This paper proposes a hybrid user group (HUG) scheme for simultaneous transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-aided millimeter wave (mmWave) cell-free massive multiple-input multiple-output (CF mMIMO) systems using rate-splitting multiple access (RSMA), where users in transmission and reflection zones are optimally paired. Nearby APs serve users, and multiple STAR-RISs enhance signals under imperfect SIC. We formulate a max-min spectral efficiency (SE) problem to jointly optimize power allocation, STAR-RIS phase shifts, and user grouping, leading to a mixed-integer non-convex problem. To solve it, we relax discrete variables and decompose the problem into sub-problems, using bisection search for phase shifts and a low-complexity iterative algorithm for power allocation. Simulations show the HUG scheme improves average SE by 14.03%, 18.79%, and 33.42% compared to random grouping, conventional beamforming, and HUG with conventional RIS, respectively.

Massive multiple-input multiple-output (MIMO) stands as a key technology for advancing performance metrics such as data rate, reliability, and spectrum efficiency in the fifth generation (5G) and beyond of wireless networks. However, its efficiency depends greatly on obtaining accurate channel state information (CSI). This task becomes particularly challenging with increasing user mobility. In this paper, we focus on an uplink scenario in which a massive MIMO base station serves multiple high-mobility users. We leverage variational Bayesian (VB) inference for joint channel estimation and data detection (JED), tailored for time-varying channels. In particular, we use the VB framework to provide approximations of the true posterior distributions. To cover more real-world scenarios, we assume the time correlation coefficients associated with the channels are unknown. Our simulations demonstrate the efficacy of our proposed VB-based approach in tracking these unknown time correlation coefficients. We present two processing strategies within the VB framework: online and block processing strategies. The online strategy offers a low-complexity solution for a given time slot, requiring only the knowledge of the parameters/statistics within that time slot. In contrast, the block processing strategy focuses on the entire communication block and processes all received signals together to reduce channel estimation errors. Additionally, we introduce an interleaved structure for the online processing strategy to further enhance its performance. Finally, we conduct a comparative analysis of our VB approach against the linear minimum mean squared error (LMMSE), the Kalman Filter (KF), and the expectation propagation (EP) methods in terms of symbol error rate (SER) and channel normalized mean squared error (NMSE). Our findings reveal that our VB framework surpasses these benchmarks across the performance metrics.

This paper proposes an adaptive user grouping (AUG) scheme for short-packet communication (SPC) in simultaneous transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) systems with SWIPT. The information users with different channel conditions are optimally grouped while the energy user harvests the energy from the base station. Besides that, multiple STAR-RISs are deployed to assist the information users in improving the quality of received signals. We formulate the spectral efficiency (SE) maximization of the considered system to optimize the linear precoding matrix, phase shift of the reflection and transmission at STAR-RIS, energy beamforming matrix, and grouping variables. The formulated problem leads to a mixed binary integer programming which is challenging to solve optimally. To tackle this problem, we first relax the integer variable to be continuous and decouple the relaxed problem into two subproblems to alternately tackle the phase shift and beamforming parts. We then propose bisection search and low-complexity iterative algorithms to solve the phase shift and beamforming subproblems with guaranteed convergence at a relative optimum of each subproblem. Towards real-time optimization, we develop a convolutional neural network (CNN) to achieve the optimal solution of the relaxed problem via a quick-inference process. Numerical results demonstrate a SE improvement of 46% in the AUG scheme over the random user grouping one and 78% over the non-user grouping under various settings. Furthermore, the developed CNN model predicts optimal phase shift variables and beamforming matrices with high accuracy compared to conventional methods but in a shorter time.

In this paper, we propose a best node and friendly jammer (bN-fJ) scheme for secure short-packet multi-hop communications in Internet-of-Things (IoT) networks. Under imperfect channel state information (CSI) conditions, the best IoT node is chosen for data transmission and a friendly jammer is chosen later to confuse the received signals at a multi-antenna eavesdropper. Approximate and asymptotic closed-form expressions for the secrecy throughput of the bN-fJ scheme are obtained, offering valuable insights into the system designs. Numerical results show that the proposed bN-fJ scheme achieves much better performance than benchmarking schemes, especially with 1 more bit/channel use and 9.4 dB enhancement in communication reliability measure. Moreover, under imperfect CSI and large antennas at the eavesdropper, the secrecy throughput and communication reliability of the system can be improved by employing the proposed node selection, e.g., a 12.5 dB improvement at 10 antennas at Eve and the imperfect CSI of 0.8. Finally, the secrecy throughput is presented as a concave curve for the number of hops and blocklengths, which enables us to identify the optimal hops and blocklength for secure multi-hop short-packet transmissions.

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to efficiently determine satellites for users to associate with and activate the best cluster of beams together with optimizing LP for every satellite-to-user transmission. These technical objectives are first framed as a complex mixed-integer programming (MIP) challenge. To tackle this, we reformulate it into a joint cluster association and LP design problem. Then, by theoretically analyzing the duality relationship between downlink and uplink transmissions, we develop an efficient iterative method to identify the optimal solution. Additionally, a simpler duality approach for rapid beam selection and LP design is presented for comparison purposes. Simulation results underscore the effectiveness of our proposed schemes across various settings.

Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of domain knowledge in the few-bit quantization process. While the first data detection network, B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the indeterminant gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into the weight matrices of its channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gains in estimation accuracy by the proposed deep learning framework.

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.

Low-resolution Analog-to-Digital Converters (ADCs) have emerged as a practical solution for reducing cost and power consumption for massive Multiple-Input Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation is formulated as an SVM problem, and then a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. Finally, we propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a nonlinear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal designed specifically for the MIMO channel model under consideration.

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.

