We study a control-based design of beamforming and trajectory that incorporates the dynamic model, focusing on a scenario where a multi-antenna unmanned aerial vehicle (UAV) simultaneously performs radar sensing of multiple targets in a specific region and communication with multiple ground users. Two optimization problems are formulated for the three-degree-of-freedom (3-DoF) and six-degree-of-freedom (6-DoF) dynamic models of UAV, which are often overlooked in existing designs. These problems aim to maximize the average weighted communication rate while maintaining the dynamic constraints and the sensing service requirements by designing the UAV trajectory and the communication and sensing beamforming vectors. To deal with the challenges posed by the UAV dynamic constraints, we decompose the original problem into two subproblems: the communication and sensing beamforming design subproblem, and the UAV trajectory optimization subproblem. Given the UAV trajectory, we employ the sequential convex approximation (SCA) and semi-definite relaxation (SDR) methods to transform the beamforming design subproblem into a convex problem. Given the communication and sensing beamforming vectors, we propose a control-based approach with piecewise parameterization and exact penalty function strategies to transform the UAV trajectory optimization subproblem into a static nonlinear program, which can be efficiently solved by sequential quadratic programming (SQP). Numerical simulations indicate that the proposed scheme is more feasible in terms of the UAV control than the existing scheme in practical systems, with less performance loss or even no performance degradation.

In this paper, we propose a time-division integrated sensing, communication and control (ISCC) scheme designed to dynamically enhance communication and sensing capabilities on the UAV platform. The UAV is dispatched to track a randomly moving target for capturing and transmitting sensing data to the base station via wireless communication. The goal is to leverage the ISCC framework for maximizing the cumulative sensing mutual information while guaranteeing successful data transmission by optimizing the allocation of the communication and sensing time slots together with the UAV’s control scheme. The formulated problem cannot be straightforwardly solved by off-the-shelf optimization algorithms due to the time-varying environment. To tackle this challenge, a constrained soft actor-critic (C-SAC) algorithm is developed, which dynamically switches between maximizing rewards and minimizing constraint violations to ensure robust performance in changing environments while maintaining the simplicity and efficiency of unconstrained policy optimization. Simulation results demonstrate that the proposed C-SAC algorithm outperforms dual-variable-based methods in handling the constrained problems, while extensive Monte Carlo tests confirm the robustness of the ISCC policy trained by the proposed algorithm, which adapts to varying target speeds and achieves higher cumulative mutual information compared to the point-mass UAV models.

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least$2~dB$signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying.

In this paper, signal detection in ambient backscatter communication (AmBC) with dynamic sources is investigated. Dynamic ambient radio frequency sources are the sources which transmit their signals randomly. The states of the dynamic source include the on-state and off-state. In the off-state, dynamic sources do not transmit any signal and the backscatter device cannot backscatter any signal accordingly. The received signals may contain only pure noises. Thus, it brings new challenges to the signal detection. We develop detection methods with/without truncating received signals for AmBC under both dynamic complex Gaussian source signals and dynamic M-ary phase-shift keying (M-PSK) source signals with on-off keying. To make detection simple and tractable for the dynamic M-PSK at the receiver, Manchester code is applied. Simulation results show it is necessary to truncate part of the signal in detections in the high signal-to-noise ratio region unless the received signals contain few pure noise samples, in order to reduce the bit-error-rate of backscatter signals.

Backscatter communication (BackCom) technology that takes advantage of the radio frequency signals to facilitate the communications of passive devices has attracted much attention in recent years. To enhance its communication performance, the multiple-input and multiple-output (MIMO) technology has been introduced to BackCom. Channel estimation is crucial for the MIMO BackCom system. However, the optimization for the pilot training sequences has not been studied. In this paper, we propose a pilot sequence design algorithm for MIMO BackCom systems with spatially correlated antennas, which can estimate both the direct link and the backscatter link channel information. We derive the optimal structure of the source and the tag pilot sequences which achieves the minimum mean-squared error (MSE) of channel estimation. Then, we optimize the power allocation between the source pilot sequences. Simulation results show that our proposed algorithm can estimate channel efficiently and achieve better sum MSE performance than the benchmark without power allocation.

