two antennas

Space-time coding (STC) has been proposed recently for multiple-antenna wireless communication systems. Most of the proposed STC schemes use the assumption that either no channel-state information, or the channel mean/covariance information, is available at the transmitter. In this paper, we propose a new STC scheme for a closed-loop transmission system, where quantized channel-phase information is available at the transmitter. A new performance criterion is derived for the quasi-static fading channel. This design criterion is then used to construct a new class of space-time trellis codes (STTCs). The proposed code construction is based on the concatenation of a standard multiple trellis-coded modulation outer code with an inner code. The inner code is selected from a series of inner codes using the channel-phase feedback. The series of inner codes are constructed based on the systematic set partitioning of several classes of space-time signal designs. Simulation results show significant performance improvement over the other STTCs in the literature. In addition, the proposed coding scheme enjoys low peak-to-average-power ratio, simple decoding, and power-efficient low-cost implementation

We consider progressive transmission over a hybrid channel introducing bit errors and packet erasures. The existing solutions are analyzed and extended to the case of a channel that exhibits memory on both bit errors and packet erasures. We then propose a simple, low-complexity coding scheme that transforms the hybrid channel into a channel with a single impairment for which various optimization techniques exist. Both rate-based and distortion-based optimization problems are investigated. It is shown that our proposed solution has lower channel coding and rate-distortion optimization complexities compared to the known solutions. Simulation results for channels with and without memory show the effectiveness of our proposed solution over a wide range of operating conditions. Numerical results also indicate that the rate-based solution of our proposed algorithm is very close to the corresponding distortion-based solution

We study the problem of real-time error control in layered and replicated media systems. We formulate an optimization problem aimed at minimizing a cost metric defined over the wasted bandwidth of redundancy in such systems. We also provide an analytical solution to the problem in the context of layered media multicast control (LMMC) protocol. In doing so, we present closed-form expressions describing the temporally correlated loss pattern of communication networks. Utilizing our closed-form expressions, we rely on an a priori estimate of loss along with a hybrid proactive FEC-ARQ scheme to statistically guarantee the QoS for the receivers of a media system. We show the effectiveness of our protocol by means of simulating realistic error control scenarios

In this paper, we present two differential trellis coded space-time modulation schemes when the channel state information is not available at the transmitter and at the receiver. First, we extend the design of super-orthogonal space-time trellis codes to improve their performance. Then, based on these extended super-orthogonal codes, we propose the first differential scheme. Compared with the trellis coded differential unitary space-time modulation scheme, not only does the proposed differential scheme achieve full diversity, but also it provides identical or higher coding gain and much lower decoding complexity. In order to achieve further improved coding gain in addition to full diversity for more than two transmit antennas, we propose a second differential scheme which is based on super-quasi-orthogonal codes. Such a differential scheme outperforms the differential modulation scheme based on super-orthogonal codes with simpler decoding. Simulation results demonstrate the good performance of our schemes

In this paper, we present a novel power loading scheme for several existing space-time trellis codes (STTCs). These STTCs are originally designed for the open loop systems where no channel state information is available at the transmitter. To further enhance the performance of these STTCs, we assume that the scalar quantized channel magnitude information is available at the transmitter through a feedback channel. Therefore, the error performance of these STTCs is improved through proper power loading on the different transmit antennas. To calculate the power loading parameters, we derive the distance spectrum of these STTCs based on the channel magnitude information. We also use a novel transfer function analysis to optimize the overall distance spectrum. The proposed power loading algorithm is flexible enough to be applied for various kinds of existing STTCs and it also enjoys easy implementation at the transmitter. Finally, numerical simulations show that the new power loading scheme accomplishes superior performance compared to several widely used open loop algorithms as well as some popular closed loop algorithms in the literature.