This paper studies the impacts of low-bit quantization to wideband signal reconstruction with a sub-Nyquist sampling scheme Modulated Wideband Converter (MWC). Several real types of analog-to-digital converters (ADCs) are firstly simulated to the MWC and their results on wideband signal reconstruction are compared based on the low-bit criterion of the ADCs. Assessing the wideband reconstruction performances, this study proposes a trade-off between the oversampling factor, the number of bits and the type of quantizer, which can achieve the performance of reconstruction as close as possible to the ideal reconstruction.

This work addresses the sum-capacity-achieving signaling schemes and the sum-capacity of a multiple access channel (MAC) with two mobile users communicating to a base station equipped with 1-bit quantizers. We consider Rayleigh fading channels between the users and the base station where channel state information (CSI) is known only at the base station. Towards this end, we first establish a necessary and sufficient condition refereed to as Kuhn-Tucker condition (KTC) on the input distribution of one user for a given input signal used at another user so that the input/output mutual information (MI) is maximized. By relaxing the power constraint and establishing upper bounds on the MI, we demonstrate that the power constraint of the first user is active. Then using Fubini-Tonelli theorem to exchange the order of integrations between fading and input distributions, and exploiting novel bounds on the output distribution and a related relative entropy, it is shown that the optimal input distribution has a bounded amplitude. Due to the symmetry of the problem, it is then concluded that the sum-capacity-achieving amplitude distributions are bounded, and both users must use full power to achieve the sum-capacity. Next, we exploit the independence of the amplitude and phase of fading gains to show that the optimal inputs are π/2 circular symmetric. Building upon the results on the amplitude and phase, it is then demonstrated that any π/2 circular symmetric input distribution having a constant amplitude is sum-capacity achieving. The sum-capacity is finally obtained in a precise form.

This paper investigates the optimal signaling schemes and capacities of non-coherent correlated multiple-input single-output (MISO) channels in fast Rayleigh fading. We consider both channels under per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. For per-antenna power constraint channels, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of optimal input distribution and the uniqueness of optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution, with a mass point located at the origin. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine the capacity gain of MISO over SISO via the phase solutions of a constrained quadratic optimization problem on a sphere, which can be obtained using a proposed penalized optimization algorithm. We also extend the results to MISO channels subject to the joint per-antenna and sum power constraints. Under this consideration, it is shown that not all per-antenna constraints are active. While the finiteness and discreteness of the optimal effective magnitude and the optimal input vector distributions still hold, the optimal phases and the optimal power allocation among the transmit antennas need to be determined simultaneously via a quadratic optimization problem under inequality constraints. These solutions can finally be used to obtain the MISO capacity gain.

Low-resolution analog-to-digital converter (ADC) has been considered as a promising solution to save power and cost in communication systems using high bandwidth and/or multiple RF chains. The goal of this work is to address the design of optimal signaling schemes and establish the capacity limit of Rician fading channels with low-resolution output quantization. This fading channel can be used to accurately model a wide range of wireless channels with the line-of-sight (LOS) components, including emerging mm-wave communications. The focus is on non-coherent fast fading channels where neither the transmitter nor the receiver knows the channel state information (CSI). By examining the continuity of the input-output mutual information, the existence of the optimal input signal is first validated. Then, considering the case of 1-bit ADC, we show that the optimal input is$\pi /2$circularly symmetric. A necessary and sufficient condition for an input signal to be optimal, which is referred to as the Kuhn-Tucker condition (KTC), and Lagrangian optimization problem are then established. By exploiting the novel log-quadratic bounds on the Gaussian$Q$-function, it is then demonstrated that for a given mass point’s amplitude, the corresponding rotated mass points through the phase of LOS component must form a square grid centered at zero. Furthermore, the amplitude of the mass points in the optimal distribution can take on only one value. As a result, the capacity-achieving input with 1-bit ADC is a rotated quadrature phase-shift keying (QPSK) constellation, and the rotation angle depends on the Rician factor. The characterization of the optimal input has also been extended to the case of multi-bit ADCs. Specifically, it is shown that for a$K$-bit ADC, the optimal input is discrete having atmost$2^{2K}$mass points. In both the cases of 1-bit and$K$-bit ADCs, the channel capacities are established in closed-form.

This paper examines the problem of blind detection in multiple-input-multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) using learning approaches. Recently, the use of low-resolution ADCs has been considered an effective technique to mitigate the issue of power consumption in millimeter-wave transceivers. One serious problem caused by the low-resolution ADCs is the significant distortion of received signals, resulting in difficulty of obtaining Channel State Information (CSI) at both transmitter and receiver sides. The primary motivation of our work is that learning the input-output relation can help mitigate the impact of CSI unavailability. In both supervised and semi- supervised methods that we propose, a sequence of pilot symbol vectors is used as the initial training data for the learning task. The idea of the supervised learning method is in typical communications systems, cyclic redundancy check (CRC) is used, and thus correctly decoded symbols confirmed by CRC can be exploited as supplementary training data to improve the detection accuracy. In the semi-supervised learning method, the to-be- decoded data is exploited to help the learning process, and so no further training information is required except the pilot symbol vectors. Simulation results show that the two proposed learning methods outperform existing detection techniques.

This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.

This paper aims to study an energy-efficiency (EE) maximization hybrid precoding (HP) design for mmWave multi-user (MU) systems where the analog precoding (AP) matrix is realized by a number of switches and phase shifters so that a connection between an RF chain and a transmit antenna can be switched off for energy saving. By explicitly considering the effect of each connection on the required power of digital precoding (DP) and AP design process, we describe the total power consumption as a sparsity form of the AP matrix. Together with the novel sparsity-modulus constraints of AP matrix, these sparsity terms make our system EE maximization (SEEM) problem be non-convex and challenging to solve. To tackle the SEEM problem, we first transform it into a subtractive-form weighted sum rate and power (WSRP) problem. We then exploit an alternating minimization of the mean-squared error algorithm to solve the WSRP problem where the DP vectors and AP matrix are updated alternatively, and a compressed sensing-based re-weighted quadratic- form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints.