The unmanned aerial vehicle (UAV)-enabled data collection system with a rotary-wing UAV and multiple ground nodes (GNs) is investigated in this paper. The average transmission data rate is maximized through the coordinated optimization of the GNs’ transmit power and the UAV’s trajectory. In particular, the UAV dynamic constraints and physical constraints are imposed. The UAV dynamics, which are governed by a group of differential equations, are usually ignored in existing works. As a consequence, the planned trajectory cannot be fully tracked by the controller in real world applications, which could lead to severe performance degradation. Thus, a control-based method is devised to address this issue. Specifically, by adopting the state-space model from control theory, the data collection problem is established as a dynamic optimization problem subject to state constraints, in which both of the decision variables and constraints are infinite-dimensional in nature. The key idea of the solution method is to convert the infinite-dimensional dynamic program into a finite-dimensional static nonlinear problem. This is achieved by deriving the required gradients of the dynamic optimization problem based on the control parametrization scheme and an exact penalty function method. The effectiveness and superiority of the proposed design are validated via numerical experiments.

Underwater visible light communication (UVLC) is believed to revolutionize the future of optical communication, where visible light of wavelength 400-500 nm range is used to transmit data underwater. In this paper we aim to find how the position of the receiver affects the accuracy of the received optical signal. The message signal is modulated using pulse position modulation (PPM) because of its high noise resistance through water. The position of the receiver is changed accordingly and the bits in error are recorded to discuss the relationship between angle of the receiver, bit-error-rate and received signal power.

Underwater wireless communication networks (UWCNs) can support a wide range of applications in the underwater domain including: mining and drilling, coastline monitoring, border surveillance and submarine/mine detection. Some of these applications are sensitive in nature (e.g., military) and demand stringent security requirements for data communications. In order to prevent malicious attacks (e.g., jamming) in these UWCNs, robust security countermeasures must be implemented. Additionally, sensitive data communications must be protected. However, computationally expensive security protocols, such as encryption, can severely shorten UWCN lifetime, where battery-powered nodes already suffer from scarce energy supplies. In this work, we exploit content caching as a countermeasure for jamming and utilize selective encryption of sensitive data to simultaneously maximize network security and node residual energy in UWCNs. Our work formulates the joint security and residual energy maximization challenge as an optimization problem in which results indicate that the proposed technique can guarantee secure communications without sacrificing network lifespan.

In this article, a joint design of communication sensing and control (JDCSC) scheme is developed which focuses on a scenario where a cellular-connected unmanned aerial vehicles (UAV) senses a moving target. The goal is to maximize the sensing mutual information via jointly optimizing the transmit power, the trajectory of the UAV and the task completion time, while meeting the onboard energy, the communication service quality, and the UAV flight safety constraints. In particular, UAV dynamics are considered, which are usually ignored in the existing design and inferior communication and sensing quality of service might be resulted. The formulated problem is dynamic optimization problem, which is difficult to be solved. The control parameterization method and exact penalty function scheme are utilized to transform the problem into a static nonlinear program which can be solved by gradient-based methods. The effectiveness of the JDCSC approach is verified by carrying out some numerical examples.

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order.

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.

Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users’ information rates by jointly optimizing the duration of users’ time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users’ rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms.

In this article, we investigate a two-hop decode-and-forward (DF) multicasting multiple-input multiple-output (MIMO) wireless relay communication system. Different to conventional systems, the radio frequency (RF) energy from the source node is harvested at the relay node and used for forwarding signals to a group of receivers. Considering the structure of the energy harvesting (EH) relay node, we present a power splitting (PS) based protocol and a novel time switching (TS) based protocol by introducing two additional TS factors. For both protocols, we maximize the system mutual information (MI) of the multicasting MIMO relay system by jointly optimizing the source and relay covariance matrices under the constraints of the source energy and the relay harvested energy. In addition, a practical nonlinear EH model is adopted, where the energy harvested by the relay node is bounded as the incident RF signal power increases, and the harvested power is zero when the input power is below the minimum power for harvesting. For the TS based protocol, we also consider peak transmission power constraints at both the source and relay nodes. The performance of the proposed algorithms is verified via numerical simulations. The results demonstrate that the novel TS based protocol achieves a larger MI than the conventional TS protocol. The PS and TS based protocols achieve tradeoffs at different source power levels. In particular, compared with the PS based protocol, the proposed novel TS based protocol can reach a higher system MI when the EH bound is not reached, while the former protocol reaches a higher MI when the EH circuit is saturated. We show that the peak harvested energy constraint plays an important role in selecting the optimal location of the relay node.

