Rydberg atomic receiver has recently emerged as a breakthrough technology for sensing and communications in next-generation Internet-of-Things (IoT) owing to its potential to surpass sensitivity limits of classical radio frequency (RF) receivers. In this paper, we consider a multiple-input multiple-output (MIMO) system with an RF transmitter and a Rydberg atomic receiver. Unlike prior works, a key technical innovation of our approach lies in the joint optimization of both transmit and receive processing techniques along with the design of reference signal injection according to a mean square error (MSE) criterion for signal recovery. However, the design problem is nonconvex on account of phase information loss in the received signal and nonlinearity of the objective function. To overcome this tricky challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning (DL) framework calledDeepQ-MIMObased on the construction of an advanced DL network with innovative customization mechanisms. Numerical results confirm the supremacy and efficacy of the proposed DeepQ-MIMO system, and further provide useful design insights.

Anomaly detection in run-to-failure (RTF) data is one of the pivotal tasks in a variety of real-world signal processing applications. Vast majority of the existing anomaly detection methods in the literature are based on generative adversarial networks (GANs), but they are ineffective and problematic when applied to the RTF data. To break through such a limitation, in this paper, we proposeRTF-Skip-GANomaly, a novel and high-performing deep learning (DL) framework for anomaly detection in the RTF data, by leveraging the off-the-shelf Skip-GANomaly as backbone. Key innovations in our approach are two-fold: (i) we introduce an extra dual-stream DL network calledWeibull-Netand integrate it into the Skip-GANomaly backbone such that a pseudo Weibull function is predicted from the latent representation of the reconstructed RTF data with time embedding, and (ii) we also devise an effective strategy to train the Weibull-Net jointly with the Skip-GANomaly backbone through a customized loss function design. Experimental results validate the superior performance and enhanced efficacy of our framework over the existing approaches.

In real-world AI applications, training datasets are often contaminated, containing a mix of in-distribution (ID) and out-of-distribution (OOD) samples without labels. This contamination poses a significant challenge for developing and training OOD detection models, as nearly all existing methods assume access to a clean training dataset of only ID samples—a condition rarely met in real-world scenarios. Customizing each existing OOD detection method to handle such contamination is impractical, given the vast number of diverse methods designed for clean data. To address this issue, we propose a universal, model-agnostic framework that integrates with nearly all existing OOD detection methods, enabling training on contaminated datasets while achieving high OOD detection accuracy on test datasets. Additionally, our framework provides an accurate estimation of the unknown proportion of OOD samples within the training dataset—an important and distinct challenge in its own right. Our approach introduces a novel dynamic weighting function and transition mechanism within an iterative training structure, enabling both reliable estimation of the OOD sample proportion of the training data and precise OOD detection on test data. Extensive evaluations across diverse datasets, including ImageNet-1k, demonstrate that our framework accurately estimates OOD sample proportions of training data and substantially enhances OOD detection accuracy on test data.

Federated Class-Incremental Learning (FCIL) refers to a scenario where a dynamically changing number of clients collaboratively learn an ever-increasing number of incoming tasks. FCIL is known to suffer from local forgetting due to class imbalance at each client and global forgetting due to class imbalance across clients. We develop a mathematical framework for FCIL that formulates local and global forgetting. Then, we propose an approach called Hybrid Rehearsal (HR), which utilizes latent exemplars and data-free techniques to address local and global forgetting, respectively. HR employs a customized autoencoder designed for both data classification and the generation of synthetic data. To determine the embeddings of new tasks for all clients in the latent space of the encoder, the server uses the Lennard-Jones Potential formulations. Meanwhile, at the clients, the decoder decodes the stored low-dimensional latent space exemplars back to the high-dimensional input space, used to address local forgetting. To overcome global forgetting, the decoder generates synthetic data. Furthermore, our mathematical framework proves that our proposed approach HR can, in principle, tackle the two local and global forgetting challenges. In practice, extensive experiments demonstrate that while preserving privacy, our proposed approach outperforms the state-of-the-art baselines on multiple FCIL benchmarks with low compute and memory footprints.

In class-incremental learning (CIL), effective incremental learning strategies are essential to mitigate task confusion and catastrophic forgetting, especially as the number of tasks $t$ increases. Current exemplar replay strategies impose $\mathcal{O}(t)$ memory/compute complexities. We propose an autoencoder-based hybrid replay (AHR) strategy that leverages our new hybrid autoencoder (HAE) to function as a compressor to alleviate the requirement for large memory, achieving $\mathcal{O}(0.1 t)$ at the worst case with the computing complexity of $\mathcal{O}(t)$ while accomplishing state-of-the-art performance. The decoder later recovers the exemplar data stored in the latent space, rather than in raw format. Additionally, HAE is designed for both discriminative and generative modeling, enabling classification and replay capabilities, respectively. HAE adopts the charged particle system energy minimization equations and repulsive force algorithm for the incremental embedding and distribution of new class centroids in its latent space. Our results demonstrate that AHR consistently outperforms recent baselines across multiple benchmarks while operating with the same memory/compute budgets. The source code is included in the supplementary material and will be open-sourced upon publication.

Vision–Language Models (VLMs) have recently attracted considerable attention in Federated Learning (FL) due to their strong and robust performance. In particular, few-shot adaptation with pre-trained VLMs like CLIP enhances the performance of downstream tasks. However, existing methods still suffer from substantial communication overhead, high local computational demands, and suboptimal performance under non-IID user data. To simultaneously address all those limitations, we propose NormFit, a lightweight solution that selectively fine-tunes only a very small portion of the model parameters, specifically only the Pre-LayerNorm parameters of the vision encoder within a VLM. Overcoming the existing tradeoff between performance and communication/computation efficiency in few-shot FL, NormFit sets a new benchmark by simultaneously achieving superior accuracy and substantially reduced communication and computational demands. Theoretically, we show that NormFit yields a considerably smaller generalization gap compared to tuning all LayerNorm parameters. Importantly, NormFit can function effectively as a standalone solution or integrate seamlessly with existing few-shot fine-tuning methods to further enhance their performance. Notably, NormFit offers implementation simplicity, achieving these improvements without any algorithmic modifications, changes to the underlying model architecture, or the addition of external parameters.

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: 1) a feature extractor corresponding to the encoder part of our customized U-Net and 2) a RUL predictor constructed by a multilayer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pretrained for high-quality feature learning, and then, is retrained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme.

pinching antenna system (PASS) has been demonstrated as a feasible flexible-antenna technology for upcoming 6G wireless networks and Internet of Things (IoT). In this article, we investigate a new design problem on capacity maximization for a point-to-point multiple-input–multiple-output (MIMO) PASS in a realistic IoT environment by jointly optimizing precoding matrix and antenna positioning. Unfortunately, this problem is not mathematically tractable. To break through this challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning framework, named CaMPASS-Net, based on an advanced dual-stream network architecture with a residual connection, inspired by our insight into the problem. Furthermore, we present an effective unsupervised training strategy for the proposed CaMPASS-Net based on an innovative loss function design. Simulation results confirm that the proposed CaMPASS-Net exhibits remarkable performance improvements over baseline and existing schemes.

Super-resolution (SR) of remote sensing imagery based on generative AI models is vital for practical applications such as urban planning and disaster assessment. However, current approaches suffer from poor performance trade-offs among the pivotal, yet competing, objectives: perceptual quality, factual accuracy, and inference speed. To break through this limitation, we propose a novel and high-performing two-stage SR framework for the remote sensing imagery based on a generative diffusion model. First, in Stage 1, factually grounded base images are generated by employing a guidance-free diffusion process relying solely on the original low-resolution images, such that the risk of semantic hallucination can be effectively mitigated. The generated images are refined subsequently in Stage 2 such that high-frequency details for SR quality can be restored via our customized and innovative guidance mechanism with a vision–language model (VLM) and a ControlNet, and a dynamic inference acceleration technique is applied to ensure efficiency. Extensive experimental results confirm that our proposed framework excels in perceptual quality—achieving top CLIP-IQA scores—and in structural integrity while achieving robust performance. In particular, it enables reliable, high-fidelity SR for large-scale, real-world remote sensing pipelines by surpassing the conventional fidelity–hallucination trade-off at practical inference speed. Source code is available at https://github.com/Bluear7878/Remote-Sensing-Vision-Language-Diffusion-Model.

In class-incremental learning (class-IL), models must classify all previously seen classes at test time without task-IDs, leading to task confusion. Despite being a key challenge, task confusion lacks a theoretical understanding. We present a novel mathematical framework for class-IL and prove the Infeasibility Theorem, showing optimal class-IL is impossible with discriminative modeling due to task confusion. However, we establish the Feasibility Theorem, demonstrating that generative modeling can achieve optimal class-IL by overcoming task confusion. We then assess popular class-IL strategies, including regularization, bias-correction, replay, and generative classifier, using our framework. Our analysis suggests that adopting generative modeling, either for generative replay or direct classification (generative classifier), is essential for optimal class-IL.

Optical character recognition (OCR) is one of the key enabling technologies in industrial internet-of-things (IIoT) for extracting and utilizing useful textual information, but it is technically challenging due to poor environmental conditions. To deal with such challenges, in this letter, we propose a novel two-stage deep learning framework for OCR using a generative diffusion model, namely, OCR-Diff. In the first stage, our customized conditional U-Net is pre-trained jointly with a feature extractor with the aid of the forward diffusion process such that the quality of a low-resolution text image is improved via the reverse diffusion process. In the next stage, the pre-trained conditional U-Net and feature extractor are jointly fine-tuned for an off-the-shelf text recognizer to precisely recognize the texts in the image. Experimental results on TextZoom datasets substantiate the superiority and effectiveness of the proposed scheme.