This paper explores joint design of two-timescale hybrid RF-baseband precoding with minimum-mean-square-error (MMSE)-vector perturbation (VP) for multi-user massive multiple-input multiple-output systems, where users on the downlink are separated into geographical clusters, and each user cluster experiences identical transmit spatial correlation. Considering the perfect effective channel state information-based MMSE-VP at baseband, the spatial correlation-based RF precoder design is formulated as orthonormality-constrained stochastic optimization problems, where the objective functions cannot be characterized in closed form. RF eigen-beamforming is shown as an optimal solution for single-cluster transmission. In multi-cluster scenarios, mathematically tractable lower bounds are proposed and numerically optimized by trust-region Newton methods on Riemannian manifolds. Additionally, constant-modulus RF precoding based on the discrete Fourier transform (DFT) codebook is addressed. By recognizing the objective functions as a difference of increasing functions, branch-reduce-and-bound techniques are developed to find the globally optimal solutions to such combinatorial problems with reduced computational complexity. Simulation results demonstrate that the proposed nonlinear hybrid schemes deliver a superior bit error rate to other state-of-the-art baselines. The effectiveness of the suboptimal DFT-based RF solutions is also verified.

Wireless virtualization has emerged as a promising technology to support a diverse multitude of wireless networks due to their efficient exploitation of network resources. In a mobile virtualized network, a network operator (NO) benefits from dynamically leasing its physical resources to multiple service providers (SPs). The SPs then make profits from offering wireless services to their end users. A key challenge for the NO is to captivate the SPs into the leasing and efficiently allocate its wireless resources. To achieve the maximum profit for the NO, specialized trading mechanism needs to be designed, especially when the NO is unable to access SPs' private information. This paper proposes a framework of contract-based allocation for efficient spectrum leasing under incomplete information. First, a general system model is developed with one NO and multiple SPs engaged in an exchange for spectrum resources and money. The NO provides spectrum leases in both downlink and uplink at different multiplexing gains. Optimal trading contracts are then derived to maximize the total utility at the NO, while satisfying the SPs' requirements for the trade. Numerical results confirm our analyses on the optimal contracts and their benefits, including higher utilities and lower spectrum usages for the NO.

This paper aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN). Here, data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. Both data sharing and compression-based strategies are considered for fronthaul data transfer. New mixed-integer nonlinear problems are formulated, in which the ratio of network throughput and total power consumption is maximized. These challenging problem formulations include practical constraints on routing, predefined minimum data rates, fronthaul capacity, and maximum remote radio head transmit power. By employing the successive convex quadratic programming, iterative algorithms are proposed with guaranteed convergence to the Fritz John solutions of the formulated problems. Significantly, each iteration of the proposed algorithms solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization designs markedly improve the C-RAN's energy efficiency compared to benchmark schemes. They also show that the fronthaul data-sharing strategy outperforms its compression-based counterpart in terms of energy efficiency, in both single-hop and multi-hop network scenarios.

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. Two two-stage solution approaches are proposed. In the first approach, a fully digital precoder (FDP) is optimized considering the computation sparsity constraint in the first stage. In the second approach, the sparsity constraint is only imposed in the second stage. To find the FDP, we employ the minimization of the weighted mean-squared error and the ℓ1-reweighted methods to tackle the non-convex objective function and sparsity constraints, respectively. In the second stage of each approach, we exploit an alternating weighted mean-squared error minimization algorithm to reconstruct HP's based on the FDP found in the first stage. Two novel analog precoding designs, namely semi-definite-relaxation-based and projected-gradient-descent-based, are then proposed to optimize the analog part of the obtained HP's. We also study the impacts of various system parameters on the system sum-rate and provide resource provisioning insights for HP systems. Numerical results show the superior performances of the proposed designs over joint SA and HP benchmark algorithms.

The IEEE 802.11ad waveform can be used for automotive radar by exploiting the Golay complementary sequences in the preamble of a frame. The performance of radar, however, is limited by the preamble structure. In this paper, we propose an adaptive preamble design that permits a trade-off between radar parameters' estimation accuracy and communication rate. To quantify this trade-off, we propose a minimum mean square error (MMSE) metric based on rate distortion theory. The simulation results demonstrate that by adapting the preamble structure, we can achieve decimeter-level range mean square error (MSE) per symbol duration and gigabit per second (Gbps) data rates simultaneously for a distance upto 280 m.

This paper investigates the processing of delay and Doppler information with IEEE 802.11p OFDM signaling for multi-target detection. We study the feasibility of extending IEEE 802.11p short-range communication (DSRC) in vehicles to automotive radio detection and ranging (radar) functionality. By exploiting the unique structure of 802.11p OFDM packets over multiple subcarriers and multiple time-slots, we apply the estimation of signal parameters via rotational invariance technique (ESPRIT) for concurrent multi-target detection and range/velocity estimation. Numerical results show sub-0.2m accuracy in range estimation and sub-0.02m/s accuracy in velocity estimation with high probability.

This paper examines design of mixed-timescale hybrid precoding with vector perturbation (VP) to achieve minimum mean square errors (MMSE) for multi-user massive multiple-input multiple-output (MIMO) systems. In particular, equipped with the perfect effective channel state information (CSI)-based MMSE-VP solution at the baseband, we derive partial CSI-based linear radio frequency (RF) precoding as the solution to a stochastic programming problem. In the scenario of single-cluster transmission, RF eigen-beamforming in the transmit correlated subspace is proved to be optimality-achieving. In the multi-cluster case, by approximating the discrete constellation as uniformly distributed, we establish a closed-form lower bound for the objective function. A critical point to this bound is found by a trust-region method on a smooth Riemannian manifold. Simulation results demonstrate that in the former case, a limited number of RF chains suffices for the nonlinear hybrid scheme to outperform the fully digital linear solution, and approaches the performance of the traditional MMSE-VP solution in terms of bit error rate. In the latter case, the proposed solution delivers a superior error performance to the state-of-the-art baselines.