We study the problem of connectivity in MIMO fading ad-hoc networks. Based on a probabilistic analysis of achievable capacity on individual links of a random topology, we introduce a novel connectivity metric for wireless ad-hoc networks. We assume a pair of nodes are connected if their bi-directional capacity is more than a given threshold. Our metric is more sophisticated compared to previously proposed metrics of connectivity as it captures the effects of time-varying fading channel, power, and multiple antennas. Our results show that employing mobile nodes with multiple antennas enhances the connectivity of fading wireless ad-hoc networks

The extrinsic information transfer (EXIT) charts are used to analyze the sum-product decoding algorithm of low-density parity-check (LDPC) codes. The codes are optimized by performing curve fitting on EXIT charts. This technique is developed for binary erasure channels based on area property and is extended to other channels. We show that on channels where the area property is not valid, the curve fitting approach is a sub-optimal method of optimizing for the best convergence threshold. On Gaussian channels, the messages from variable nodes to check nodes are approximated by a symmetric Gaussian distribution. We prove that the check node detector mutual information transfer function derived using this approximation is an upper bound. So the EXIT chart analysis predicts an optimistic value for the convergence threshold. We give an empirical lower bound for the check node detector mutual information transfer function. For few irregular LDPC codes designed for AWGN channel using density evolution, we have examined the accuracy of convergence thresholds predicted by EXIT charts using these bounds for the check node transfer function. The analysis of EXIT chart technique presented in this paper can be extended to other channels for which the distribution of channel messages can be approximated by a symmetric Gaussian/Gaussian mixture distribution.

Many space-time transmission schemes have been proposed recently to exploit the high channel capacity from the MIMO communication systems. However, algorithms for a large antenna array (LAA) remain an open research problem. The standard channel estimation and space-time modulation schemes are not practical for LAAs due to high implementation complexity. In this paper, we propose a novel transmit beamforming scheme based on a reconfigurable LAA. Compared to the traditional schemes, our new scheme requires a much smaller number of radio frequency (RF) chains, yet it still enjoys the high performance offered from the LAA. Based on a reconfigurable LAA, we present a pilot assisted round robin channel estimation algorithm. With this estimation algorithm, the tracking of the fading channel is significantly simplified at the mobile station. We then introduce an adaptive transmit beamforming algorithm based on relative channel phase feedback at the transmitter. For a time varying Rayleigh fading channel, we investigate the system SNR as a function of the latency of the round robin channel estimation and derive the optimal training duration for given mobile speed. Finally, numerical simulations show that the proposed scheme accomplishes superior performance for various kinds of time varying Rayleigh fading channels. Overall, the proposed system enjoys good performance, high flexibility, low cost as well as low power consumption at both the base station and mobile station.

Space-time coding has been proposed recently for MIMO wireless communication systems. Most of these space-time coding schemes use the assumption that either no channel state information, or the channel mean/covariance information, is available at the transmitter. We propose a new space-time coding scheme for a close-loop transmission system, where quantized channel phase information is available at the transmitter. A new performance criterion is derived for the quasi-static fading channel. This design criterion is then used to construct a new class of space-time trellis codes. The proposed code construction is based on the concatenation of a standard M-TCM outer code with an inner code. The inner code is selected from a series of inner codes using the channel phase feedback. The series of inner codes is constructed based on the systematic set partitioning of two classes of space-time signal designs. Simulation results show significant improvement over other space-time trellis codes. In addition, the proposed coding scheme enjoys low peak-to-average power ratio, simple decoding, and easy implementation without complicated eigenanalysis.

In this paper, first, we extend the design of super-orthogonal space-time trellis codes to improve their performance. Then, we present a differential space-time trellis coded modulation scheme based on these extended super-orthogonal codes when the channel state information is not available at the transmitter and the receiver. We compare our differential modulation scheme with the trellis coded differential unitary space-time modulation scheme and note that not only does the proposed differential trellis coded modulation scheme achieve full diversity, but also it provides identical or higher coding gain and much lower decoding complexity for a given transmission rate. Simulation results demonstrate the good performance of our proposed scheme.