We propose a non-uniform joint pilot design and channel estimation (JPDCE) scheme in closed-loop orthogonal frequency-division multiplexing (OFDM) systems. Specifically, the encoder of the proposed JPDCE scheme is composed of two sub-networks which are used for pilot location assignment and pilot power allocation, respectively. To ensure accurate learning of the pilots, the OFDM block consisting of intelligently customized layers is designed to constrain the output of the encoder. Furthermore, the decoder is used to learn the channel state information (CSI) based on the output of OFDM layers by minimizing the mean square error (MSE) of the channel estimation. The numerical results indicate that the JPDCE scheme considerably outperforms the traditional methods as well as two state-of-the-art deep learning (DL)-based ones, demonstrating its excellent ability to learn the statistical characteristics of the wireless channel. In addition, we demonstrate that the JPDCE scheme shows excellent robustness against various channel distortion and interference: when 1) there are limited pilots; 2) the cyclic prefix (CP) is removed; and/or 3) clipping noise is introduced.

One of the most critical challenges of federated learning (FL) is to send data efficiently and reliably over the noisy wireless channels between the clients and server to achieve target learning accuracy as fast as possible. To achieve this goal, we design effective error correction coded FL with managed retransmissions. Rather than using Shannon capacity as the performance measure to design the communication mechanisms for FL, our approach relies critically on learning accuracy. Our fundamental idea is based on the observation that Stochastic Gradient Decent (SGD) and its family can tolerate some errors in the course of training. Inspired by this, to reduce the communication burden without degrading the learning accuracy, our FL framework with Managed Redundancy (FL-MR) has two phases: (i) the No-Retransmission phase, where retransmissions are never performed even in case of erroneous decoding of data and (ii) the Select Retransmission phase, where only some carefully selected data packets are retransmitted. Our extensive simulation results demonstrate that the proposed coded FL system achieves target accuracies much faster than the baseline coded approach.

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multidevice terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporateboth rate and transmission distanceinto the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of therate–distance productsover all users. This metric has been widely adopted in large-scale ad hoc networks and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.

This work studies the cooperative inference of deep neural networks (DNNs), in which a memory-constrained end device performs a delay-constrained inference process with an aid of an edge server. Although several works considered the cooperative inference of DNNs in the literature, it was assumed in those works that the memory footprints at end devices are unlimited, which is in practice not realistic. To address this issue, in this work, a memory-aware cooperative DNN inference is proposed. Specifically, we propose to adopt knowledge distillation to obtain high-performing lightweight DNNs. To minimize the inference delay, we first analyze the end-to-end delay required for processing the proposed cooperative DNN inference, and then we minimize the delay by jointly optimizing the DNN partitioning point and the intermediate data transmission rate. Also, a dynamic DNN selection scheme is developed by fully exploiting the available memory resource in order to maximize the performance of the inference task in terms of inference accuracy. Experimental results demonstrate that the proposed cooperative DNN inference considerably outperforms the comparable schemes while satisfying both the delay constraint and the memory constraint.

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions decelerate FL. To mitigate the communication overheads, two main techniques have been studied: (i) local update of weights characterizing the trade-off between communication and computation and (ii) gradient compression characterizing the trade-off between communication and precision. To the best of our knowledge, studying and balancing those two trade-offsjointly and dynamicallywhile considering their impacts on convergence has remained unresolved even though it promises significantly faster FL. In this paper, we first formulate our problem to minimize learning error with respect to two variables:local update coefficientsandsparsity budgetsof gradient compression who characterize trade-offs between communication and computation/precision, respectively. We then derive an upper bound of the learning error in a given wall-clock time considering the interdependency between the two variables. Based on this theoretical analysis, we propose an enhanced FL scheme, namely Fast FL (FFL), that jointly and dynamically adjusts the two variables to minimize the learning error. We demonstrate that FFL consistently achieves higher accuracies faster than similar schemes existing in the literature.

Simultaneous wireless information and power transfer (SWIPT) is very promising for various applications with the Internet of Things (IoT). In this article, we study dynamic power splitting for the SWIPT in an ergodic fading channel. Considering nonlinearity of practical energy harvesting (EH) circuits, we adopt the realistic nonlinear EH model rather than the idealistic linear EH model. To characterize the optimal rate-energy (R-E) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): 1) when the CSI is known only at the receiver (the CSIR case) and 2) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power-splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful insights into the dynamic power splitting with nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. The numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.

Ground vibration monitoring with microelectromechanical systems (MEMS) sensors is very effective and promising for alerting geological disasters. In this letter, explicitly considering and effectively addressing several specific issues related to practical MEMS sensors, we develop a novel ground vibration monitoring scheme for MEMS sensed data based on a deep convolutional neural network (CNN). Experiments are then conducted on the synthetic and real data sets. Experimental results on both data sets demonstrate that the proposed scheme significantly outperforms the other comparable schemes. For the synthetic data set, the proposed scheme achieves a very high overall accuracy of 98.82%. Also, for the real data set, the proposed scheme achieves a high overall accuracy of 81.64%, which is about 7% higher than that reported in the literature.

This paper studies coordinated multipoint (CoMP) transmission with non-orthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) network. We investigate two different CoMP-NOMA schemes, namely joint transmission-NOMA (JT-NOMA) and Alamouti NOMA (A-NOMA). Also, as the benchmark, joint transmission orthogonal multiple access (JT-OMA) is considered. Expressions are derived for the outage probabilities achieved by JT-NOMA, A-NOMA, and JT-OMA for cell edge users with quality of service (QoS) requirements. To compare these schemes in terms of spectral efficiency, we use the ε-outage rate region that is defined to be the set of all achievable rate pairs at which the outage probabilities are equal to ε. It is shown that A-NOMA is superior to both JT-NOMA and JT-OMA in high SNR. Also, it is analytically proved that JT-NOMA outperforms JT-OMA, when the circuit energy consumption is negligible. A particularly interesting observation is that when the circuit energy consumption is non-negligible, JT-NOMA is not always better than JT-OMA. It is shown that when the difference between the expectations of effective channel power gains is not sufficiently large, JT-OMA is superior to JT-NOMA for the same circuit energy consumption.

This letter proposes a coupled compressed samples online robust learning (C2ORL) algorithm for constructing an adaptive analog-to-information conversion (AIC) structure. The C2ORL optimizes both the dictionary and the observation matrix in each mini-batch learning process. The compressed samples are used for learning to resolve the issue that both the analog received signals and the unstable reconstructed signals affected by reconstruction error are not suitable for direct training. Furthermore, the online mechanism and the robust objective functions are adopted in order to make the learning algorithm better matched for the requirements of the analog signal compressed sensing. Based on the C2ORL, we construct an adaptive AIC structure, which can considerably improve the performance in the sense of minimizing the reconstruction error. Also, the structure is more tolerant to the outliers and the noise in the receive process and has denoising ability to improve the signal-to-noise ratio under the noise background. The mathematical expression shows that the proposed structure clearly reflects the optimization brought by the coupled learning algorithm. Finally, the experiments demonstrate that the proposed scheme considerably outperforms any existing approaches.

In this paper, the problem of joint mode switching and power adaptation is studied for simultaneous wireless information and power transfer (SWIPT) over a fading channel. The receiver dynamically switches between information decoding (ID) and energy harvesting (EH) modes while the transmitter dynamically adapts the transmit power. Considering the nonlinearity of practical EH circuits, a realistic nonlinear EH model is adopted rather than the idealistic linear EH model. To characterize the ultimate performance tradeoff between ID and EH, an optimization problem is formulated to maximize the average harvested energy under the constraints on the average achievable rate and the average transmit power, which is a nonconvex and combinatorial problem. To solve this problem, first, the optimal power adaptation scheme for the nonlinear EH receiver that operates only in the EH mode is proposed. Using this scheme, the jointly optimal solution for the mode switching and power adaptation is then derived. By comparing the obtained results to the existing results, various useful and interesting insights into the optimized SWIPT system with nonlinear EH are presented. An important insight into the impact of nonlinear EH is that, to exploit the high energy conversion efficiency of the nonlinear circuit, the EH mode has to be selected only in the moderate range of channel gains. Also, in the EH mode, the power has to be adapted to the short-term power threshold only for the moderate channel gains.

In this paper, we study rate-energy (R-E) tradeoffs for simultaneous wireless information and power transfer (SWIPT). In the existing literature, by invoking a simplistic and ideal assumption of linear energy harvesting, the R-E tradeoff performance was analyzed only for the four SWIPT schemes: the dynamic power splitting, type-I on-off power splitting (OPS), static power splitting, and time switching. Different from such works, in this work, we consider the realistic and practical scenario of nonlinear energy harvesting. Furthermore, to characterize the R-E tradeoff with nonlinear energy harvesting, we propose a new SWIPT scheme, the generalized OPS (GOPS). As a special case of the proposed GOPS, we also investigate an additional SWIPT scheme, the type-II OPS. Through the analysis based on the realistic nonlinear models reported in the literature, we derive new theoretical results on the R-E tradeoff, which are in sharp contrast to those in the existing literature obtained with linear energy harvesting. Furthermore, we provide various useful insights into the SWIPT system with nonlinear energy harvesting.

In this paper, we study time-varying channel estimation and optimal pilot design for multiple-input multiple-output two-way multi-relay systems in the sense of minimizing the total estimation mean square error (MSE) under the power constraints at two source nodes and multiple relays. A particularly challenging issue in the optimal pilot design is to completely eliminate the inter-link interference while most efficiently using the channel resource. To address this challenging issue, we consider a pilot design approach based on the phase rotation technique at multiple relays. First, we establish various optimality conditions for the pilot design in the sense of the minimum total MSE. Then, we propose the optimal pilot scheme by designing the pilot signals, phase rotations, and pilot symbol positions to satisfy the established optimality conditions. Finally, we propose the asymptotically optimal pilot scheme in the high signal-to-noise ratio (SNR) regime with low complexity. Simulation results show that the proposed schemes significantly outperform the existing schemes in terms of the channel estimation and the bit error rate, and the proposed asymptotically optimal scheme provides the near-optimal performance even in the low to moderate SNR range.