This paper studies hybrid precoding (HP) for mmWave multi-user OFDMA systems with sub-carrier allocation (SA) consideration. Constrained by a computation limit on the total number of data streams that can be processed, we aim to jointly optimize the SA and HP design to maximize the system sum-rate. This optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. We then propose two-stage solution approach to tackle the problem. In stage one, we optimize the fully digital precoding (FDP) considering the computation sparsity constraint. In the second stage, we exploit an alternating MMSE minimization algorithm to reconstruct the HP's based on the achieved FDP. A novel analog precoding design, namely “Projected-Gradient-Descent based”, is then proposed to optimize the analog part of the HP's.

This paper investigates the secrecy rates and optimal power allocation schemes of a decode-and-forward (DF) wiretap relay channel where a relay operates in a full-duplex (FD) mode. A practical self-interference model is adopted to take into account the effect of residual self-interference. At first, we demonstrate that while the optimal power allocation schemes between the source and the relay are non-convex, closed-form solutions can still be established under different power constraints. An asymptotic behavior of the solutions is then provided to shed important insights on the derived power allocation schemes. Specifically, by using the method of dominant balance, we show that the relay should use full power when its power budget is sufficiently small as compared to power budget at the source. Otherwise, the optimal power consumed at the relay approaches a constant to effectively handle the residual self-interference. The analysis is also helpful to demonstrate that the secrecy capacity of the considered full-duplex relay system is twice as much as that of the half-duplex system. In addition, numerical results reveal that DF relaying provides significantly higher secrecy rate over Amplify-and-Forward (AF) relaying.

This paper studies joint design of mixed-timescale hybrid precoding and combining to maximize the effective capacity for wireless backhaul in massive multiple-input multiple-output (MIMO) systems. Specifically, radio frequency (RF) analog processing is adaptive to statistical channel state information (CSI) while digital baseband processing is updated with instantaneous effective CSI. Equipped with traditional MIMO solutions at the baseband, the issue of RF design for both unconstrained-modulus and constant-modulus elements is addressed. Under the jointly correlated channel model, the objective function does not have a closed-form expression. In the unconstrained case, we derive the optimal RF solution structures, which lead to a combinatorial eigenmode selection formulation. Such an NP-hard problem is solved to near-optimality by semi-definite relaxation. In view of the additional difficulty posed by the non-convex modulus constraint, we exploit the problem structure to construct the constant-modulus design from the unconstrained-modulus solution which is cast as a problem of joint matrix approximation and solved by low-complexity Jacobi-like algorithms. Numerical results show that under loose and stringent delay-outage constraints, the mixed-timescale hybrid designs deliver effective rates comparable with other perfect CSI-based state-of-the-art baselines.

This paper examines a coordinated multi-point transmission/reception system where multiple base-stations (BSs) employ coordinated beamforming to serve multiple mobile-stations (MSs). Under the dynamic point selection mode, each MS can be assigned to only one BS at any time. This paper then presents a solution framework to optimize the BS associations and coordinated beamformers for all MSs. With target signal-to-interference-plus-noise ratios at the MSs, the design objective is to minimize either the weighted sum transmit power or the per-BS transmit power margin. Since the original optimization problems contain binary variables indicating the BS associations, finding their optimal solutions is a challenging task. To circumvent this difficulty, we first relax the original problems into new optimization problems by expanding their constraint sets. Based on the nonconvex quadratic constrained quadratic programming framework, we show that these relaxed problems can be solved optimally. Interestingly, with the first design objective, the obtained solution from the relaxed problem is also optimal to the original problem. With the second design objective, a suboptimal solution to the original problem is then proposed, based on the obtained solution from the relaxed problem. Simulation results show that the resulting jointly optimal BS association and beamforming design significantly outperforms fixed BS association schemes.

Wireless network virtualization has emerged as a promising technology to provide multifarious services and applications for future wireless networks due to its efficient exploitation of network resources. In a mobile virtual network (MVN), a network operator (NO) benefits from leasing physical resources, such as subcarriers, to multiple service providers (SPs), whom then make profits from offering certain wireless services to end users. To achieve the maximum profit for the NO, or the highest efficiency of a MVN, specialized trading mechanism needs to be designed when the NO is unable to access SPs' private information about cost. In this paper, we tackle the problem of efficient trading of subcarriers under incomplete information from the SPs to the NO. First, a general system model is developed with one NO and multiple SPs engaged in a trading relation to exchange spectrum resources and money. Subsequently, an optimal trading contract is derived to maximize the total utility at the NO, while maintaining the requirements by the SPs in the trading process. Simulation results confirm our analysis on the optimal contract and its benefits.

In this paper, we study joint design of linear hybrid precoding and equalization for full-duplex (FD) massive multiple-input multiple-output (MIMO) systems such that the sum mean squared error is minimized across all mobile stations. To better resolve practical issues such as hardware complexity, power consumption, and overhead of channel estimation, hybrid processing, which consists of digital processing in the baseband and radio frequency (RF) analog processing, is employed at the base station for simultaneous transmission and reception. In particular, baseband processing is adjusted according to instantaneous channel variation while RF processing is only updated based on such long-term channel statistics as transmit and receive correlation. In the presence of self-interference (SI) and co-channel interference, joint power optimization is carried out in order to achieve balanced performance for both the uplink and the downlink. As demonstrated by numerical results, FD is able to outperform half-duplex under realistic SI. Furthermore, the employment of the proposed hybrid processing structure is justified by its near optimal performance when equipped with even only a small number of RF chains.