In this article, a new relay-aided secure communication system is investigated, where a transmitter sends signals to a destination via an amplify-and-forward (AF) relay in the presence of an eavesdropper. We consider a general system configuration, where the source, relay, destination, and eavesdropper are all equipped with multiple antennas. In the practical scenarios of unknown eavesdropper's channel state information (CSI) and uncertainty of the eavesdropper's location, we aim to maximize the expected value of the system secrecy rate over the presumed distribution of the eavesdropper's channels, by exploiting the artificial noise (AN) transmitted by the source and relay nodes. The system design issue is formulated as a nonconvex stochastic optimization problem with a source transmission power constraint and a nonconvex relay transmission power constraint. A novel computational method is proposed to solve this challenging problem. The new method is developed based on an exact penalty function method together with a parallel stochastic decomposition algorithm. Numerical simulations are performed to study the effectiveness of the proposed scheme at various locations of the eavesdropper. Simulation results show that for most cases, secure communication can be achieved without the CSI knowledge of eavesdropper's channels, and the achievable secrecy rate follows the trend of a benchmark system where the eavesdropper's full CSI is available. In particular, the achievable system secrecy rate increases with the number of antennas at the legitimate users. Moreover, the optimal power allocated for the transmission of the AN increases with the system signal-to-noise ratio. The proposed computational method achieves a higher system secrecy rate than a conventional penalty function based approach.

Targeting at a better design of the analogue network coding (ANC)-assisted two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) multi-relay communication systems, we bring in the nonlinear minimal mean-squared error (MMSE)-decision feedback equalization (DFE) receiving technique to jointly optimize the source precoding, relay amplifying, feed-forward and feedback matrices. Under the transmission power constraints at both source nodes and each relay node, the two-way sum mean-squared error (MSE) of the signal waveform estimation of all data streams is minimized. To solve the complicated nonconvex optimization problem with four groups of system parameters, this paper develops an iterative block coordinate descent (BCD) algorithm, which converges to at least a Nash point. On the basis of it, for mitigating the error propagation in MMSE-DFE receivers, a group of permutation matrix variables, determining the detection orders of all data streams, are further introduced in our system optimization. Moreover, in case there is no sufficiently precise channel state information (CSI), we also make an extension of the developed algorithms, yielding a robust design scheme, to handle the channel uncertainties. Numerical simulation results show that, compared with the existing linear MMSE receiving-based algorithm, our proposed nonlinear ones provide improved MSE and bit-error-rate (BER) performance as well as good robustness against the imperfect CSI, indicating a promising application prospect of this research.

Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance.

Due to its tree‐like topology, the point‐to‐point (P2P) indoor power line communication (PLC) channel exhibits a similar broadcasting property as the wireless propagation channel shows. However, after relay nodes are introduced, the relay‐assisted PLC (RaPLC) channel shows some notable differences compared with its wireless counterpart. A lot of results about the relay‐assisted wireless channel can be found in the literature, but the work of modelling RaPLC channel has rarely been discussed in detail. As the first attempt to fill this gap, we apply the ABCD method to compute the channel transfer function (CTF) of the RaPLC channel. It has been clarified that, in general, the CTF of RaPLC channel cannot be obtained as a cascade of two independent P2P PLC sub‐channels located on two sides of the relay node. Also, it is shown that the challenge of generating CTF of the RaPLC channel can be transformed into a group of equivalent P2P PLC channel modelling tasks. Under the time‐division duplexing constraint, the relay node changes its working mode from the receiving phase to the transmitting phase, which shows that the RaPLC channel is abruptly time‐varying in nature. Following the hybrid bottom‐up approach, a statistical channel model with a simple six‐segment indoor power grid topology has been developed for simulation. Practical measurements and numerical examples verify our results.

This letter is concerned with a robust minimum variance beamformer design. To hedge the mismatch between the true and the assumed steering vectors, a distributionally robust beamformer (DR-beamformer) is proposed. The tractable reformulation of this beamformer is developed. Compared with the existing robust beamformers (e.g., worst-case robust beamformer and Gaussian robust beamformer), the proposed robust beamformer does not assume full knowledge of the channel mismatch. Therefore, it is more flexible in practice and more general in formulation. In addition, the relationships of the proposed robust beamformer to the existing ones are investigated. The performance gain of the DR-beamformer over the other robust beamformers is highlighted through numerical simulations.

A two-hop amplify-and-forward multiple-input multiple-output relay system with direct link is considered in this paper. The relay node has no self-power supply and relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. In particular, we consider the time switching (TS) protocol between wireless information and energy transfer. We study the joint optimization of the source and relay precoding matrices and the TS factor to maximize the achievable source-destination rate when a single data stream is transmitted from the source node. The optimal structure of the source and relay precoding matrices is derived, which reduces the original problem to a simpler optimization problem. The simplified problem is then solved efficiently by a two-step method. Numerical simulations show that the proposed algorithm yields a higher rate and better rate-energy tradeoff than suboptimal approaches.