We propose a novel distortion minimization technique for the transmission of a packetized progressive bitstream. We consider tandem channels introducing bit errors and packet erasures. We formalize the distortion minimization problem as a constrained optimization problem and propose an algorithm that optimally allocates the available budget between the bit error and packet loss protection components. We show that our proposed optimization technique is robust and has a linear complexity in the transmission rate. Numerical results show the effectiveness of the proposed algorithm.

We propose a novel distortion minimization technique for the transmission of a packetized progressive bitstream. The optimality of our proposed algorithm is analytically proved for a class of sources satisfying a stated condition. It is shown that Gauss-Markov sources belong to the latter class for which the algorithm is optimal. We show that our proposed optimization technique is robust and has a linear complexity in the transmission rate. Simulation results show the effectiveness of our proposed algorithm.

The objective of layering techniques of distributing multimedia traffic over multicast IP networks is to effectively cope with the challenges in continuous media applications. The challenges include heterogeneity, fairness, real-time constraints, and quality of service. We study the problem of rate allocation and receiver partitioning in layered and replicated media systems. We formulate an optimization problem aimed at maximizing a close approximation of the so-called max-min fairness metric subject to loss and bandwidth constraints. Our optimal layered media multicast control (LMMC) solution to the problem analytically determines the layer rates and the corresponding partitioning of the receivers. Our simulation results show the effectiveness of our proposed solution in realistic scenarios.

We introduce a new family of space-time trellis codes that extends the powerful characteristics of super-orthogonal space-time trellis codes to four transmit antennas. We consider a family of quasi-orthogonal space-time block codes as building blocks in our new trellis codes. These codes combine set partitioning and a super set of quasi-orthogonal space-time block codes in a systematic way to provide full diversity and improved coding gain. The result is a powerful code that provides full rate, full diversity, and high coding gain. It is also possible to maintain a tradeoff between coding gain and rate. Simulation results demonstrate the good performance of our new super-quasi-orthogonal space-time trellis codes.

This paper proposes a differential modulation scheme based on quasi-orthogonal space-time block codes (STBCs) when neither the transmitter nor the receiver has knowledge of the channel. A system with four transmit antennas is considered and divided into two subsystems. Each subsystem is equivalent to a system with two transmit antennas and uses differential STBC modulation. Based on the encoding and decoding for the two subsystems, the overall encoding and decoding algorithm for the original system is derived. We also compare the performance of our differential detection with that of the corresponding coherent detection in terms of achieved signal-to-noise ratio (SNR) for a given transmission power and propose a quantitative measure to estimate the performance difference between the coherent quasi-orthogonal code and our differential detection. Furthermore, we show that our differential modulation scheme can be extended to a system with more than four transmit antennas. Compared with the existing differential modulation schemes for four and eight transmit antennas, the proposed scheme has a lower bit-error rate (BER) and provides full diversity and pair-wise decoding complexity.

We present a statistical optimization framework for solving the end-to-end problem of progressive transmission of images over noisy channels. We consider the impacts of transmission bit errors as well as packet erasures. To cope with the impact of random bit errors, we formulate an optimization problem aimed at minimizing the end-to-end expected distortion of a reconstructed image subject to rate and efficiency constraints. In order to eliminate the impact of packet erasures, we propose utilizing an algorithm that is capable of statistically guaranteeing the delivery of a packet set associated with the progressive bitstream of an image source. Using receiver feedback, our framework is capable of effectively coping with the channel loss effects characterized by the Gilbert-Elliott model.

The moment-generating function (MGF)-based method, which has previously been applied with great success in evaluating the error probability performance of more traditional space-time block and trellis codes, is equally useful when applied to systems employing super-quasi-orthogonal space-time trellis codes. Its utility in exactly evaluating the pairwise error probability has been demonstrated for both fast and slow Rayleigh fading channels with the results obtainable either in closed form or as a single integral with finite limits and an integrand composed solely of trigonometric functions.