Full-duplex (FD) has emerged as a promising solution for increasing the data rate of wireless communication systems. With FD, a wireless terminal can transmit and receive concurrently at the same frequency band. This paper focuses on the resource allocation in a FD multiuser system. With a FD-enabled base-station (BS) and multiple half-duplex (HD) mobile stations (MS), we are interested in jointly optimizing the uplink and downlink channel assignment for each MS and maximizing the system sum-rate. Since the joint optimization problem is a difficult nonconvex problem, we then propose an iterative algorithm to obtain at least a locally optimal solution. In the proposed algorithm, the system sum-rate is maximized via an equivalent problem of minimizing the weighted sum mean-squared error, whereas the channel assignment is updated by a gradient method. Simulation results show that the FD mode has the potential to substantially enhance a multiuser system's data-rate, compared to the HD mode.

Millimeter-wave (mmWave) communication has emerged as one of the most promising technologies to deal with the increasing demand in data transmissions over wireless networks. However, due to the propagation characteristic at the mmWave band, much higher pathloss is observed compared to the commonly-used microwave band. Thus, antenna arrays become a necessary ingredient in mmWave systems because of their needed beamforming gains. Beamforming for multiple users, also known as multiuser precoding, can be utilized to further improve the spectral efficiency of mmWave systems. Unfortunately, fully digital precoding with large antenna arrays is difficult to implement due to the hardware cost and power constraint in mmWave systems. Recent works in literature have advocated the structure of hybrid analog/digital precoding for mmWave systems, in which only minor performance degradation is observed. In this work, we study hybrid precoding for multiuser mmWave systems. After reviewing recent works in literature on hybrid precoding designs, we then develop a new hybrid minimum mean-squared error (MMSE) precoder. The proposed precoder can be easily obtained by an orthogonal matching pursuit-based algorithm. Simulation results show significant performance advantages of the proposed precoder over known designs in various system settings.

This paper studies hybrid RF/baseband linear pre-coding design to minimize the mean square error (MSE) for millimeter-wave massive multiple-input multiple-output (MIMO) systems using optimal linear equalizer. Instead of dealing with the objective function of sum MSE, which involves matrix inverses, we approach this problem by minimizing the Euclidean distance between the hybrid precoder and the optimal minimum MSE precoder. In an effort to impose the optimal structure of channel diagonalization, we separate the design of modulus-constrained RF precoder from the design of unconstrained baseband pre-coder. Magnitude-least-squares approximation is introduced to formulate the RF precoder design problem, and is subsequently transformed into a simultaneous matrix diagonalization problem. Such transformation enables application of a simple and numerically stable Jacobi-like algorithm. The effective channel representing a cascade of the derived RF precoder and the MIMO channel, is diagonalized by the baseband precoder. The error performance of the proposed solution is examined by numerical results where the effectiveness is verified by its closeness to the optimal design and its noticeable gain over sparse approximation based schemes.

This paper presents a solution framework to jointly optimize the base station association strategy and linear beamforming design for downlink transmission in a multicell system. Assuming each mobile station can only be assigned to one base station, our design objective is to minimize the sum transmit power across the base stations with a set of target signal-to-interference-plus-noise ratios at the mobile stations. Since the original optimization problem involves binary variables for base station associations, finding its optimal solution is a challenging task. To circumvent this difficulty, the original problem is relaxed into a new optimization problem by expanding its constraint set. Interestingly, it is shown that the relaxed problem can be solved optimally and its solution is also optimal to the original problem. We then propose two solution approaches to tackle the relaxed problem: one via its Lagrangian dual problem and the other via its dual uplink problem. Simulation results show that the resulting jointly optimal base station association and beamforming design can significantly outperform fixed base station association schemes.

This paper focuses on the design of vector perturbation (VP) precoding for multi-user multiple-input singleoutput downlink transmission under quantized channel state information. Each receiver decomposes its downlink channel vector in forms of channel direction information (CDI) and channel magnitude information (CMI) for feedback to the transmitter. Under quantized CDI and quantization error statistics, closed-form expressions to the mean-squared-error (MSE) between channel input and output when (i) perfect CMI available to the transmitter and (ii) only CMI statistics known at the transmitter, are derived. We then propose a unified approach to design the MSE minimization based VP precoders. Bit error rate simulation results indicate that the proposed VP precoder designs are less sensitive to quantization errors and CMI availability helps to improve the performance.

In this paper, the problem of joint access point (AP) selection and linear precoding for multiple-input multiple-output orthogonal frequency division multiplexing systems is studied in the framework of a non-cooperative game. This game is shown to be a potential game with the potential function being the sum rate achieved by successive interference cancellation. Due to the mixed-integer nature of the optimization variables, it is difficult, if not impossible, to directly characterize the maxima of the potential function, which are closely related to the Nash equilibrium (NE) of the game. Instead, we establish the existence and achievability of the maximum through non-decreasing and upper-bounded properties of the potential function as a direct result of the proposed update scheme. A distributed algorithm is designed where each player selfishly optimizes its AP selection and linear precoding strategy in a sequential manner. Convergence is a byproduct of the established properties of the potential function which are materialized by an iterative water-filling algorithm. Numerical results show that the algorithm is able to reach fast convergence, scale linearly with the number of users in terms of complexity, and provides a practical system sum rate at the NE nearing that of the optimal centralized solution.