In this paper, we study a training design problem for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) two-way relay networks. Unlike the existing studies, we assume the spatially correlated fading and consider the nonreciprocal channel condition, which is a more practical assumption but makes the training problem more challenging. The equivalent channels of bidirectional relaying links, which consist of self-interfering channels and information-bearing channels, are estimated at each source node based on a linear minimum mean square error (LMMSE) approach. The total mean square error (MSE) of the channel estimation is minimized under the transmit power constraints at the source nodes and at the relay. To solve this problem, we first derive an optimal structure of the training signals, and then, convert the optimization problem into a tractable convex form, from which the optimal training scheme is designed efficiently. Furthermore, for a practical special case, the optimal training design is derived in semi-closed form, which provides useful insights. To reduce the required complexity, a low-complexity training scheme is also derived in closed-form. This scheme is shown to be asymptotically optimal in the high signal-to-noise ratio (SNR) regime and gives further insights into the optimal training. The performance of the proposed schemes is demonstrated through numerical simulations.

In this paper, the fundamental performance of the simultaneous wireless information and power transfer (SWIPT) system is studied. Unlike any existing works where the codelength was assumed to be infinity, we explicitly consider the case of the finite codelength, which is much more realistic especially for the practical SWIPT system due to its limited power and complexity. For the four well-known SWIPT schemes, we analyze the tradeoff between the rate and energy; then we study the optimality of those SWIPT schemes. Furthermore, to fully characterize the fundamental performance of the SWIPT system in the regime of finite codelength, we propose to additionally use the new tradeoff between the decoding error probability and the harvested energy. In the sense of this new tradeoff, we study the optimality of the four SWIPT schemes. For the analysis of the two types of tradeoffs, we consider two different cases: when the transmit power of symbols is adapted or not. For various scenarios, we provide useful insights into the performance of the SWIPT system in the finite codelength.

In this paper, we consider an amplify-and-forward-based two-way relaying network, in which the relays need to harvest energy from the received radio frequency signals to remain active in the network and assist data exchange between two transceivers. In particular, considering time-switching architecture, we investigate the problem of joint energy harvesting (EH) time allocation and distributed beamforming in the presence of interference. Specifically, assuming that the perfect knowledge of the interfering links is not available, we study three different design approaches. First, we maximize the sum-rate of the network subject to individual EH power constraints at relays. Resorting to the semi-definite relaxation (SDR) and successive upper-bound minimization techniques, we devise an iterative algorithm that efficiently solves such a challenging problem. Next, we minimize the total power consumed by the relays subject to the rate constraints at the transceivers. Finally, we minimize the EH-phase duration subject to the individual EH power constraints at the relays as well as the rate constraints at the transceivers. The rate constraints, however, make both second and third design optimization problems non-convex and complicated. Although no closed-form solutions are available for these approaches, we propose efficient schemes by applying the SDR technique followed by semi-definite programming problems.

The authors study cooperative security in the physical layer of wireless systems based on the recently developed polar codes for multiple access channel (MAC). Specifically, the authors first consider the case of a m ‐user MAC with external eavesdropper. Using polar alignment, the authors formulate a discrete optimisation problem where security and reliability criteria can be handled separately over the bases of a set of deterministic binary matrices. A discrete algorithm with incrementally polynomial complexity is used to maximise the uniform sum secrecy rate of users. Moreover, the proposed coding scheme is shown to achieve strong security for any subset of users. The authors next examine the case of minimum number of cooperative helpers to fulfil a feasible secrecy rate requirement at the legitimate user. Here, a low‐complexity suboptimal algorithm is presented with at most one helper more than the optimal solution. Using Tal–Sharov–Vardy implementation of MAC polar codes, secure polar coding is implemented for a 2‐user Gaussian wiretap MAC channel. The upper and lower bound of block error probability are compared, respectively, at the legitimate receiver and the eavesdropper. The results demonstrate clearly that with sufficiently long block length, strong secrecy with respect to the eavesdropper is achieved, while block error probability approaches 0.5.

A bit-error-rate (BER)-based physical layer security approach is proposed for the finite blocklengths. For secure communication in the sense of high BER, the information-theoretic strong converse is combined with cryptographic error amplification achieved by the substitution permutation networks based on the confusion and diffusion. For the discrete memoryless channels (DMCs), an analytical framework is provided showing the tradeoffs among the finite blocklength, the maximum/minimum possible transmission rates, and the BER requirements for the legitimate receiver and the eavesdropper. In addition, the security gap is analytically studied for the Gaussian channels and the concept is extended to other DMCs including the binary symmetric channels and binary erasure channels.

In this paper, the fundamental rate-energy (R-E) tradeoffs are studied for simultaneous wireless information and power transfer (SWIPT). Unlike the existing results obtained using Gaussian input given an average power constraint, we focus on the equi-probable arbitrary shaped discrete inputs considering a peak power constraint as well as an average power constraint. For these power constraints, the transmit power strategies are presented and the R-E regions are derived. The optimality of different SWIPT schemes is first studied in terms of the R-E tradeoff for the case of negligible circuit power consumption in the information decoder. The results are extended to the case of non-negligible circuit power consumption in the information decoder. The asymptotic R-E regions are also derived, which give us useful insights into the fundamental R-E tradeoffs. Finally, the R-E regions of Gaussian input and discrete inputs are compared under an average power constraint.

This paper studies the problem of optimal beamforming and power allocation for an amplify-and-forward (AF)-based two-way relaying network in the presence of interference and channel state information (CSI) uncertainty. In particular, we obtain the beamforming vector as well as the users’ transmit powers under two assumptions on the availability of the CSI of the interfering links, namely norm-bounded uncertainty model and the second-order statistics scenario. To do so, we develop two design approaches. The first approach is based on the total transmit power minimization technique. We start with the norm-bounded uncertainty model and derive the optimal solution to the corresponding problem. To reduce the computational complexity, we also develop a low-complexity algorithm which offers performance that is very close to the optimal one. In the second approach, we apply a signal-to-interference-plus-noise ratio (SINR) balancing technique. We propose another low-complexity algorithm based on the SINR balancing criteria. Next, we consider the scenario where the second-order statistics of the CSIs are available. Again we start with the total power minimization method and derive both optimal and suboptimal algorithms. Finally, we apply the SINR balancing technique to this scenario and develop another low-complexity algorithm, which is suitable for practice.

This paper investigates simultaneous wireless information and power transfer (SWIPT) in energy harvesting (EH) relay systems. Unlike the existing SWIPT schemes requiring the instantaneous channel state information (CSI) for coherent information delivery, we propose a noncoherent SWIPT framework for decode-and-forward (DF) relay systems bypassing the need for the instantaneous CSI and consequently saving energy in the network. The proposed SWIPT framework embraces both the power-splitting noncoherent DF (PS-NcDF) and timeswitching noncoherent DF (TS-NcDF) in a unified form, and supports arbitrary M-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK). The exact (noncoherent) maximum-likelihood detectors (MLDs) for PS-NcDF and TS-NcDF are derived in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark for noncoherent SWIPT. To reduce the computational cost of the exact MLDs, we also propose closed-form approximate MLDs achieving near-optimum performance, thus serving as a practical solution for noncoherent SWIPT. Numerical results demonstrate a performance tradeoff between the first and second hops through the adjustment of time switching or power splitting parameters, whose optimal values minimizing the symbol-error rate (SER) are strictly between 0 and 1. We demonstrate that M-FSK results in a significant energy saving over M-DPSK for M ≥ 8; thus M-FSK may be more suitable for EH relay systems.

In energy harvesting (EH) relay networks, the coherent communication requires accurate estimation/tracking of the instantaneous channel state information (CSI) which consumes extra power. As a remedy, we propose two noncoherent EH relaying protocols based on the amplify-and-forward (AF) relaying, namely, power splitting noncoherent AF (PS-NcAF) and time switching noncoherent AF (TS-NcAF), which do not require any instantaneous CSI. We develop a noncoherent framework of simultaneous wireless information and power transfer (SWIPT), embracing PS-NcAF and TS-NcAF in a unified form. For arbitraryM-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK), we derive maximum-likelihood detectors (MLDs) for PS-NcAF and TS-NcAF in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark. To avoid expensive integral computations, we propose a closed-form detector using the Gauss-Legendre approximation, which achieves almost identical performance as the MLD but at substantially lower complexity. These EH-based noncoherent detectors achieve full diversity in Rayleigh fading. Numerical results demonstrate that our proposed PS-NcAF and TS-NcAF may outperform the conventional grid-powered relay system under the same total power constraint. Various insights which are useful for the design of practical SWIPT relaying systems are obtained. Interestingly, PS-NcAF outperforms TS-NcAF in the single-relay case, whereas TS-NcAF outperforms PS-NcAF in the multi-relay case.

We investigate secure transmission for multiuser relay networks, where the undesired users who are not selected for data reception may overhear the source message as eavesdroppers. In this system, the secrecy performance may deteriorate as the number of users increases, since the number of eavesdroppers also increases. To address this issue, we consider a multiuser relay scheme with cooperative jamming (MUCJ). In this scheme, the desired user sends a jamming signal to the relay while the source sends its message to the relay, and then the relay amplifies and forwards the received signal to the desired user. Since the jamming can be subtracted only at the desired user, it acts as interference to prevent the eavesdroppers from intercepting the source message. We propose an optimal user selection scheme for the MUCJ, which is optimal in the sense of maximizing the secrecy rate. For the existing multiuser relay scheme (MURS) without cooperative jamming and the MUCJ, we derive the ergodic secrecy rates and analyze the asymptotic secrecy rate gains. We reveal that the ergodic secrecy rate can be increased as the number of users grows and much higher secrecy rate can be achieved by the MUCJ.

Unlike the existing detectors, which are developed for decode-and-forward (DF) networks in the ideal interference-free case, we consider a more practical scenario where arbitrary interference exists. We consider a DF cooperative network consisting of a source, multiple relays, a destination, and multiple interferers affecting both the relays and the destination. Each relay is equipped with multiple antennas and knows its local instantaneous channel state information (CSI). Assuming that the destination knows the instantaneous CSI of the source-relay, relay-destination, and source-destination channels, we develop, for the first time in the literature, the end-to-end optimum maximum-likelihood (ML) detectors in closed-form for DF systems employing either simultaneous or orthogonal transmissions in the presence of interference. Furthermore, theoretical analysis shows that the proposed detectors achieve full diversity gains in the presence of interference with finite interference-to-noise ratios. Numerical results demonstrate that the proposed optimum detectors substantially outperform the conventional schemes which simply ignore interference.