In this paper, we present an analytical solution to the problem of rate allocation and receiver partitioning in layered media systems. The framework of our proposed protocol layered media multicast control (LMMC) determines the layer rates and the corresponding partitioning of the receivers maximizing a mathematically well-behaved approximation of the so-called max-min fairness metric.

This paper presents the framework of tripartite LDPC codes and more than a single coded bit affects its channel outputs. Symbol-nodes along with variable-nodes and check-nodes of the graph of an LDPC codes is used to represent a third set of nodes in channel output. Thus the concentration theorem is considered in this framework, which optimizes the average performance of the codes and the design criterion is meaningful for a randomly chosen sample from the ensemble of LDPC codes.

Addressing the tradeoff between the QoS and consumed power is a critical issue for wireless ad-hoc networks. The loss observed in such networks is often temporally correlated. This paper examines an optimal scheme to maximize the aggregate data rate of wireless ad-hoc networks under the power and loss constraints. In order to properly model temporally correlated loss observed in a fading wireless channel, we propose the use of finite-state Markov chains. Details of fading statistics of SIR, an important indicator of transmission quality, are presented. We also analyze the impacts of enforcing power, block-loss probabilities, and data rates constraints.

In this paper, we propose a differential modulation scheme bused on quasi-orthogonal space-time block codes when neither the transmitter nor the receiver has the knowledge of the channel. We consider a system with four transmit antennas and divide it into two subsystems. Each subsystem contains two transmit antennas and uses a differential space-time block code modulation. Based on the encoding and decoding for the two subsystems, we derive the overall encoding and decoding algorithm for the original system. Compared with the existing differential modulation schemes for four transmit antennas, our scheme has lower bit error rate and provides pair-wise decoding complexity.

We present analytical and numerical solutions to the problem of power control in wireless media systems with multiple antennas. We formulate a set of optimization problems aimed at minimizing total power consumption of wireless media systems subject to a given level of QoS and an available bit rate. Our formulation takes into consideration the power consumption related to source coding, channel coding, and transmission of multiple-transmit antennas. In our study, we consider Gauss-Markov and video source models, Rayleigh fading channels along with the Bernoulli/Gilbert-Elliott loss models, and space-time block codes.

We propose a new class of space-time trellis codes for 4 transmit antennas. These codes combine set-partitioning and a super set of quasi-orthogonal space-time block codes in a systematic way to provide full diversity and improved coding gain. The result is a very powerful non full-rate code that provides full diversity and high coding gain for different number of states and at different rates. Also, it is possible to maintain a tradeoff between coding gain and rate. The decoding complexity of the new codes is very low because the symbols can be decoupled at the decoder. Simulation results demonstrate the good performance of our new space-time trellis codes. We provide a study of coding gain and different code examples.

In this research article, we present an analytical solution to the problem of power control in wireless media systems with multiple transmit antennas. We formulate an optimization problem aimed at minimizing total power consumption of wireless media systems subject to a given level of quality of service (QoS) and an available bit rate. Our formulation takes into consideration the power consumption related to source coding, channel coding, and transmission of multiple transmit antennas relying on the H.263 source coding standard, Rayleigh fading channels along with the Bernoulli loss model, and space-time block codes respectively.

Recently, G. Jongren et al., (see IEEE Trans. Inform. Theory, vol.48, p.611-27, 2002) and S. Zhou and G.B. Giannakis (see IEEE Trans. Sig. Process., vol.50, no.10, p.2599-613, 2002) proposed a new design criterion and algorithm that combine space-time block coding with linear beamforming. This method is very successful when it is applied to the orthogonal space-time block code (STBC). However, the same method cannot be directly applied to the more advanced quasi-orthogonal STBCs, due to the non-orthogonal nature of the quasi-orthogonal designs. Based on the same design criterion, we consider the quasi-orthogonal codes as beamforming candidates. Through asymptotic analysis, we dramatically simplify the design criterion and construct the beamforming matrix for the quasi-orthogonal designs. The proposed quasi-orthogonal STBC beamformer can be presented as a novel full rate eigen-beamformer that works for any system with four or more transmit antennas. Simulation results for a system with channel mean feedback demonstrate that the new beamforming scheme has significant gains over the orthogonal STBC based beamforming schemes.