This paper is concerned with linear precoding designs in a multiuser multiple-input single-output system. With the design objective of maximizing the system sum-rate, we take into consideration multiple linear power constraints at the base-station, including sum, per-antenna, and interference power constraints. We then propose two mean-squared error (MSE)-based precoders, namely minimum MSE (MMSE) and iterative minimization of weighted MSE (IWMMSE) precoders. Both proposed precoding designs are obtained by specialized iterative algorithms. To enforce the multiple power constraints, a certain set of auxiliary variables are introduced and updated iteratively at each algorithm. The proposed precoders are then given in closed-form at each iteration. Convergence of both proposed algorithms is then proved and verified by numerical simulations. Simulation results also show significant enhancements in sum-rate performance by the proposed precoding designs over zero-forcing precoding.

This paper considers a sparse precoding design for sum-rate maximization in a cloud radio access network (Cloud-RAN). Constrained by the fronthaul link capacity and transmit power limit at each remote radio head (RRH), the sparse design amounts to determine the precoders at the RRHs as well as the set of serving RRHs for each mobile user. In this work, we first formulate the fronthaul link constraints as non-convex and discontinuous constraints with sparsity terms. These sparsity terms are then iteratively approximated into linear forms by means of reweighted ℓ1-norm with conjugate functions. Finally, to determine the beamforming vectors, the non-convex sum-rate maximization problem with linear constraints is transformed into an equivalent problem of iterative weighted mean-squared error minimization. Convergence of the proposed iterative algorithm is then proved and verified by the presented numerical results. In addition, numerical results demonstrate the superior performance by the proposed algorithm over a previously proposed one in literature.

This work studies the problem of joint multiuser downlink beamforming and admission control in multiple-input multiple-output (MIMO) heterogeneous networks. Considered is a network where a newly deployed femtocell base-station (FBS) has the coverage overlapped with that of an existing macrocell base-station (MBS). Our design objective is to serve as many femto-users (FUEs) as possible at their quality-of-service (QoS) requirements while maintaining the QoS requirements at the macro-users (MUEs). In the first part of this work, we consider the joint downlink beamforming and admission control problem as a joint optimization problem, which can be solved in a centralized manner with full coordination between the MBS and the FBS. In the second part, we propose a distributed algorithm in performing joint downlink beamforming and admission control at the femtocell with only limited MBS-FBS coordination. Specifically, after acquiring certain design parameters from the MBS, the FBS unilaterally determines its beamforming and admission control strategy while coordinating its induced interference to the macrocell. We then prove that the distributed algorithm will converge to a fixed-point where the QoS at the MUEs and admitted FUEs is guaranteed. Simulation results show that the distributed algorithm performs as well as the centralized one in terms of number of FUEs served with only a small penalty on the power usage at the MBS and the FBS.

This paper is concerned with linear precoding designs in a multiuser downlink system. We consider a multiple-input single-output system with multiple single-antenna user-equipments (UE) experiencing non-homogeneous user conditions, including the channel strength and the background noise power. Assuming perfect knowledge of channel state information and noise power at the base-station (eNB), we propose a new regularized zero-forcing (RZF) precoder, which takes advantage of the non-homogeneous user conditions. Given in a closed-form solution, the proposed RZF precoder outperforms other well-known linear precoders, while achieving a close performance to the locally optimal iterative weighted minimization of mean-squared error precoder, in terms of the achievable network sum-rate. We then propose a greedy user selection algorithm in conjunction with the proposed RZF precoder when the number of UEs exceeds the number transmit antennas at the eNB.

This paper is concerned with the maximization of the weighted sum-rate (WSR) in the multicell MIMO multiple access channel (MAC). We consider a multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell, while the inter-cell transmissions are treated as noise. Nonetheless, the uplink precoders are jointly optimized at MSs through the coordination among the cells in order to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is shown to be nonconvex, obtaining its globally optimal solution is rather computationally complex. Thus, our focus in this work is on low-complexity algorithms to obtain at least locally optimal solutions. Specifically, we propose two iterative algorithms: one is based on successive convex approximation and the other is based on iterative minimization of weighted mean squared error. Both solution approaches shall then reveal the structure of the optimal uplink precoders. In addition, we also show that the proposed algorithms can be implemented in a distributed manner across the coordinated cells. Simulation results show a significant improvement in the network sum-rate by the proposed algorithms, compared to the case with no interference coordination.

This paper studies a multiuser multicell system where block-diagonalization (BD) precoding is utilized on a per-cell basis. We examine and compare the multicell system under two operating modes: competition and coordination. In the competition mode, the paper considers a strategic non-cooperative game (SNG), where each base-station (BS) greedily determines its BD precoding strategy in a distributed manner, based on the knowledge of the inter-cell interference at its connected mobile-stations (MS). Via the game-theory framework, the existence and uniqueness of a Nash equilibrium in this SNG are subsequently studied. In the coordination mode, the BD precoders are jointly designed across the multiple BSs to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is nonconvex, we consider a distributed algorithm to obtain at least a locally optimal solution. Finally, we extend our analysis of the multicell BD precoding to the case of BD-Dirty Paper Coding (BD-DPC) precoding. We characterize BD-DPC precoding game for the multicell system in the competition mode and propose an algorithm to jointly optimize BD-DPC precoders for the multicell system in the coordination mode. Simulation results show significant network sum-rate improvements by jointly designing the BD or BD-DPC precoders across the multicell system in the coordination mode over the competition mode.

This paper is concerned with the maximization of the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). Studied is the multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the inter-cell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices at the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complex. By applying a successive convex approximation technique, this work proposes a distributed algorithm that efficiently achieves a locally optimal solution. Simulations then show that the proposed algorithm can significantly improve the network-wide sum-rate, compared to the schemes with linear precoding or no interference coordination between the BSs.