In this paper, we study a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, where the relay node has no self-power supply, and relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. We apply the time switching (TS) protocol between wireless information and energy transfer. As a novel contribution of this paper, we propose a more general energy consumption constraint at the source node during the information and energy transfer, which includes the constant power constraints used in existing works as special cases. We study the joint optimization of the source precoding matrices, the relay amplifying matrix, and the TS factor to maximize the source-destination mutual information (MI). The optimal structure of the source and relay matrices is derived, which reduces the original transceiver optimization problem to a simpler power allocation problem. Numerical simulations show that the proposed algorithm yield higher system MI and better rate-energy tradeoff than an existing approach.

The orthogonal frequency-division multiplexing (OFDM) technology receives increasing attention in underwater acoustic (UA) communications. This paper presents a real-time OFDM-based UA communication system, implemented using the National Instruments CompactDAQ device and the LabVIEW software. The system design including both the transmitter and receiver is discussed. The performance of this real-time system is verified through a UA communication experiment conducted recently in a tank. Compared with conventional digital signal processor (DSP)-based design, the proposed implementation simplifies the prototype design and reduces the software development time.

Impulsive noise occurs frequently in underwater acoustic (UA) channels and can significantly degrade the performance of UA orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel compressed sensing based algorithms for joint channel estimation and impulsive noise mitigation in UA OFDM systems. The first algorithm jointly estimates the channel impulse response and the impulsive noise by utilizing pilot subcarriers. The estimated impulsive noise is then converted to the time domain and removed from the received signals. We show that this algorithm reduces the system bit-error-rate through improved channel estimation and impulsive noise mitigation. In the second proposed algorithm, a joint estimation of the channel impulse response and the impulsive noise is performed by exploiting the initially detected data. Then, the estimated impulsive noise is removed from the received signals. The proposed algorithms are evaluated and compared with existing methods through numerical simulations and on real data collected during a UA communication experiment conducted in the estuary of the Swan River, WA, Australia, during December 2015. The results show that the proposed approaches consistently improve the accuracy of channel estimation and the performance of impulsive noise mitigation in UA OFDM communication systems.

A receiver design problem for multi-access space-time block coded multiple-input multiple-output systems is considered. To hedge the mismatch between the true and the estimated channel state information (CSI), several robust receivers have been developed in the past decades. Among these receivers, the Gaussian robust receiver has been shown to be superior in performance. This receiver is designed based on the assumption that the CSI mismatch has Gaussian distribution. However, in real-world applications, the assumption of Guassianity might not hold. Motivated by this fact, a more general distributionally robust receiver is proposed in this paper, where only the mean and the variance of the CSI mismatch distribution are required in the receiver design. A tractable semi-definite programming (SDP) reformulation of the robust receiver design is developed. To suppress the self-interferences, a more advanced distributionally robust receiver is proposed. A tight convex approximation is given and the corresponding tractable SDP reformulation is developed. Moreover, for the sake of easy implementation, we present a simplified distributionally robust receiver. Simulations results are provided to show the effectiveness of our design by comparing with some existing well-known receivers.

We investigate the transceiver design for interference two-way amplify-and-forward multiple-input multiple-output relay communication systems. A novel algorithm with a closed-form solution is developed to optimize the relay precoding matrix based on its optimal structure and a modified transmission power constraint at the relay node. An iterative algorithm is proposed to minimize the sum mean-squared error of the signal waveform estimation. Simulation results demonstrate that the proposed algorithm achieves a better performance-complexity tradeoff compared with existing techniques.

In this paper, we propose statistically robust design for multiuser multiple-input multiple-output (MIMO) relay systems with direct source-destination links and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. Simulation results demonstrate the improved robustness of the proposed algorithms against CSI errors.

In this paper, we address the proportional fair (PF) issue of a K link full-duplex (FD) multiple-input multiple-output (MIMO) interference channel, where each link consists of two FD nodes exchanging information simultaneously. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from the nodes in other links due to simultaneous transmission from each link. The PF issue is important for networks with asymmetric topology and/or asymmetric traffic demands. We demonstrate that the proposed algorithm provides a good trade-off between sum achievable rate and rate distribution for asymmetric links, and moreover we show that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline half-duplex (HD) schemes.

Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking methods have been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples.