We extend the method based on the moment generating function (MGF), previously applied to analyzing the performance of orthogonal space-time block and trellis codes, to super-orthogonal codes that combine set-partitioning with a super set of orthogonal space-time block codes in such a way as to provide full diversity with increased rate and improved coding gain. It is shown that the maximum-likelihood (ML) metric and expressions for the pairwise error probability (PEP), previously developed for the Alamouti code combined with multidimensional trellis-coded modulation (TCM), can be readily extended to the super-orthogonal case.

We present an analytical study for the Gilbert loss model describing temporally correlated loss observed in the Internet and other communication systems. We obtain closed-form solutions describing the transient and steady-state behavior of the model. Additionally, starting from recursive equations of the Gilbert loss model, we derive closed-form solutions for arrival and loss patterns of the systems governed by the model. We show that utilizing our model yields a lower complexity compared to existing recursive models, attracting special attention for use in many different wired and wireless networking applications relying on such a model.

We introduce a new family of space-time trellis codes which extends the powerful characteristics of super-orthogonal space-time trellis codes to four transmit antennas. We consider a family of quasi-orthogonal space-time block codes as building blocks in our new trellis codes. These codes combine set-partitioning and a super set of quasi-orthogonal space-time block codes in a systematic way to provide full diversity and improved coding gain. The result is a very powerful code that provides full rate, full diversity, and high coding gain. Simulation results demonstrate the good performance of our new super-quasi-orthogonal space-time trellis codes.

In this paper, we present an optimal solution to the problem of error control in layered and replicated media systems satisfying real-time delay constraints. In doing so, we rely on an a priori estimate of loss along with a hybrid proactive FEC-ARQ scheme to statistically guarantee the quality of service for receivers. Our optimal layered media multicast control (LMMC) solution to a formulation of the error control problem analytically determines the redundancy assignment of individual groups associated with a layered media system minimizing a cost metric defined over wasted bandwidth of redundancy.

We introduce a new class of space-time codes called super-orthogonal space-time trellis codes. These codes combine set partitioning and a super set of orthogonal space-time block codes in a systematic way to provide full diversity and improved coding gain over earlier space-time trellis code constructions. We also study the optimality of our set partitioning and provide coding gain analysis. Codes operating at different rates, up to the highest theoretically possible rate, for different number of states can be designed by using our optimal set partitioning. Super-orthogonal space-time trellis codes can provide a tradeoff between rate and coding gain. Simulation results show more than 2-dB improvements over the codes presented in the literature while providing a systematic design methodology.

We introduce a new class of space-time codes called super-orthogonal space-time trellis codes. These codes combine set partitioning and a super set of orthogonal space-time block codes in a systematic way to provide full diversity and improved coding gain over earlier space-time trellis code constructions.

We propose multiple description (MD) video coders which use motion-compensated predictions. Our MD video coders utilize MD transform coding and three separate prediction paths at the encoder to mimic the three possible scenarios at the decoder: both descriptions received or either of the single descriptions received. We provide three different algorithms to control the mismatch between the prediction loops at the encoder and decoder. We present simulation results comparing the three approaches to two standards-based approaches to MD video coding. We show that when the main prediction loop at the encoder uses a two-channel reconstruction, it is important to have side prediction loops and transmit some redundancy information to control mismatch. We also examine the performance of our MD video coder with partial mismatch control in the presence of random packet loss, and demonstrate a significant improvement compared to more traditional approaches.

We consider a simple multiple description (MD) video coder, that uses redundancy-rate-distortion criteria to split a one-layer stream generated by a standard video coder into two correlated streams. Our simulation results demonstrate that this MD coder has much better performance for large redundancies than our previous MDTC video coder, although it cannot perform as well at low redundancies. This MD video coder is very simple to implement and is compatible with H.263 to the extent that each description can be decoded by a standard H.263 decoder. This MD coder was used in a previous study on the transport of MD and layered video over an EGPRS wireless network, where the fact that it creates two streams with very balanced rates was a strong advantage.