This paper characterizes the multicell precoding game where block-diagonalization (BD)-based precoding is utilized on a per-cell basis for downlink transmissions. Sharing the same frequency band, the base-station (BS) at each cell wishes to maximize the sum-rate for its connected mobile-stations (MS) with BD precoding. In this context, the paper considers a strategic non-cooperative game (SNG), where each BS greedily determines its precoding strategy in a distributed manner, based on the knowledge of the inter-cell interference (ICI) at its connected MSs. Via the game-theory framework, the existence and uniqueness of a Nash Equilibrium (NE) of this multicell game are subsequently studied. It is shown that there always exists at least one pure NE in the game, whereas the uniqueness of the NE is guaranteed under a certain condition on the ICI. The paper also characterizes the multicell precoding game where BD-Dirty Paper Coding (BD-DPC) is utilized at each BS on a per-cell basis. Simulation results then confirm our analysis on the NE's uniqueness in the BD and BD-DPC multicell precoding games.

This study is concerned with the downlink beamforming designs in a coordinated multicell system with dynamic base‐station (BS) assignment. At each cell, a multiple‐antenna BS employs linear beamforming to send multiple data streams to its assigned mobile‐stations (MSs). Exploiting multicell coordination, the multiple BSs jointly optimise the beamformers and the BS‐MS assignments to enhance the overall system performance. With per‐BS power constraints, considered are the coordinated beamforming problems under the following two design criteria: (i) minimising the transmit power margin at the BS with a set of target signal‐to‐interference‐plus‐noise ratios (SINR) at the MSs and (ii) jointly maximising the minimum SINR margin at the MSs. As the original problem formulations are shown to be non‐convex integer programs, which are combinatorially hard, the authors propose an efficient convex relaxation approach to solve the problems with low complexity. Simulations show that the convex relaxation‐based assignment schemes significantly outperform heuristic fixed assignment schemes.

This paper is concerned with the maximization of the weighted sum-rate in the multicell MIMO multiple access channel (MAC). Considered is the multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell. However, the uplink covariance matrices at the MSs are jointly designed between the BSs in order to optimize the network-wide weighted sum-rate. Since this optimization is shown to be a nonconvex problem, obtaining its globally optimal solution is very hard. By applying successive approximation technique, a distributed algorithm is then proposed to efficiently achieve a locally optimal solution. Nonetheless, simulations then show that the proposed algorithm can significantly improve the network-wide sum-rate, compared to the case with no interference coordination between the BSs.

This paper is concerned with optimal downlink beamforming designs in a coordinated multicell system with per-base-station power constraints. At each cell, a multiple-antenna base-station (BS) employs linear beamforming to send multiple data streams to its remote mobile-stations (MS). With the coordination between the cells, the multiple BSs jointly optimize their beamformers to enhance the overall system performance. Under per-BS power constraints, considered are the two following design criteria: (i) minimizing transmit power with guaranteed signal-to-interference-plus-noise ratio (SINR) at each each MS and (ii) jointly maximizing the minimum SINR margin at the MS. The two optimization problems are reformulated as second-order conic programs (SOCP), and simple and fast converging numerical algorithms to efficiently solve them are proposed.

This paper studies the precoding design in a multicell multiuser (MU) system with universal frequency-reuse using a game-based approach. Considered is a multicell system, where the MU downlink transmissions in each cell are facilitated by a multi-antenna base-station (BS). In particular, the BS wishes to maximize the transmission sum-rate to its connected mobile-stations (MS) by the means of zero-forcing (ZF) precoding. In this context, the paper considers a strategic non-cooperative game (SNG), where each BS greedily determines its optimal power allocation in a distributed manner, based on the knowledge of the out-of-cell interference (OCI) at its connected MSs. Via the game theory framework, we study the existence and uniqueness of a Nash equilibrium (NE) of this multicell game. It is shown that a NE of the game always exists, whereas the NE uniqueness is guaranteed under a certain condition on the OCI. Numerical results confirm with the analysis that a small OCI level almost always leads to the NE's uniqueness. Simulations also show that the multicell game using known OCI knowledge provides additional sum-rate gains over the scheme with no OCI information.

This article studies optimal power allocation schemes in a multi-relay cooperating network employing amplify-and-forward (AF) protocol with multiple source–destination pairs. It is assumed that full channel state information is available at the relays. As such, distributed beamforming is employed in forwarding signals to the destinations. In this context, the authors extend recent works on distributed beamforming for a single source–destination pair to the scenario where multiple source–destination pairs are competing for the power resource at the relays. Under orthogonal transmissions of each source–destination pair, considered are the two following power allocation problems: (i) minimise the sum relay power with guaranteed quality of service (QoS) in terms of signal-to-noise ratio (SNR) at the destinations, and (ii) jointly maximise the SNR margin at the destinations subject to individual power constraints at the relays. Although these optimisation problems can be formulated as second-order conic programs (SOCP), the main contribution of this work are proposals of simple and fast converging numerical algorithms, based on the fixed point iteration framework, to efficiently solve these two problems.

This paper is concerned with the game-theory approach in designing the multiuser downlink beamformers in multicell systems. Sharing the same physical resource, the base station of each cell wishes to minimize its transmit power subject to a set of target signal-to-interference-plus-noise ratios (SINRs) at the multiple users in the cell. In that process, each base station determines its optimal downlink beamformer strategy in a distributed manner, without any coordination between the cells. Via the game-theory framework, we examine the conditions guaranteeing the existence and uniqueness of the Nash Equilibrium (NE). We establish the best response strategy of a cell, given the beamforming strategies from other cells. Such best response strategy is shown to be a standard function, which then guarantees the uniqueness of the NE and the convergence of the distributed algorithm. A sufficient condition for the existence of the NE is also presented.