In this paper, we introduce a method of hyperspectral unmixing in the linear mixing model with the given library of the constituent materials. The proposed algorithm employs an arctan function to approximate the l0norm in the minimization problem. This approximation makes the objective function smooth, facilitates the convergence and results in reduced reconstruction errors. We evaluate the proposed method and compare it with other methods via simulation. This reveals that the proposed method outperforms the state-of-the-art methods and results in higher reconstruction signal-to-noise-ratio.

Linear precoding and cooperative jamming for multiuser broadcast channel is studied to enhance the physical layer security. We consider the system where multiple independent data streams are transmitted from the base station to multiple legitimate users with the help of a friendly jammer. It is assumed that a normalized linear precoding matrix is given at the base station, whereas the power allocated to each user is to be determined. The problem is to jointly design the power allocation across different users for linear precoding and the cooperative jamming at the friendly jammer. The goal is to maximize a lower bound of the secrecy rate, provided that a minimum communication rate to the users is guaranteed. The optimal solution is obtained when the number of antennas at the friendly jammer is no less than the total number of antennas at the users and eavesdropper. Moreover, a suboptimal algorithm is proposed, which can be applied for all the scenarios. Numerical results demonstrate that the proposed schemes are effective for secure communications.

We consider a cooperation scenario between primary users and untrusted secondary users in cognitive radio networks. The secondary users are willing to help the primary users to relay the primary users' messages in reward for being allowed to share the primary users' spectrum bands. However, the primary users might be reluctant to accept this help, since the secondary users are untrustworthy and may try unauthorized decoding of the primary users' messages. This paper will answer the question: when is this cooperation mutually beneficial for primary and secondary users? Taking an approach of information-theoretic secrecy, such as coding techniques for wiretap channels, the primary users can allow the secondary users to sense and relay the message, while ensuring that the secondary users are ignorant of the primary users' messages. We characterize an achievable secrecy rate of primary users with a rate of the secondary users' communication. From the derived rate pairs, an optimization problem is formulated such that the secondary transmitter distributes its transmit power to maximize its data rate while providing a higher secrecy rate to the primary users. We demonstrate that this cooperation can provide a positive secrecy rate, even when a non-cooperative scheme achieves a zero secrecy rate.

We develop closed-form generalized likelihood ratio test (GLRT) sequence detectors for multi-relay amplify-and-forward (AF) cooperative networks employing M-ary differential phase-shift keying (M-DPSK) and noncoherent M-ary frequency-shift keying (M-FSK). The proposed detectors achieve robust performance in a wide range of fading environments where prior knowledge of channels, signal powers, noise variances, and relay functionalities is unavailable to the destination. A comprehensive error probability performance analysis is carried out in Rayleigh fading. Specifically, we derive unified pairwise error probability (PEP) expressions for both detectors in a dual-hop network, which are valid for arbitrary modulation order M and arbitrary sequence length Ns. We also derive unified bit-error probability (BEP) expressions for both detectors employing binary signalings in various networks for Ns= 1. It is further shown that both detectors achieve near full diversity orders. Finally, the superiorities of the proposed detectors over the state-of-the-art noncoherent detectors are justified through extensive comparisons in practical scenarios. For example, in a multi-relay network where the relays are uniformly distributed between the source and destination, the proposed detector for noncoherent FSK with Ns= 1 outperforms the well-known maximum energy selector by almost 15 dB in high signal-to-noise ratios.

In this paper, we examine the marriage of Fountain coding and network coding (NC). Fountain codes are capacity achieving erasure codes designed for point-to-point transmissions. NC is a throughput-optimal data dissemination technique, but its high-complexity decoding makes it unattractive for applications where limited resources are available. In this paper, we consider Fountain network coding to take advantage of efficient fountain decoders. Protocols such as Soliton-like rateless coding (SLRC) have previously addressed this issue, yet the re-encoding at the relay is expensive while there is still room for improving the performance. Extending SLRC, we propose the Improved Soliton-like Rateless Coding (ISLRC) protocol, where the relay is designed to perform distribution shaping given limited resources. ISLRC preserves the same properties as SLRC, but also makes the aggregate degree distribution more efficient for Fountain decoding. We analyze ISLRC's degree distribution and perform an asymptotic error analysis for the case where resources are most scarce. The ISLRC scheme is compared against other existing schemes. Simulation results show that even under the worst-case scenario of ISLRC, better performance can be achieved compared to SLRC and other existing schemes.

Likelihood-based algorithms for the classification of linear digital modulations are systematically investigated for a multiple receive antennas configuration. Existing modulation classification (MC) algorithms are first extended to the case of multiple receive antennas and then a critical problem is identified that the overall performance of the multiple antenna systems is dominated by the worst channel estimate of a particular antenna. To address the performance degradation issue, we propose a new MC algorithm by optimally combining the log likelihood functions (LLFs). Furthermore, to analyze the upper-bound performance of the existing and the proposed MC algorithms, the exact Cramer-Rao Lower Bound (CRLB) expressions of non-data-aided joint estimates of amplitude, phase, and noise variance are derived for general rectangular quadrature amplitude modulation (QAM). Numerical results demonstrate the accuracy of the CRLB expressions and verify that the results reported in the literature for quadrature phase-shift keying (QPSK) and 16-QAM are special cases of our derived expressions. Also, it is demonstrated that the probability of correct classification of the new algorithm approaches the theoretical bounds and a substantial performance improvement is achieved compared to the existing MC algorithm.

Cooperative jamming for multiuser multiple input multiple output (MIMO) broadcast channel is studied to enhance the physical layer security with the help of a friendly jammer. We assume the base station transmits multiple independent data streams to multiple legitimate users. During the transmission, however, there are multiple eavesdroppers with multiple antennas that have interests in the streams from the base station. In order to wiretap the desired streams, the eavesdroppers may collude or not, and maximize the signal-to-interference-plus-noise ratio (SINR) of the desired streams using receive beamforming. The optimal cooperative jammer is designed to keep the achieved SINR at eavesdroppers below a threshold to guarantee that the transmission from the base station to legitimate users is confidential. One main advantage of the proposed cooperative jamming scheme is that no modification is needed for the existing precoding schemes at the base station and decoding schemes at legitimate users. Thus, any existing practical precoding/decoding schemes for multiuser MIMO broadcast channel can be applied directly with the help of a friendly jammer using the proposed cooperative jamming.

We propose a reactive opportunistic relaying (OR) scheme for time-division broadcast (R-TDBC) in a bidirectional cooperative network. In the proposed R-TDBC, a subset of relays that reliably decode both signals from the sources is determined reactively after two source-transmissions; then a single relay among the determined set is selected by maximizing the achievable rate of the broadcast phase. A somewhat surprising result is that the proposed R-TDBC achieves identical outage performance as its proactive counterpart in bidirectional cooperative networks with the direct link. We then derive the exact outage probability, tight closed-form bounds, and the diversity-multiplexing tradeoff (DMT) for the R-TDBC.

We study relay selection (RS) for time-division broadcast (TDBC) in a bidirectional network consisting of two different end-sources and multiple relays. By modifying the well-known proactive RS scheme valid only for unidirectional cooperative networks, we propose a proactive RS with TDBC (P-TDBC) for bidirectional cooperative networks. In the P-TDBC scheme, a single best relay is selected proactively before two consecutive source-transmissions by maximizing the bottleneck of the end-to-end maximal achievable rates in both directions. We then derive the exact outage probability of P-TDBC in closed-form for Es≤ Er, and in a single-integral form for Es>; Er, where Esand Erdenote the transmission powers at each end-source and the selected relay, respectively. Moreover, we derive very tight closed-form upper and lower bounds on the outage probability for Es>; Er. Finally, we study the diversity-multiplexing tradeoff (DMT) and show that the P-TDBC scheme achieves full diversity.

Relay selection (RS) has widely been studied for both decode-and-forward (DF) and amplify-and-forward (AF) protocols, and relay ordering (RO) was recently proposed for the AF protocol only . The two strategies, RS and RO, have been individually shown to be very effective to enhance the performance of cooperative relay networks. In this letter, we first discuss the fundamental difference of the two schemes and investigate an important tradeoff in terms of spectral efficiency and energy efficiency between RS and RO. Specifically, RS is more spectrally efficient, whereas RO can be more efficient in energy consumption. Then we demonstrate that indeed RS and RO generally have different outage performance and may complement each other depending on channel conditions. To optimize the outage performance, therefore, we combine the two strategies, RS and RO, and we propose joint RS-RO, followed by exact outage probability derivation of the joint RS-RO in closed-form. Finally, we obtain some interesting insights into the issue of selecting efficient transmission schemes between RS and RO.

In this paper, a relay precoding problem is considered in a non-regenerative multiple-input multiple output (MIMO) relay system, when multiple interferers exist near the destination. The relay has the perfect channel state information (CSI) of the source-relay link and only the covariance information of the relay-destination link. Also, we assume that the training signals of the interferers are known at the destination, and thus, the covariance information of the channels from the interferers to the destination can be estimated at the destination and the information is fed back to the relay. For this scenario, the structure of the optimal relay precoder is derived to maximize the average capacity seen by the relay under a relay transmit power constraint. For the derivation of the optimal relay precoder, a new partial ordering result for the outage probability and the ergodic capacity of spatially correlated MIMO channels is derived. Numerical results demonstrate that the proposed scheme considerably improves the performance. Overall, the contributions of this paper are twofold: i) a new partial ordering result for MIMO channels is derived and ii) the structure of the optimal relay precoder is derived using the partial ordering result.