It has been shown that a complex orthogonal design that provides full diversity and full transmission rate for a space-time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space-time block codes with full diversity and a high transmission rate. We design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.

We explicitly construct multiple transmit antenna differential encoding/decoding schemes based on generalized orthogonal designs. These constructions generalize the two transmit antenna differential detection scheme that we proposed before (Tarokh and Jafarkhani 2000).

It has been shown that a complex orthogonal design which provides full diversity and full transmission rate for a space-time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space-time block codes with full diversity and a high transmission rate. In this work, we design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.

We present a transmission scheme for exploiting diversity given by two transmit antennas when neither the transmitter nor the receiver has access to channel state information. The new detection scheme can use equal energy constellations and encoding is simple. At the receiver, decoding is achieved with low decoding complexity. The transmission provides full spatial diversity and requires no channel state side information at the receiver. The scheme can be considered as the extension of differential detection schemes to two transmit antennas.

We study the design of channel-optimized vector quantizers in the presence of channel mismatch. We show that when the statistics of the channel bit-error rate (BER) are not known, a minimax solution is the one obtained by designing for the worst possible channel. Then, we consider the case when the probability density function of channel BER is known and propose an algorithm that provides a minimum average distortion. Also, by using an estimate of the channel BER at the decoder, we develop a decoder-adaptive scheme that further improves the performance. In all cases, we have limited ourselves to table-lookup decoders, which amount to very small computational complexities. Finally, the utilization of lookup tables at the encoder and the effects of imperfect estimation of channel BERs are considered.

For any code C defined over an equal energy constellation, it is first shown that at any time instance, the problem of determining codewords of C with high peak-to-average power ratios (PAPR) in a multicarrier communication system is intimately related to the problem of minimum-distance decoding of C. Subsequently, a method is proposed for computing the PAPR by minimum-distance decoding of C at many points of time. Moreover an upper bound on the error between this computed value and the true one is derived. Analogous results are established for codes defined over arbitrary signal constellations. As an application of this computational method, an approach for reducing the PAPR of C proposed by Jones and Wilkinson (1996) is revisited. This approach is based on introducing a specific phase shift to each coordinate of all the codewords where phase shifts are independent of the codewords and known both to the transmitter and the receiver. We optimize the phase shifts offline by applying our method for computing the PAPR for the coding scenario proposed by the ETSI BRAN Standardization Committee. Reductions of order 4.5 dB can be freely obtained using the computed phase shifts. Examples are provided showing that most of the gain is preserved when the computed optimal phase shifts are rounded to quantenary phase-shift keying (PSK), 8-PSK, and 16-PSK type phase shifts.

In this correspondence, we propose a channel-matched hierarchical table-lookup vector quantizer for a discrete memoryless channel. We show that this scheme simultaneously provides low encoding complexity and robustness against transmission noise. Additionally, we propose the use of lookup tables for transcoding in heterogeneous networks. We show, through an example motivated by an application in multicasting, that the proposed table-lookup transcoding approach is an elegant alternative to the expensive and delay-introducing method of transcoding achieved by decoding and re-encoding.

The authors note a few misprints exist in the final version of the above-named paper [ibid., vol. 45, pp. 1456–1467, July 1999]. In Definitions 3.4.1, 4.1.1, and 5.4.1, also in the last line of the proof of Theorem 3.5.2.

We explore the ability of multiple description (MD) source coders to achieve good performance on channels other than ideal MD channels. We examine both the overall system design and compare the performance of a system with MD source coder to that of a more traditional system using a layered source coder. For the memoryless channels we consider, MD source coding cannot achieve acceptable performance for a memoryless Gaussian source without appropriate channel coding. Also, in memoryless channels, a system with MD source coding outperforms a layered source coding system only in very poor channels. The introduction of memory in the channel degrades the performance of both systems equally. Using interleaving to reduce the impact of memory in the channel has more influence on performance than the choice of source coder.