This paper considers an adaptive iterative water-filling (IWF) algorithm to deal with the near-far problem in Digital Subscriber Lines (DSL) networks. In order to protect the far-end users, the paper proposes a new power back-off strategy by a pro-active configuration of the spectral mask at the near-end users. Instead of reducing the total power of the near-end users as in IWF, the proposed algorithm allows the near-end users to only back off the power in certain frequency bands. Simulation shows a significant performance advantage of the proposed algorithm over the traditional IWF algorithm. Moreover, the performance of the proposed algorithm approaches the optimal performance obtained by centralized algorithm with full channel information requirement while retaining the low-complexity and distributed implementation of the IWF.

This paper studies optimal distributed beamforming designs to jointly maximize the signal-to-noise (SNR) margin in a multiuser multi-relay network. Considered are optimization problems with two different types of power constraints: sum relay power constraint and per-relay power constraints. Although these two problems can be readily solved by the bisection method via a sequence of second-order conic feasibility programs, we propose simple and fast converging iterative algorithms to directly solve the two optimization problems under consideration.

This paper considers optimal distributed beamforming designs in a multi-relay network with multiple sources and multiple destinations. It is assumed that all source-destination pairs operate in orthogonal channels to avoid inter-user interference at the destinations. The distributed beamforming designs are carried out to minimize the sum relay power with guaranteed quality of service (QoS) in terms of signal-to-noise-ratio (SNR) at the destinations. Considered are optimization problems with and without per-relay power constraints. Although the two optimization problems can be readily transformed into convex second-order conic programs (SOCPs), the paper proposes simple and fast iterative algorithms to efficiently solve them.

This paper develops an amplify-and-forward relaying technique for multiuser wireless cooperative networks under frequency-selective block-fading. Single-carrier frequency division multiple-access with frequency-domain equalization technique is employed at both the relay and destination to combat the inter-block and inter-symbol interference caused by multipath propagation. With the assumption that the full channel state information (CSI) is available at the destination, the relay only knows the uplink channels while no CSI is available at the sources, two power allocation schemes are obtained: (i) to minimize the total transmit power at the relay while maintaining the signal-to-interference-plus-noise ratio (SINR) for each user at the destination above a certain level, and (ii) to maximize the worst SINR among all the users subject to a constraint on total relay transmit power. Analysis and simulation results are provided to illustrate the effectiveness of the proposed schemes.

In this paper, we study the joint power allocation and relay selection problem for multi-user amplify-and-forward (AF) cooperative networks. To increase the system's spectral efficiency under the orthogonal transmission assumption, each source-destination pair is constrained to be assisted by a small subset of a set of available relays. The aim of this work is to establish a framework that determines which relays to help which users and with how much power. In particular, we propose the joint schemes under two design criteria: i) maximization of user rates, and ii) minimization of the total transmit power at the relays. As the original problem formulations are shown to be nonconvex integer optimization problems, and thus, are combinatorially hard, we also propose an efficient convex relaxation approach to solve the problems with low complexity. Numerical results demonstrate the effectiveness of the proposed approaches.

This paper proposes a novel power allocation among the source and relays of wireless relay networks to minimize the mean-square error of the channel estimation when distributed space-time coding (DSTC) is applied. Both the maximum likelihood (ML) and minimum mean-square error (MMSE) estimations are considered. The impact of imperfect channel estimation on the error performance of DSTC is also analyzed, where it is proved that the mismatched decoding of DSTC is able to achieve the same diversity order as the coherent decoding of DSTC. Furthermore, when the optimal power allocation obtained in the training phase is applied to the transmission phase, the mismatched decoding is able to achieve the maximum diversity order as the coherent decoding.

This paper considers a communication network with multiple pairs of source and destination, assisted by multiple relays. It is assumed that perfect channel state information (CSI) is available at the relays. In a two-stage AF protocol, all the sources broadcast their signals to all the relays in the first stage. The received signal at each relay is processed by a beamforming weight and then re-broadcasted to all the destinations at the same time with other relays in the second stage. The focus is to find the optimal beamforming weights to meet a given set of target signal-to-interference-and-noise ratio (SINR) at the destinations, while minimizing the total transmitted power at the relays. We show that this problem can be formulated as a nonconvex quadratically constrained quadratic program (QCQP). Through relaxations, the problem can be solved efficiently by convex programming.

This paper derives an optimal power allocation (PA) to maximize the effective average signal-to-noise ratio (SNR) of distributed space-time coding (DSTC) in wireless relay networks, where the locations of the relays can be anywhere between the source and destination. It is first shown that in maximizing the average SNR, not all the relays might be active, and hence the code performance might be compromised. The amount of fading is then introduced for the relay networks and used as a constraint to derive a novel PA scheme. This new PA is shown to obtain the maximum diversity order of both noncoherent and coherent DSTC systems at high SNR.

This paper considers power allocation and distributed beam-forming optimization in a multiuser multi-relay network. Under the constraint on the total relay power, we investigate two resource allocation problems, namely the sum-rate maximization and minimum-rate maximization. Although both problems are not readily expressed in a convex form, a change in variables transforms the problems into standard optimization problems, which then can be solved efficiently. In addition, via dual decomposition, two algorithms are proposed to solve the two problems in a distributed fashion.

Space-time-frequency (STF) coding for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) over frequency-selective Rayleigh fading channels is considered. The maximum diversity order and coding gain of the system are derived when linear constellation preceding (LCP) is applied to OFDM symbols. It is also shown that with a proper subcarrier grouping of the OFDM symbols, the system implementation can be greatly simplified while maintaining the diversity order and coding gain. In particular, as long as the group size F is not less the number of effective resolvable channel taps L, the maximum diversity order can always be achieved. Moreover, if F ges L, and F is an Euler number or an integer power of 2, the maximum coding gain can be achieved. Otherwise, the achievable coding gain approximates 70% of the maximum one.