We study two scenarios of full-duplex (FD) multiple-input-multiple-output cognitive radio networks: FD cognitive ad hoc networks and FD cognitive cellular networks. In FD cognitive ad hoc networks (also referred as interference channels), each pair of secondary users (SUs) operate in FD mode and communicate with each other within the service range of primary users (PUs). Each SU experiences not only self-interference but also interuser interference from all other SUs, and all SUs generate interference on PUs. We address two optimization problems: one is to minimize the sum of mean-squared errors (MSE) of all estimated symbols, and the other is to minimize the maximum per-SU MSE of estimated symbols, both of which are subject to power constraints at SUs and interference constraints projected to each PU. We show that these problems can be cast as a second-order cone programming, and joint design of transceiver matrices can be obtained through an iterative algorithm. Moreover, we show that the proposed algorithm is not only applicable to interference channels but also to FD cellular systems, in which a base station operating in FD mode simultaneously serves multiple uplink and downlink users, and it is shown to outperform HD scheme significantly.

This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method.

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We apply the method of superimposed channel training to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the mean-squared error (MSE) of channel estimation. Moreover, the optimal power allocation between the source and relay training sequences is derived to improve the performance of channel estimation. Numerical examples are shown to demonstrate the performance of the proposed superimposed channel training algorithm for two-way MIMO relay systems in frequency-selective fading environments.

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.

In this paper, we investigate the performance of the Tomlinson-Harashima (TH) precoder based nonlinear transceiver design for a nonregenerative multiple-input multiple-output (MIMO) relay system assuming that the full channel state information (CSI) of the source-relay link is known, while only the channel covariance information (CCI) of the relay-destination link is available at the relay node. We first derive the structure of the optimal TH precoding matrix and the source precoding matrix that minimize the mean-squared error (MSE) of the signal waveform estimation at the destination. Then we develop an iterative algorithm to optimize the relay precoding matrix. To reduce the computational complexity of the iterative algorithm, we propose a simplified precoding matrices design scheme. Numerical results show that the proposed precoding matrices design schemes have a better bit-error-rate performance than existing algorithms.

In this paper, we propose statistically robust design for multiple-input multiple-output (MIMO) relay systems with the direct source-destination link and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. In particular, we derive the structure of the optimal relay precoding matrix and show the effect of CSI mismatch on the structure of the optimal robust source and relay matrices. The proposed algorithms generalize the transceiver design of MIMO relay systems with the direct link to the practical scenario of imperfect CSI knowledge. Simulation results demonstrate an improved performance of the proposed algorithms with respect to the conventional methods at various levels of CSI mismatch.

In this paper, we investigate the transceiver design for amplify-and-forward interference multiple-input multiple-output (MIMO) relay communication systems, where multiple transmitter-receiver pairs communicate simultaneously with the aid of a relay node. The aim is to minimize the mean-squared error (MSE) of the signal waveform estimation at the receivers subjecting to transmission power constraints at the transmitters and the relay node. As the transceiver optimization problem is nonconvex with matrix variables, the globally optimal solution is intractable to obtain. To overcome the challenge, we propose an iterative transceiver design algorithm where the transmitter, relay, and receiver matrices are optimized iteratively by exploiting the optimal structure of the relay precoding matrix. To reduce the computational complexity of optimizing the relay precoding matrix, we propose a simplified relay matrix design through modifying the transmission power constraint at the relay node. The modified relay optimization problem has a closed-form solution. Simulation results demonstrate that the proposed algorithms perform better than the existing techniques in terms of both MSE and bit-error-rate.

In this paper, we consider a dual-hop multicasting multiple-input multiple-output (MIMO) relay system where multiple transmitters multicast their own messages to a group of receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. We aim at minimizing the maximal MSE of the signal waveform estimation among all receivers subjecting to power constraints at the transmitters and the relay node. We propose a low complexity solution for the problem under some mild approximation. In particular, we show that under (moderately) high signal-to-noise ratio (SNR) assumption, the min-max optimization problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

In this paper, we establish the signal-to-interference-noise-ratio (SINR) duality between multiple access (MAC) and broadcast (BC) multihop amplify-and-forward multiple-input-multiple-output relay systems under an imperfect channel state model, which is a generalization of several previously established MAC-BC duality results. We show that identical SINRs in the MAC and BC systems can be achieved by two approaches. The first one is to use the Hermitian transposed MAC relay amplifying matrices at the relay nodes in the BC system under the same total network transmission power constraint. The second one is to use the scaled and Hermitian transposed MAC relay amplifying matrices in the BC system under the transmission power constraint at each node of the system, where the scaling factors are obtained by swapping the power constraints of the nodes in the MAC system. Moreover, we derive the MAC-BC mean-squared error and achievable sum-rate [or mutual information] duality properties based on the SINR duality. Numerical results show the utility of the duality results established.