When a feedback channel is available from the receiver to the transmitter, adaptive schemes for source-channel coding can be used for image transmission. In this paper we see that the use of embedded source codes and embedded channel codes, when combined in a hybrid ARQ protocol, can provide the desired flexibility at high efficiency, low complexity and low feedback channel usage. We propose optimized schemes for transmission over Gilbert-Elliot channels, with and without constraints on the feedback channel usage. We observe that the end-to-end quality of the received image can be improved significantly by a moderate use of feedback.

We propose multiple description (MD) video coders which use motion compensated predictions. Our MD video coders utilize MD transform coding and three separate prediction paths at the encoder, to mimic the three possible scenarios at the decoder: both descriptions received or either of the single descriptions received. We provide three different algorithms to control the mismatch between the prediction loops at the encoder and decoder. The results show that when the main prediction loop is the central loop, it is important to have side prediction loops and transmit some redundancy information to control mismatch.

We present a transmission scheme for exploiting diversity given by two transmit antennas when neither the transmitter nor the receiver has access to channel state information. The new detection scheme can use PSK constellations and encoding is simple. At the receiver, decoding is achieved with low decoding complexity. The transmission provides full spatial diversity and requires no channel state side information at the receiver. The scheme can be thought as the extension of differential detection schemes to two transmit antennas.

We document the performance of space-time block codes, which provide a new paradigm for transmission over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space-time block code, and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximum likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space-time block code and gives a maximum likelihood decoding algorithm which is based only on linear processing at the receiver. We review the encoding and decoding algorithms for various codes and provide simulation results demonstrating their performance. It is shown that using multiple transmit antennas and space-time block coding provides remarkable performance at the expense of almost no extra processing.

We present a construction of multiple description trellis-coded quantizers. We use the tensor product of trellises to build a trellis which is applicable to multiple description coding. The problems of index assignment and set partitioning for the resulting trellis are considered. The Viterbi algorithm provides the best path for encoding and the design procedure utilizes a generalized Lloyd algorithm. The encoding process simultaneously generates all the transmitted sequences. Furthermore, the complexity of the scheme is almost independent of the rate. The quantizer provides remarkable performance with little encoding complexity.

We propose a fast reconstruction method for a subband-decomposed, progressive signal coding system. We show that unlike the conventional approach which requires a fixed computational complexity, the computational complexity of the proposed approach is proportional to the number of refined coefficients at each level of progression. Therefore, unrefined coefficients do not add to the computational complexity of the proposed scheme. It is shown, through specific examples, that the proposed approach can lead to significant reductions in reconstruction complexity. Furthermore, the proposed approach provides the capability for an online updating of the reconstructed image based on receiving the refinement of each coefficient.

We propose successively refinable trellis-coded quantizers for progressive transmission. (Progressive transmission is an essential component of image and multimedia browsing systems.) A new trellis structure which is scalable is used in the design of our trellis-coded quantizers. A hierarchical set partitioning is developed to preserve successive refinability. Two algorithms for designing trellis-coded quantizers which provide embedded bit streams are provided. The computational complexity of the proposed schemes is compared with that of trellis-coded quantization. Simulation results show excellent performances for memoryless sources.

We introduce space-time block coding, a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space-time block code and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximum-likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space-time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space-time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space-time block codes. It is shown that space-time block codes constructed in this way only exist for few sporadic values of n. Subsequently, a generalization of orthogonal designs is shown to provide space-time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space-time block codes are designed that achieve 1/2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space-time block codes are designed that achieve, respectively, all, 3/4, and 3/4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well.

We propose successively refinable trellis coded quantizers which are suitable for progressive transmission. A new trellis structure which is scalable is used in the design of our trellis coded quantizers. A hierarchical set partitioning is used to preserve successive refillability. Two algorithms for designing trellis coded quantizers which provide embedded bit streams are provided. The computational complexity of the proposed schemes is compared with that of trellis coded quantization. Simulation results show good performance for memoryless sources.