We consider a relay precoding problem in a non-regenerative multiple-input multiple-output (MIMO) relay system, where the relay has the perfect channel state information of the source-relay link and only the covariance information of the relay-destination link. The average capacity seen by the relay is maximized under a relay transmit power constraint. The relay precoding problem is transformed to its equivalent power allocation problem at the relay. The optimal power allocation solution is then derived by formulating the optimization problem via difference of convex (DC) functions programming. To reduce the computational complexity, we also propose a suboptimal scheme by modifying the naive Jensen method. Furthermore, we propose the maximum eigenmode relaying (MER) scheme, which allocates the relay transmit power only to the maximum eigenmode. A necessary and sufficient condition for the MER to be asymptotically optimal is derived in the high signal-to-noise ratio (SNR) regime of the source-relay link. The performance of the proposed schemes is investigated through extensive numerical simulations.

We consider the uplink of a distributed antenna system (DAS) in the presence of interference and unknown oscillator offsets, which lead to synchronization errors. For this scenario, we develop a linear receiver which maximizes the output signal-to-interference-plus-noise ratio (SINR). Specifically, we first propose a new structured generalized sidelobe canceller (SGSC) formulation for the commonly used minimum-variance receiver, and derive a general framework to improve the robustness of a linear receiver when oscillator offsets exist. Then a new linear receiver is proposed in closed-form based on the proposed robust SGSC and a ridge regression technique. It is shown by simulations that the proposed linear receiver can provide very robust SINR performance and excellent symbol error rate (SER) performance in the existence of interference and unknown oscillator offsets.

For the binary erasure channel, Luby Transform (LT) and Raptors codes have been shown to achieve capacity by carefully designed degree distributions for multicasting scenarios. Generalizing fountain codes to multihop networks requires transport nodes to perform network coding (NC). However, if intermediate nodes perform decentralized NC blindly, the statistical properties imposed by the fountain code are lost, and thus, a Gaussian elimination decoder must be used at the sink at the cost of significant increase in complexity compared to a belief propagation (BP) decoder. Addressing this problem, in this paper, we propose a new protocol, namely Soliton-like rateless coding (SLRC), by exploiting the benefits of fountain coding and NC coding over a Y-network. Ensuring key properties of the fountain code are preserved; BP can be effectively applied when transport nodes perform NC. Additionally, the proposed coding protocol is resilient to nodes churn rates. The SLRC scheme is evaluated against buffer-and-forwarding, and the distributed LT (DLT) codes; SLRC exhibits a 5% reduction in overhead compared to the state of the art DLT code at high decoding success rates. Simulations show that the proposed scheme preserves the benefits of NC and fountain coding.

We investigate the exact error performance of hard minimum mean-squared error (MMSE) detection possibly with successive interference cancellation (SIC) in multiple-input multiple-output (MIMO) systems. To facilitate the analysis, we start with an exact bit-error rate (BER) analysis for a general system with decision statistic, z=ax+u, where a>;0, x is the transmitted signal, and u is an arbitrarily distributed noise component which is possibly dependent on the signal component x. For this general system, we derive the exact and closed-form BER expressions for M-ary pulse amplitude modulation (PAM) and arbitrary rectangular quadrature amplitude modulation (QAM), which include the well-known BER result of as a special case. Furthermore, by formulating the MIMO MMSE decision statistics in the same form as z in the general system, we derive the exact and closed-form instantaneous BER and symbol-error rate (SER) expressions for MIMO hard MMSE detection with/without SIC employing PAM and QAM. Finally, the validity of our derived error probability expressions is verified through extensive Monte Carlo simulations and the results reported in the literature.

We study the analog network coding (ANC), which is a well-known amplify-and-forward (AF)-based bidirectional protocol, for a bidirectional network consisting of two different sources and a relay. In this protocol, the two sources exchange information with the help of the relay during two time slots in a half-duplex mode. For this system, we first derive a tight lower bound of outage probability, which is very close to the exact outage probability in the whole signal-to-noise ratio (SNR) range irrespective of the values of channel variances. Using the tight lower bound, we obtain finite-SNR diversity-multiplexing tradeoff of the ANC protocol. Furthermore, we propose an optimum power allocation scheme, which simultaneously minimizes the outage probability and maximizes the total mutual information of the ANC protocol.

Time division broadcasting (TDBC) is a well-known bidirectional protocol. In this protocol, two sources exchange information with the help of a relay terminal. For amplify-and-forward (AF)-based TDBC, we first derive a tight lower bound of the outage probability in closed-form, and it is very close to the exact outage probability in the whole signal-to-noise ratio (SNR) range irrespective of the values of channel variances. Using the tight lower bound, diversity-multiplexing tradeoff of the TDBC protocol is obtained for finite-SNR. Furthermore, we investigate how to optimize the TDBC protocol; specifically, an optimum method to combine the received signals at the relay terminal is developed. This method minimizes the outage probability and maximizes the total mutual information of the TDBC protocol at the same time.

This paper focuses on the average bit error rate (BER) and diversity order analysis of the distributed Alamouti's code in dissimilar cooperative networks with channel state information (CSI)-assisted relays. We first assume that the relays adopt the amplifying coefficient proposed in . Lower and upper bounds of the average BER of the distributed Alamouti's code are derived. Those two bounds tightly bound the exact average BER. Then we show that, surprisingly, the distributed Alamouti's code achieves only diversity order one when the relays use the amplifying coefficient proposed in . To resolve this problem, we propose a new threshold-based amplifying coefficient for the distributed Alamouti's code based on the work in . This new amplifying coefficient makes the distributed Alamouti's code achieve the full diversity order two. Moreover, based on three different CSI assumptions, four amplifying schemes are developed in order to determine the value of the threshold used in the new amplifying coefficient.

Recently, single-symbol maximum-likelihood (ML) decodable distributed space-time block coding (DSTBC) has been developed for use in cooperative diversity networks. However, the symbol rate of the DSTBC decreases with the number of relays. This issue can be addressed if the channel phase information (CPI) of the first-hop is exploited, and such code is referred to as DSTBC-CPI. Some complex single-symbol decodable DSTBCs-CPI were reported in the literature. However, no upper-bound on the symbol rate of such DSTBC-CPI has been derived, although it is a fundamental issue. Furthermore, finding a tight (more preferably achievable) upper-bound is essential to check if any developed code is optimum or not. In this letter, we derive an upper-bound on the symbol rate of real single-symbol decodable DSTBC-CPI and show that the bound is independent of the number of relays in the network. Finally, we demonstrate that our derived bound is actually achievable.

We study relay selection with the physical-layer network coding (PNC), which is a major decode-and-forward (DF)-based bidirectional protocol. We consider a network consists of two different end-sources and multiple relays, where each relay adopts XOR-encoding to combine two received symbols from the two end-sources. For this network, we propose a relay selection scheme by modifying the well-known selection cooperation (SC) to use in the PNC protocol, and it is referred to as SC-PNC. The SC-PNC consists of two phases: a multiple access channel (MAC) phase and a broadcast channel (BC) phase. In the MAC phase, a set of relays that correctly decode two received symbols from the two end-sources is determined; in the BC phase, among the relays in the determined set, a single best relay is selected such that the minimum mutual information of the two links from each relay to the two end-sources is maximized. Finally, we derive the exact outage probability of the SC-PNC in closed-form.

We investigate the exact error performance of hard minimum mean-squared error (MMSE) detection in multipleinput multiple-output (MIMO) systems. To facilitate the analysis, we first conduct error analysis for a general system with decision statistic, z = ax + u, where a >; 0, x is the transmitted signal, and u is the arbitrarily distributed noise component which is independent of x. For this general system, we derive the exact and closed-form bit-error rate (BER) expressions for M-ary pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) signalings, which include the well-known BER result in [12] as a special case. For the first time in the literature (to the best of our knowledge), we then derive the exact and closed-form instantaneous BER and symbol-error rate (SER) expressions for hard MMSE detection. Finally, the validity of the derived error probability expressions is verified through our own Monte Carlo simulations and the simulation results reported in the literature.

Analog network coding (ANC) and time division broadcasting (TDBC)are two major protocols used in bidirectional cooperative networks with half-duplex terminals. Most previous work on those two protocols was based on the assumption that there was no direct channel between the two sources. In this paper, we remove this limitation and investigate both protocols in a general bidirectional cooperative network with a direct channel between the two sources. Lower bounds on the outage probabilities of the two protocols are derived first. These lower bounds are very close to the exact outage probabilities in the whole signal-to-noise ratio (SNR) range irrespective of the values of channel variances. Based on the lower bounds, the finite-SNR diversity-multiplexing tradeoffs (DMTs) of the ANC and TDBC protocols are obtained. These tradeoffs establish a framework which enables us to make a comprehensive comparison between both protocols.

An amplify-and-forward, multi-branch, multi-hop relay system with K relays, in which the relays broadcast to other relays as well as the destination, is analyzed. An approximate symbol-error-rate (SER) expression, which is valid for any number of relays and for several modulation schemes, is found for the multi-hop system. Also, the cumulative density function (CDF) and probability density function (PDF) are found for the random variable, Z = XY/(X + Y + c), where X and Y are sums of independent, Erlang random variables, and c is a constant. The moment generating function (MGF) of Z is found for the special case in which c = 0. It is shown that these results are generalizations of previously published results for special cases of Z. The MGF of Z is used to develop the approximate SER expression. Results for the analytic SER expression are included and compared with simulation results for various values of K, for various modulation schemes, and for two choices of system parameters (channel variances). Results for the multi-hop system are also compared to results for the two-hop system (in which relays transmit only to the destination).

We analyze the error performance of the physical-layer network coding (PNC) protocol without channel coding in bidirectional relay networks for binary phase shift keying (BPSK) over Rayleigh fading channels. It is assumed that a bidirectional relay network consists of two sources and a relay, where each node has a single antenna and operates in a half-duplex mode, and the PNC over finite GF(2) is employed. In this system, since the maximum-likelihood (ML) detection metric of the multiple access channel (MAC) at the relay is given by the sum of two exponential functions, it is not possible to utilize the classical Euclidean distance rule. To make the performance analysis tractable, we approximate the ML detection metric by adopting the max-log approximation. Then we derive tight upper and lower bounds in closed form for the average symbol error probability of the MAC at the relay. Finally, we obtain tight upper and lower bounds in closed form for the end-to-end average bit-error rate (BER).