In this paper, we consider a multicasting multiple-input multiple-output (MIMO) relay system where multiple transmitters multicast their own messages to a group of receivers over multiple hops, and all nodes are equipped with multiple antennas. The joint transmit and relay precoding design problem has been investigated for multicasting multiple data streams based on min-max mean-squared error (MSE) criterion. We aim at minimizing the maximal MSE of the signal waveform estimation among all receivers subjecting to power constraints at the transmitters and all the relay nodes. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices by solving convex subproblems. By exploiting the optimal structure of the relay precoding matrices, we then propose a low complexity solution for the problem under some mild approximation. In particular, we show that under (moderately) high signal-to-noise ratio assumption, the min-max optimization problem can be solved using the semidefinite programming technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Transceiver designs have been a key issue in guaranteeing the performance of multiple-input multiple-output (MIMO) relay systems, which are, however, often subject to imperfect channel state information (CSI). In this paper, we aim to design a robust MIMO transceiver for nonregenerative MIMO relay systems against imperfect CSI from a worst-case robust perspective. Specifically, we formulate the robust transceiver design, under the minimum mean-squared error (MMSE) criterion, as a minimax problem. Then, by decomposing the minimax problem into two subproblems with respect to the relay precoder and destination equalizer, respectively, we show that the optimal solution to each subproblem has a favorable channel-diagonalizing structure under some mild conditions. Based on this finding, we transform the two complex-matrix subproblems into their equivalent scalar forms, both of which are proven to be convex and can be efficiently solved by our proposed methods. We further propose an alternating algorithm to jointly optimize the precoder and equalizer that only requires scalar operations. Finally, the effectiveness of the proposed robust design is verified by simulation results.

In this paper, the problem of transceiver design in a non-regenerative MIMO relay system is addressed, where linear signal processing is applied at the source, relay and destination to minimize the mean-squared error (MSE) of the signal waveform estimation at the destination. In the proposed design scheme, optimal structure of the source and relay precoding matrices are obtained with the assumption that the relay knows the mean and channel covariance information (CCI) of the relay-destination link and the full channel state information (CSI) of the source-relay link. Based on this assumption, an iterative joint source and relay precoder design is proposed to achieve the minimum MSE of the signal estimation at the destination. In order to reduce computational complexity of the proposed iterative design, a suboptimal relay-only precoder design is proposed. A numerical example shows that the performance of the proposed iterative joint source and relay precoder design is very close to that of the algorithm using full CSI.

The demand for wireless access continues to increase rapidly in both military and civilian communities. The modern internet and modern personal-area devices have made billions of users around the world accustomed to data-hungry applications such as videos. This has an inevitable effect on the users' desire for the same through wireless media. The articles in this special issue focus on new theories and methods for advanced wireless relay technologies.

This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the inaccessible source matrix is normalized to be column-sum-to-1. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recovery of the sources. The proposed method is much faster than Chan's method, which has similar assumptions to ours, due to the usage of optimal projection. It is also more advantageous in separating cross-correlated sources than the independence- and uncorrelation-based methods, as it does not employ any statistical information of the sources. Furthermore, the new method does not require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.

We consider a non-regenerative multi-group multi-way relaying scenario in which each group consists of multiple half-duplex nodes. Each node wants to share its data with all other nodes within its group. The transmissions are performed via an intermediate non-regenerative half-duplex multi-antenna relay station, termed RS, which spatially separates the different groups. In our proposal, all nodes simultaneously transmit to RS during a common multiple access phase and RS retransmits linearly processed versions of the received signals back to the nodes during multiple broadcast (BC) phases. We propose a novel transmit strategy which exploits analog network coding (ANC) and efficiently combines spatial transceive processing at RS with joint receive processing at each node over multiple BC phases. A closed-form solution for an ANC aware relay transceive filter is introduced and closed-form solutions for the joint receive processing filters at the nodes are presented. Furthermore, self-interference cancellation and successive interference cancellation are exploited at the nodes to improve the joint receive processing. By numerical results, it is shown that the proposed transmit strategy significantly outperforms existing multi-way strategies.