We propose algorithms to design channel-optimized vector quantizers in the presence of channel mismatch. We consider two cases: (i) no information about the statistics of the channel bit error rate is available and (ii) the probability density function of the channel bit error rate is known. We also consider the use of an estimate of the channel signal-to-noise ratio to improve the performance. Simulation results demonstrate the advantages of new design algorithms.

We present a construction of multiple description trellis coded quantizers. We use the tensor product of trellises to build a trellis which is applicable to multiple description coding. The problems of index assignment and set partitioning for the resulting trellis are considered. The Viterbi algorithm provides the best path for encoding and the design procedure utilizes a generalized Lloyd algorithm. The encoding process simultaneously generates all the transmitted sequences. Furthermore, the complexity of the scheme is almost independent of the rate. The quantizer provides remarkable performance with little encoding complexity.

We present a new classification scheme, dubbed spectral classification, which uses the spectral characteristics of the image blocks to classify them into one of a finite number of classes. A vector quantizer with an appropriate distortion measure is designed to perform the classification operation. The application of the proposed spectral classification scheme is then demonstrated in the context of adaptive image coding. It is shown that the spectral classifier outperforms gain-based classifiers while requiring a lower computational complexity.

We study the design of entropy-constrained successively refinable scalar quantizers. We propose two algorithms to minimize the average distortion and design such a quantizer. We consider two sets of constraints on the entropy: (i) constraint on the average rate and (ii) constraint on aggregate rates. Both algorithms can be easily extended to design vector quantizers.

This paper investigates various classification techniques, applied to subband coding of images, as a way of exploiting the nonstationary nature of image subbands. The advantages of subband classification are characterized in a rate-distortion framework in terms of "classification gain" and overall "subband classification gain." Two algorithms, maximum classification gain and equal mean-normalized standard deviation classification, which allow unequal number of blocks in each class, are presented. The dependence between the classification maps from different subbands is exploited either directly while encoding the classification maps or indirectly by constraining the classification maps. The trade-off between the classification gain and the amount of side information is explored. Coding results for a subband image coder based on classification are presented. The simulation results demonstrate the value of classification in subband coding.

We propose a fast reconstruction method for a progressive subband-decomposed signal coding system. It is shown that unlike the normal approach which contains a fixed computational complexity, the computational complexity of the proposed approach is proportional to the number of refined coefficients. Therefore, using the proposed approach in image coding applications, we can update the image after receiving each new coefficient and create a continuously refined perception. This can be done without any extra computational cost compared to the normal case where the image is reconstructed after receiving a predefined number of bits. The idea for complexity reduction is extended to general filter banks.

We propose a channel-matched hierarchical table-lookup vector quantizer (CM-HTVQ) which provides some robustness against the channel noise. We use a finite-state channel to model slowly fading channels and propose an adaptive coding scheme to transmit a source over wireless channels. The performance of CM-HTVQ is in general slightly inferior to that of channel-optimized vector quantizer (COVQ) (the performances coincide at some cases); however, the encoder complexity of CM-HTVQ is much less than the encoder complexity of COVQ.

A hierarchical pruned tree-structured vector quantizer (PTSVQ) employing multi-stage PTSVQ's is introduced to encode image wavelet coefficients. The result is a low-complexity and scalable image coding system. The effects of non-square selection of the block sizes and normalization on the performance of the system are investigated.

In this paper we present an adaptive discrete wavelet transform image coding scheme. The main contribution of the work is in developing a simple classification procedure which is used to classify the subband blocks into a finite number of classes. The classified subband blocks are subsequently encoded. The low frequency band is encoded by means of a 2-D DCT using entropy-coded trellis coded quantization (ECTCQ); other bands are directly encoded using ECTCQ. The peak signal-to-noise ratio results of the proposed adaptive system are 0.4-1.0 dB superior to those of its non-adaptive counterpart.>