We study relay selection (RS) with the analog network coding (ANC) and time division broadcast (TDBC), which are two major amplify-and-forward (AF)-based protocols in bidirectional relay networks. We consider a bidirectional network consisting of two different end-sources and multiple relays, where each terminal has a single antenna and operates in a half-duplex mode. In this network, a single best relay is selected depending on channel conditions to help bidirectional communication between the two end-sources. Specifically, we first consider RS schemes for the ANC and TDBC protocols based on a max-min criterion to minimize the outage probabilities. Then, for the RS in the ANC protocol, we derive a closed-form expression of the outage probability; for the RS in the TDBC protocol, we derive a one-integral form of the outage probability and its lower bound in closed-form. Numerical results confirm that the closed-form expression of the ANC protocol and the one-integral form of the TDBC protocol are very accurate, and that the closed-form lower bound of the TDBC protocol is also tight.

For the decode-and-forward protocol in relay networks, opportunistic relaying (OR) and selection cooperation (SC) are two major relay selection schemes, which have been studied only for single antenna terminals. We study OR and SC in a multi-antenna relay network where each terminal has multiple antennas. To fully exploit multiple antennas without incurring high feedback overhead, we adopt transmit antenna selection (TAS). Specifically, we first propose two joint relay-and-antenna selection schemes which combine OR and SC, respectively, with TAS: joint OR-TAS and joint SC-TAS. For each joint selection scheme, a single best transmit antenna at the source, a single best relay, and a single best transmit antenna at this selected relay are jointly determined in an optimum sense. In this network, at the first time slot, the selected antenna at the source transmits a symbol to the selected relay; at the second time slot, the selected antenna at the selected relay retransmits the detected symbol to the destination. We derive the outage probability of joint OR-TAS. Also, we obtain the outage probability of joint SC-TAS by proving that the outage probability of joint SC-TAS is identical to that of joint OR-TAS.

We study and compare the performance of three very typical bidirectional communication protocols based on the decode-and-forward relaying: time-division broadcast (TDBC), physical-layer network coding (PNC), and opportunistic source selection (OSS). We first derive the exact closed-form outage probabilities for the PNC and OSS protocols and an exact but one-integral form outage probability for the TDBC protocol. To gain more insight, we also derive closed-form asymptotic outage probability expressions for all protocols, with which we obtain further analytical results on the asymptotic optimal power allocation, the asymptotic optimal relay location, and the performance gain of the OSS protocol over the TDBC protocol in the high signal-to-noise ratio (SNR) regime. Finally, we study the diversity-multiplexing tradeoff (DMT) for each protocol both in the finite and infinite SNR regimes.

The outage probability of multi-hop amplify-and-forward relay systems with multiple relays is analyzed. Previously, the outage probability of the two-hop system with multiple relays had been analyzed. In this paper, that work is generalized by finding results for the outage probability of the multi-hop system, of which the previously found results for the two-hop system are special cases. In the two-hop system, signals from the source are forwarded by the relays to the destination only, whereas in the multi-hop system, signals are forwarded to other relays as well as to the destination. We derive a lower bound, which is extremely tight to the actual outage probability for almost all values of outage probability, and asymptotically tight in the high-SNR region, for all important practical cases. We also derive a high-SNR straight-line approximation that is extremely tight to the actual outage probability at very high SNRs, and often at moderately high SNRs as well, for all important practical cases.

In this paper, we combine opportunistic transmission and source/destination cooperation in a decode-and-forward (DF)-based two-hop cooperative diversity network consisting of multiple source-destination pairs and a single relay. Firstly, we consider a special scenario with two source-destination pairs. For this network, there are two possible strategies in each hop: no-cooperation or cooperation. Considering the combination of the two strategies in two cascaded hops, we investigate four different end-to-end transmission strategies, and we find that the four strategies complement one another depending on channel conditions in terms of outage performance. To maximize the mutual information, we propose an optimum joint selection of source-destination pair and end-to-end transmission strategy. Then we show that the optimum joint selection scheme can be simplified without loss of outage performance, and derive its exact outage probability in closed-form. Secondly, we generalize the proposed transmission strategy into a scenario with multiple source-destination pairs. For this network, we first propose an optimum end-to-end transmission strategy to maximize the mutual information, then a suboptimum end-to-end transmission strategy to reduce the signaling overhead and computational complexity. For the suboptimum strategy, we derive the outage probability and diversity order.

We study the optimum maximum-likelihood (ML) detection and sub-optimum detection for a multi-branch dual-hop cooperative diversity network with limited channel state information (CSI). Compared to the full CSI strategy, the signalling overhead at each relay involved with the limited CSI is reduced by 50%. We derive optimum ML detection with the limited CSI, which involves numerical integral evaluations. We also propose two closed-form sub-optimum detection rules of low complexity. It is shown that the first sub-optimum detection has almost identical performance to the optimum ML detection when Gaussianity in the added noise dominates, and the second sub-optimum detection has almost identical performance to the optimum ML detection when non-Gaussianity dominates. Finally, we propose a hybrid sub-optimum detection and demonstrate that its performance is almost identical to that of the optimum ML detection for general cases.

We study the optimum maximum-likelihood (ML) detection and sub-optimum detection with limited channel state information (CSI) for a multi-branch dual-hop cooperative diversity network which consists of a source, multiple relays, and a destination without a direct source-destination path. With the limited CSI, the signalling overhead at each relay is reduced by 50%. We first derive the optimum ML detection with the limited CSI, which involves numerical integral evaluations. To reduce the computational complexity, we then propose a closed-form suboptimum detection rule. It is demonstrated that the proposed sub-optimum detection rule performs almost identically to the optimum ML detection when the non-Gaussianity in the added noise component dominates.

We analyze the error performance of two-hop relay networks adopting frequency shift keying (FSK) over frequency flat Rayleigh fading channels. It is assumed that relay networks consist of a source, a relay, and a destination without a direct path signal from the source to the destination and the relay adopts the amplify-and-forward protocol with a fixed gain. Firstly, considering imperfect frequency and phase synchronization, we obtain the exact error probability expressions for noncoherent and coherent binary FSK (BFSK). Secondly, assuming perfect frequency and phase synchronization, we derive a closed-form error probability approximation for coherent M-ary FSK (MFSK). The proposed methods can also be used for the error performance analysis of classical one-hop FSK systems with perfect/imperfect frequency and phase synchronization. The obtained error probability expressions will help the design of two-hop relay networks adopting FSK in determining the system parameters such as the transmission power at the source, the amplifying coefficient at the relay, and the maximum affordable frequency and phase offsets to satisfy the required error performance.

We analyze the bit-error rate (BER) performance of the distributed Alamouti's code for cooperative diversity networks consisting of a source, two relays and a destination node over Rayleigh fading channels. It is assumed that the relays adopt the amplify-and-forward protocol. Firstly, assuming the existence of the direct path component from the source to the destination, we derive the exact BER expression in a one-integral form for M-pulse amplitude modulation (PAM) and M-quadrature amplitude modulation (QAM) constellations. We also present a series expansion of a very accurate BER approximation, which does not require any numerical calculation, and we prove this series is convergent. Secondly, considering the system where the direct path component does not exist, we obtain the exact BER expression in a one-integral form and a series expansion of the exact BER expression. Numerical results confirm that the two exact BER expressions in a one-integral form perfectly match the simulation results and the two series expressions of BER are very accurate, even with a small truncation window.

In this paper, we first propose an optimum relay ordering algorithm for the multi-branch multi-hop cooperative diversity networks. This optimum algorithm has a high complexity that makes it hard to implement. Therefore, a suboptimum relay ordering algorithm, which considerably reduces the complexity, is then developed. Furthermore, for a cooperative network with two relays, we analytically evaluate the performance of the suboptimum algorithm by using an approximate end-to-end signal-to-noise ratio expression. Specifically, an approximate probability of wrong selection and an approximate expression of the symbol error rate are derived. The analysis and the numerical results demonstrate that the suboptimum algorithm performs very well as the optimum one at a much lower complexity.

This paper focuses on error performance analysis of distributed Alamouti's code. Recently, many works have been devoted to the performance analysis of this code. In order to simplify the analysis, however, they either assumed the channels in the cooperative network had the same variances or only considered asymptotic error performance at high signal-to-noise ratio (SNR) range. In this paper, we study a general dissimilar cooperative network, where the channels have different variances. Two accurate approximate bit error rate (BER) expressions are proposed in order to evaluate the error performance of the distributed Alamouti's code. We also investigate how the values of channel variances affect the accuracies of the proposed approximate BER expressions. Our results demonstrate that the proposed approximate BER expressions are very close to the exact BER over the whole SNR range. Furthermore, we show that the average BER of the distributed Alamouti's code behaves like(ln(E)/E)2when the transmission powerEis sufficiently large.

We analyze the maximum-likelihood (ML) performance of the decode-and-forward protocol in a cooperative diversity network which consists of a source, a relay, and a destination with a direct path signal, but which is not equipped with cyclicredundancy- check (CRC) codes. In this system, due to a symbol error at the relay, the ML receiver at the destination needs to consider all the possible symbol detection scenarios at the relay as well as at the destination. Therefore, the ML detection metric is given by a linear combination of exponential functions, which prevents the use of the classical minimum Euclidean distance rule. Adopting the max-log approximation, we approximate the ML detection rule which makes the ML performance analysis tractable. In order to facilitate the derivation of decision regions, we simplify the ML detection rule in the two-dimensional real space such that two metric values of two adjacent constellation points are sequentially compared. Then we obtain decision regions in a form without union and intersection. Finally, based on the decision regions, we derive a very accurate closedform BER approximation for M-pulse amplitude modulation (PAM) and M-quadrature amplitude modulation (QAM). The obtained BER expression can serve as the error performance upper-bound of the decode-and-forward protocol in cooperative diversity networks.