In this paper, we consider a two-hop multicasting multiple-input multiple-output (MIMO) relay system where one transmitter multicasts common message to multiple receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. Joint transmit and relay precoding design problems are investigated for multicasting multiple data streams based on two design criteria. In the first scheme, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among all receivers subjecting to power constraints at the transmitter and the relay node. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices through solving convex subproblems. By exploiting the optimal structure of the relay precoding matrix, we then propose a low complexity solution which decouples the optimization of the transmitter and relay matrices under the (moderately) high first-hop signal-to-noise ratio (SNR) assumption. In the second scheme, we propose a total transmission power minimization strategy subjecting to quality-of-service (QoS) constraints. By using the optimal structure of the relay precoding matrix and the (moderately) high first-hop SNR assumption, we show that this problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms. Interestingly, we show that for the special case of single data stream multicasting, the relay precoding matrix optimization problem can be equivalently converted to the transmit beamforming problem for single-hop multicasting systems.

In this paper, we propose a design scheme for amplify-and-forward multiple-input multiple-output (AF MIMO) relay system with direct link to minimize the mean-squared error (MSE) of the signal estimation at the destination. In the proposed design scheme, an optimal precoding matrix is derived with the assumption that the full channel state information (CSI) of the source-relay link and partial channel state information such as channel covariance information (CCI) of the relay-destination link are available at the relay. In practical cases, if the destination is closer to the source, the source-destination link cannot be ignored. Hence, in this paper, we assume that the relay knows the partial channel state information of the source-destination link. Based on this assumption, an iterative optimal covariance algorithm is developed to achieve the minimum MSE of the signal estimation at the destination. In order to reduce computational complexity of the proposed optimal covariance algorithm, a suboptimal covariance algorithm is proposed. A numerical example shows that the developed optimal covariance algorithm outperforms the conventional CCI based MSE algorithms.

The interest in utilizing Wi-Fi signals for indoor location estimation purposes has been increased recently due to wide deployment of WLANs. Received signal strength (RSS) based approach has become an attractive candidate for positioning owing to its simplicity and low-complexity, which can be easily implemented in modern wireless devices such as laptops and PDAs. However, the challenging nature of indoor wireless propagation environments provoke time varying location estimations from RSS based positioning algorithms. In this paper, we have shown that this variability of the location estimations can be reduced by introducing an antenna array at the receiving station. In our proposed approach, the variation of the received signal power with respect to time is averaged using a uniform linear antenna array (ULA) at the mobile station. We further explore the impact of number of array elements on the accuracy of the position estimations by using representative set of multi-lateration algorithms. In the first phase of analysis, we consider uncorrelated Rayleigh fading channels on each antenna element whilst in the second phase, we take into account the fading correlation between antenna elements using the spatial correlation function for two-dimensional (2D) diffuse field. The proposed positioning technique can be integrated into IEEE 802.11 compatible receivers with single-input multiple-output (SIMO) capability, thus be able to use for robust indoor localization purposes.

In conventional two-phase channel estimation algorithms for dual-hop multiple-input multiple-output (MIMO) relay systems, the relay-destination channel estimated in the first phase is used for the source-relay channel estimation in the second phase. For these algorithms, the mismatch between the estimated and the true relay-destination channel affects the accuracy of the source-relay channel estimation. In this paper, we investigate the impact of such channel state information (CSI) mismatch on the performance of the two-phase channel estimation algorithm. By explicitly taking into account the CSI mismatch, we develop a robust algorithm to estimate the source-relay channel. Numerical examples demonstrate the improved performance of the proposed algorithm.

Based on the knowledge of instantaneous channel state information (CSI), the optimal source and relay pre-coding matrices have been developed recently for multiple-input multiple-output (MIMO) relay communication systems. However, in real communication systems, the instantaneous CSI is unknown and needs to be estimated at the destination node. In this paper, we propose a superimposed channel training method for MIMO relay communication systems. It is shown that to minimize the mean-squared error (MSE) of channel estimation, the optimal training sequence at each node matches the eigenvector matrix of the transmitter correlation matrix of the forward MIMO channel. Then we optimize the power allocation among different streams of the training sequence at the source node and the relay node. Simulation results show that the proposed algorithm leads to a smaller MSE of channel estimation compared with the conventional MIMO relay channel estimation algorithm.

The 46 papers focusing on the theme of "Theories and Methods for Advanced Wireless Relays" have been divided into two groups to be published in two separate issues. This first issue includes 23 papers on relay performance bound, MIMO relay beamforming, relay channel estimation, two-way and shared relays, full-duplex relays, and security for relay networks. The second issue includes papers on coding for relay networks, medium access control for relays, implementation ans system performance studies.