For an important and standard rate-two quasi-orthogonal space-time block code, we propose a very fast detection scheme which achieves a wide range of tradeoff between performance and complexity. In the proposed scheme, exploiting the special quasi-orthogonal structure of the code, we efficiently obtain multiple candidate constellation points around the maximum likelihood (ML) point. Furthermore, we develop a very fast sorting method for the multiple candidate points to further improve the detection speed. Numerical results demonstrate that the proposed scheme can perform very close to the maximum likelihood detection at an extremely low complexity.

In this letter, the average bit-error rate (BER) performance is analyzed for uncoded decode-and-forward (DF) cooperative diversity networks. We consider two typical networks: a single-relay cooperative network with the direct source destination link and a two-relay cooperative network with the direct source-destination link, under dissimilar network settings, i.e., the fading channels of different relay branches may have different variances. We first derive a closed-form approximate average BER expression of binary signallings including noncoherent binary frequency shift keying (BFSK), coherent BFSK, and coherent binary phase shift keying (BPSK), for the single relay network. We then generalize our analysis to the two-relay network, and a closed-form approximate average BER expression for binary signallings is derived. We also show that our BER expressions can be considered as generalizations of previously reported results in the literature. Throughout our analysis, only one approximation, so-called the piecewise-linear approximation, is made. Simulation results are in excellent agreement with the theoretical analysis, which validates our proposed BER expressions.

We consider a single-branch, mobile multihop relaying (MMR) network with limited channel state information (CSI) at the destination. The considered CSI signalling strategy reduces the signalling overhead at each relay by at least 50%. We show that this significant overhead reduction comes at the expense of no performance loss at all when hard maximum likelihood detection is carried out at the destination. Furthermore, we consider the use of the MMR system with concatenated channel codes to carry out soft maximum a posteriori (MAP) detection, and demonstrate that with channel codes employed the optimum detection rule becomes prohibitively complex to implement. We propose two approximate soft MAP detection schemes to make the detection feasible for the system, and demonstrate that the performance is either almost identical to or slightly degraded from the ideal case with full CSI at the destination.

Network coding notions promise significant gains in wireless networks' throughput and quality of service. Future systems employing such paradigms are known to be also highly scalable and resilient to node failure and churn rates. We propose a simple framework where a single relay listens to two nodes transmitting simultaneously over the same band in the presence of Nakagami-m fading. For this multiple-access channel (MAC), we derive in closed-form the exact bit error rate of antipodal signaling with maximum-likelihood detection. As the MAC is the bottleneck in error of the overall system, this provides a good performance measure of the aggregate architecture. Using the new error expressions derived, we then propose a simple closed-loop cooperation strategy where via a ternary feedback from the relay node, significant gains in signal to noise ratio at the relay can be achieved. Our novel error analysis method is applicable to a number of other systems such as the vertical Bell labs spacetime (V-BLAST) scheme and synchronous multi-user systems.

In this paper, a new opportunistic source selection (OSS) protocol is studied in bidirectional cooperative networks. Unlike existing protocols, this protocol exploits multiuser nature of the bidirectional cooperative networks and it opportunistically supports two traffic flows based on instantaneous channel conditions. This makes the OSS protocol much more reliable than existing protocols. In order to show the performance improvement, we first derive a lower bound of the outage probability of the OSS protocol. Numerical results demonstrate that this lower bound is extremely tight and it indicates that the OSS protocol achieves full diversity order two in a bidirectional cooperative network with two sources and one relay. Then exact and approximate lower bounds of average bit error rates (BERs) at both sources in the OSS protocol are derived. Those lower bounds are very close to the exact average BERs as shown by numerical results. Lastly, an optimum power allocation scheme is developed for the OSS protocol. This scheme can optimize the outage probability, average BER, and data-rate of the OSS protocol at the same time.

This letter focuses on the broadcasting phase of bidirectional cooperative networks with multiple decode-andforward relays. In this phase, the relays first combine the information-bearing symbols transmitted by the sources, and then broadcast them back to the sources in order to achieve bidirectional communications. Two different combining methods at the relays are considered. The first one is that the relays transmit linear combinations of the information-bearing symbols to the sources by beamforming. We develop an algorithm that can compute the optimum beamforming vector in closed form and this beamforming vector minimizes the outage probability of the bidirectional cooperative network. The second method is that the relays combine the information-bearing symbols by exclusive-or and then transmit them to the sources by beamforming. For this case, we show that the instantaneous signal-to-noise ratios at the sources depend on the values of the information-bearing symbols. Based on [1], the optimum beamforming vector is computed and it minimizes an upper bound of the outage probability of the bidirectional cooperative network.

Very recently, we proposed the row-monomial distributed orthogonal space-time block codes (DOSTBCs) in [1] and showed that the codes achieved approximately twice higher bandwidth efficiency than the repetition-based cooperative strategy. In [1], we assumed that the relays did not have any channel state information (CSI) of the channels from the source to themselves, i.e. the channels of the first hop. However, we notice that this CSI can be readily obtained at the relays without any additional pilot signals or any feedback overhead. Therefore, in this paper, we assume that the relays have partial CSI of the first hop and use this information to construct the codes. We refer to those codes as the row-monomial DOSTBCs with channel phase information (DOSTBCs-CPI) and derive an upper bound of the data-rate of the codes. This upper bound suggests that the row-monomial DOSTBCs-CPI have higher bandwidth efficiency than the row- monomial DOSTBCs in [1], especially in a cooperative network with many relays. Furthermore, we find the actual row-monomial DOSTBCs-CPI achieving the upper bound of the data-rate.

Very recently, we proposed the distributed orthogonal space-time block codes (DOSTBCs) in [1]. We showed that the DOSTBCs achieved the single-symbol maximum likelihood (ML) decodability and the full diversity order. Furthermore, we studied some special DOSTBCs, namely the row- monomial DOSTBCs, which generated uncorrelated noises at the destination. We showed that the row-monomial DOSTBCs achieved approximately twice higher bandwidth efficiency than the repetition-based cooperative strategy. However, the data-rate of the DOSTBC was not analyzed in [1]. In this paper, we consider the general DOSTBCs, which possibly generate correlated noises at the destination. We derive an upper bound of the data- rate of the DOSTBC. This upper bound is larger than that of the row-monomial DOSTBC, and hence, the DOSTBCs can potentially improve the bandwidth efficiency of the cooperative network.

The maximum-likelihood (ML)-detected vertical bell-labs layered space-time (V-BLAST) architecture over a Nakagami-m fading channel is considered. This fading model encompasses many fading scenarios as special cases (e.g., Rayleigh). The ML detection of BLAST systems waives the requirement of not having smaller number of receive antennas than transmit antennas: a primary limitation using other sub-optimal linear processing receivers. Using exact BER expressions, a two-by-one antipodal V-BLAST system is optimized by phase precoding requiring only a single bit of feedback from the receiver. Numerical results demonstrate that the proposed single-bit feedback scheme improves the BER performance dramatically.

We consider very general code-division-multiple- access (CDMA) systems, namely multiple-chip-rate CDMA systems, where signals can be transmitted at different chip rates, carrier frequencies, processing gains, and transmitted powers to satisfy the given quality of service (QoS) requirements. Also, nonzero and possibly different carrier frequency offsets are assumed for different users. For these systems, a closed-form bit error rate (BER) expression is derived based on the simplified improved Gaussian approximation. Numerical result demonstrates that the proposed method provides much more accurate BER values compared to the standard Gaussian approximation.

A fast maximum-likelihood (ML) detection scheme is proposed for the important quasi-orthogonal Space-Time Block Codes (STBCs) of symbol rate 2 for three and four transmission antennas. In particular, the proposed scheme is applicable even to the under-determined system where the number of transmission antennas is larger than that of the receive antennas. The half of the symbols of the transmission matrix are detected by two dependent double-layer sphere decoders, which are referred to as dual double-layer sphere decoding in this paper. The remaining half of the symbols are detected by single-symbol detection, of which complexity is extremely low. Numerical results demonstrate that the proposed new detection method improves the detection speed up to ten times depending on the Signalto- Noise Ratio (SNR) compared to the state-of-the-art scheme.

The closed-form exact bit error rate (BER) expressions of generalized orthogonal space-time block codes (OSTBCs) are first derived for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations in spatially white Rayleigh multiple input multiple output (MIMO) channels. Then this analysis is generalized to arbitrarily spatially correlated MIMO channels. Based on the BER analysis, the transmission power of the generalized OSTBCs is optimized such that the average BER is minimized. Furthermore, the optimum transmit power which maximizes the upper-bound of the capacity is derived in closed-form.

In this paper, we focus on the diversity order of the decode-and-forward (DF) cooperative networks with relay selection. Many detection schemes have been proposed for the DF; but it has been shown that the cooperative maximum ratio combining (C-MRC) can achieve almost the same performance as the optimum maximum likelihood detector and has a much lower complexity. Therefore, we first combine the C-MRC with the relay selection and show that it achieves the full diversity order by deriving an upper bound of its average bit error rate (BER). In order to reduce the signaling overhead, we then combine the link-adaptive regeneration (LAR) with the relay selection. By deriving an upper bound of the average BER, we show that, when there are two relays, the diversity order of the LAR with relay selection is upper-bounded by three and lower-bounded by 3 - epsiv, where xi is an arbitrarily small positive number.

In this paper, we focus on the decode-and-forward (DF) cooperative networks with relay selection. Many detection schemes have been proposed for the DF; but it has been shown that the cooperative maximum ratio combining (C-MRC) can achieve almost the same performance as the optimum maximum likelihood detector and has a much lower complexity. Therefore, we first combine the C-MRC with the relay selection and show that it achieves the full diversity order by deriving an upper bound of its average bit error rate (BER). In order to reduce the signaling overhead in the C-MRC with relay selection, a novel detection scheme, namely product MRC (P-MRC), is proposed for the DF and it achieves the same diversity order as the C-MRC. Then we combine the P-MRC with the relay selection and show that it achieves the full diversity order by deriving an upper bound of its average BER.

We jointly optimize the relay-precoders and decoders with full or partial channel side information (CSI) in a cooperative network. Specifically, three different CSI assumptions are considered: 1) full CSI at the destination terminal and the relay terminals; 2) full CSI at the destination terminal and partial CSI at the relay terminals; 3) partial CSI at the destination terminal and the relay terminals. We show that, under the assumption of full CSI at the destination terminal and the relay terminals, the optimum relay-precoder is the cooperative transmission beamforming and the optimum decoder is a maximum ratio combiner. Under the two partial CSI assumptions, the optimum relay-precoders and decoders work in a fashion of channel selection. It is demonstrated that the proposed optimum relay-precoders and decoders improve the performance considerably

In this correspondence, the distributed orthogonal space-time block codes (DOSTBCs), which achieve the single-symbol maximum likelihood (ML) decodability and full diversity order, are first considered. However, systematic construction of the DOSTBCs is very hard, since the noise covariance matrix is not diagonal in general. Thus, some special DOSTBCs, which have diagonal noise covariance matrices at the destination terminal, are investigated. These codes are referred to as the row-monomial DOSTBCs. An upper bound of the data-rate of the row-monomial DOSTBC is derived and it is approximately twice higher than that of the repetition-based cooperative strategy. Furthermore, systematic construction methods of the row-monomial DOSTBCs achieving the upper bound of the data-rate are developed when the number of relays and/or the number of information-bearing symbols are even.

The bit-error rate (BER) performance of orthogonal space-time block codes (STBCs) in correlated multiple-input multiple-output (MIMO) channels is studied. We first derive closed-form exact BER equations for pulse amplitude modulation, quadrature amplitude modulation, and phase-shift keying constellations in a correlated Rayleigh MIMO channel. The BER expressions can be easily evaluated without any numerical methods. Then we consider correlated MIMO channels where the line of sight components exist and they suffer from shadowing, namely, correlated shadowed Rician MIMO channels. For three practical channel scenarios, the BER is analyzed, and closed-form BER equations are presented.

We construct nonuniform phase-shift keying (PSK) constellations that provide unequal error protection for multiclass traffic such as compressed voice and video data. Then closed-form expressions expression for the exact bit-error rate (BER) of the nonuniform PSK constellations is derived in multiple receive-antenna systems over Rayleigh fading channels. Based on this BER analysis, we optimize the nonuniform PSK constellations such that the BERs of all the bits of each class are equalized or such that the total transmission power is minimized subject to the average BER constraints. In particular, we demonstrate that even for transmitting single-class traffic, the optimized nonuniform PSK constellations can be better than the conventional uniform PSK. Finally, we extend the nonuniform PSK constellations to space-time coded communications systems with multiple transmit and multiple receive antennas.

Space-time codes can be decoded by the sphere decoding (SD) algorithm to reduce the complexity and retain maximum-likelihood (ML) performance. In this letter, the ML metric of quasi-orthogonal space-time block codes is written into two independent Euclidean norms, thus SD can be applied to each function independently. The new scheme reduces the complexity by at least 85% for systems with four or more transmit antennas, compared with the conventional SD algorithm.

The bit error rate (BER) performance of orthogonal space-time block codes (STBC) in correlated multiple input multiple output (MIMO) channels is studied. We derive closed-form exact BER equations for pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and phase shift keying (PSK) constellations in a correlated Rayleigh MIMO channel. The BER expressions can be easily evaluated without any numerical methods.

In order to improve the throughput of the opportunistic beamforming, we generalize the opportunistic beamforming by using multiple random weighting vectors at each time slot. The base station chooses the best weighting vector and performs the opportunistic beamforming with this optimum vector. For the case of equally strong independent fast Rayleigh fading channels, we analytically approximate the throughput of the proposed scheme and obtain the optimum number of random weighting vectors per time slot. Numerical results demonstrate that the proposed scheme considerably improves the throughput compared to the conventional opportunistic beamforming for a low/realistic number of users.

Optimum and suboptimum scheduling schemes are proposed in the uplink of array antenna systems. We assume that once a user is permitted to transmit the data, the user transmits the data using the maximum power, and adjusts the transmission bit rate so that the energy-per-bit-to-interference-plus-noise-density ratio should be equal or higher than a threshold. In this system model, we consider an optimization problem: How many and which users should be selected to transmit their data at a time in order to maximize the throughput? Based on the analysis on the complexity of the optimum scheme, we propose another optimum scheme having reduced complexity. To reduce the complexity further, we also propose three suboptimum scheduling schemes by limiting the number of users simultaneously transmitting, by choosing the users with higher channel gains, and by partitioning a cell into multiple parts. In circular and linear arrays, the performance results of all the proposed schemes are presented and compared.

In order to improve the throughput of the opportunistic beamforming, the authors generalize the opportunistic beamforming by using multiple random weighting vectors at each time slot. The base station chooses the best weighting vector and performs the opportunistic beamforming with this optimum vector. For the case of equally strong independent fast Rayleigh fading channels, the throughput of the proposed scheme is analytically approximated and the optimum number of random weighting vectors per time slot is obtained. Numerical results demonstrate that the proposed scheme considerably improves the throughput compared to the conventional opportunistic beamforming for a low/realistic number of users.

Based on a zero-forcing with successive interference cancellation (ZFSIC) method, we construct variable-rate space-time block codes (STBCs) for two, three, and four transmit antennas in Rayleigh fading channels. Considering the error-propagation effect in the ZFSIC scheme, we analyze the bit-error rate (BER) and optimize the transmit power so that the average BER is minimized. Unlike the approach based on zero-forcing (ZF), the method adopted in this paper can construct variable-rate STBCs even when the receive antennas are fewer than the transmit antennas. In addition, to improve the BERs further, we propose space-time power optimization. Numerical results show that the codes presented in this paper provide much lower BERs, compared with the codes developed by Kim and Tarokh.

An optimum transmit power management scheme is proposed for wireless video service in code-division multiple-access systems. The scheme minimizes the transmit power subject to the constraint on the distortion resulted from the loss of each packet and the error propagation effect caused by the motion compensation. It implicitly controls the target bit-error rate (BER) of the video packet according to the importance of the packet. The more important the packet is, the more securely it is transmitted over noisy channel. Furthermore, the scheme adjusts the target BER as if the water filling concept is applied to the problem of minimizing the transmit power. The simulation results show that the proposed scheme considerably outperforms the conventional schemes.

We consider a multiple antenna system when combined array processing with space-time coding is used. We present variable rate space-time block codes for two, three, and four transmit antennas and optimize the transmit power so that the average bit-error rate (BER) is minimized. Numerical results show that this optimum power allocation scheme provides significant gain over the equal power allocation scheme. We then classify all the variable rate space-time block codes having the same code rates and identify the unique code that achieves the lowest BER. We explicitly compute the performance of the variable rate codes over a Rayleigh-fading channel. The proposed variable rate space-time block codes are useful for unequal error protection in multiple transmit antenna systems.

Transmission bit rates are optimized for two-class traffic in variable spreading gain code-division multiple-access systems with antenna arrays. In an array antenna system, the interference levels experienced by the users belonging to different beams are not the same. Thus, it is not efficient to allocate the same rates to all the data users even though they belong to a cell. Considering this, an optimum rate allocation scheme is proposed for delay-tolerant data users. Additionally, we also propose the optimum rate allocation scheme for voice and data users when a packet scheduling scheme is considered. Numerical results show that, in array antenna systems, the proposed schemes considerably outperform the conventional scheme designed for omniantenna systems.

A power-distortion (P-D) optimized coding mode-selection scheme is proposed for variable bit rate videos in wireless code-division multiple-access (CDMA) systems. Unlike the conventional rate-distortion (R-D) optimized mode-selection methods, the proposed scheme takes into account time-varying channels. We show that the proposed optimization problem can be represented as the conventional R-D mode-selection problem, in which the Lagrangian multiplier depends on the channel conditions. In the proposed scheme, as the link gain of a user increases, the optimum mode for the user tends to be intramode rather than intermode, and vice versa. Numerical results show that, in CDMA systems, the proposed scheme consumes much less power at the same image quality compared with conventional R-D schemes.

An optimum power management scheme is proposed for intra-frame refreshed image sequences of the wireless video service in code-division multiple-access (CDMA) systems. The end-to-end distortion of H.263 video data is first modeled when the error concealment schemes are employed. This distortion model takes into account the error propagation effects caused by the motion compensation. Then, based on the model, the target bit-error rates (BERs) of the image frames are optimized in such a way that the consumed power could be minimized under the constraint of maximum distortion. To satisfy the specified target BER requirement, an optimum power management scheme is proposed. Simulation results show that the proposed scheme considerably outperforms the conventional scheme in the sense of the decoded image quality. Additionally, the effects of the imperfect power control on the performance are also investigated.

We consider transmission using N transmit and reception using M receive antennas in a wireless environment assuming that neither the transmitter nor the receiver knows the channel coefficients. For the scenario that the transmission employs noncoherent T /spl times/ N unitary space-time codes and for a block-fading channel model where the channel is static during T channel uses and varies from T channel uses to the other, we establish the bound r /spl les/ min(T-N, N) on the diversity advantage rM provided by the code. In order to show that the requirement r /spl les/ min(T-N, N) cannot be relaxed, for any given R, N, T, and r /spl les/ min(T-N, N), we then construct unitary T /spl times/ N space-time codes of rate R that guarantee diversity advantage rM. Two constructions are given that are also amenable to simple encoding and noncoherent maximum-likelihood (ML) decoding algorithms.

A new resource allocation scheme is proposed for video service over a Gaussian channel. For video quality rating, we introduce a suitable metric: the average peak signal-to-noise ratio (PSNR) or the average channel-induced PSNR degradation. Then, we model the end-to-end distortion considering the error propagation effect caused by motion compensation. On the basis of this model, we propose an efficient resource allocation scheme for real video service. Unlike conventional schemes, the new scheme satisfies the PSNR requirement of each video user by adjusting the bit-error rate level with the changes of image characteristics. Simulation results show that the proposed scheme utilizes the bandwidth more efficiently than conventional schemes.