The remarkable promise of multiple-input multiple-output (MIMO) wireless channels has motivated an intense research activity to characterize the theoretical and practical issues associated with the design of transmit (source) and receive (destination) processing matrices under different operating conditions. This activity was primarily focused on point-to-point (single-hop) communications but more recently there has been an extensive work on two-hop or multi-hop settings in which single or multiple relays are used to deliver the information from the source to the destination. The aim of this tutorial is to provide an up-to-date overview of the fundamental results and practical implementation issues in designing amplify-and-forward MIMO relay systems.

This paper presents a novel patchwork-based embedding and decoding scheme for digital audio watermarking. At the embedding stage, an audio segment is divided into two subsegments and the discrete cosine transform (DCT) coefficients of the subsegments are computed. The DCT coefficients related to a specified frequency region are then partitioned into a number of frame pairs. The DCT frame pairs suitable for watermark embedding are chosen by a selection criterion and watermarks are embedded into the selected DCT frame pairs by modifying their coefficients, controlled by a secret key. The modifications are conducted in such a way that the selection criterion used at the embedding stage can be applied at the decoding stage to identify the watermarked DCT frame pairs. At the decoding stage, the secret key is utilized to extract watermarks from the watermarked DCT frame pairs. Compared with existing patchwork watermarking methods, the proposed scheme does not require information of which frame pairs of the watermarked audio signal enclose watermarks and is more robust to conventional attacks.

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

The public Internet in its current form does not provide consistently the levels of service that real-time services such as Voice over Internet Protocol (VoIP) demand. Indeed, the scope of this gap is such that quality and reliability problems are characteristic of these services. Fully redundant dispersity routing exploiting the path diversity readily available in the Internet is one approach of mitigating these quality and reliability problems. This paper presents a model for estimating the quality that may be expected from fully redundant dispersity routing systems using paths with known packet loss and loss burstiness characteristics. That model is then applied to estimate the quality that may be expected from fully redundant dispersity routing systems of 2-6 paths and, for contrast, to the estimated quality that may be expected from single path systems. The insights gained by this application may be useful when selecting paths for a fully redundant dispersity routing system to satisfy some quality goal. A brief study into the accuracy of the model indicates that for two paths, 50% of the estimations are within 0.05 of the simulated Mean Opinion Score (MOS), and 98% within 0.32.

In this paper, the problem of transceiver design in a non-regenerative MIMO relay system is addressed, where linear signal processing is applied at the relay and destination to minimize the mean-squared error (MSE) of the signal waveform estimation. The optimal structure of the relay precoding matrix is derived with the assumption that the relay knows the channel covariance information of the relay-destination link and the full channel state information (CSI) of the source-relay link. Simulation results demonstrate that the proposed scheme outperforms conventional relay algorithms, and its performance is comparable to the optimal relay algorithm using the full relay-destination CSI.

In this paper, we consider an interference multiple-input multiple-output (MIMO) relay system where multiple source nodes communicate with their desired destination nodes with the aid of distributed relay nodes. An iterative algorithm is developed to minimize the total source and relay transmit power such that a minimum signal-to-interference-plus-noise ratio (SINR) threshold is maintained at each receiver. The proposed algorithm exploits the network beamforming technique at the relay nodes and the receive beamforming technique at the destination nodes to mitigate the interferences from the unintended sources in conjunction with transmit power control. In particular, we apply the semidefinite relaxation technique to transform the relay transmission power minimization problem into a semidefinite programming (SDP) problem which can be efficiently solved by interior point-based methods. Numerical simulations are performed to demonstrate the effectiveness of the proposed iterative algorithm.

In this paper, we investigate the challenging problem of joint source and relay optimization for two-way linear non-regenerative multiple-input multiple-output (MIMO) relay communication systems. First, a novel relay amplifying matrix is proposed which significantly reduces the computational complexity of the optimal relay design with only a marginal performance degradation. Interestingly, we show that the proposed relay matrix is indeed optimal for some special cases. Second, a semi-definite programming (SDP)-based source matrices optimization algorithm is developed. Then the source and relay optimization algorithms are carried out iteratively to minimized the sum mean-squared error of the signal waveform estimation in a two-way MIMO relay system. The performance of the proposed algorithm is demonstrated by numerical simulations.

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

In this paper, we develop the optimal transmit beamforming vector and the relay amplifying factors for a multiple-input multiple-output (MIMO) relay communication system with distributed relay nodes. Using the optimal beamforming vector, an iterative joint source and relay beamforming algorithm is developed to minimize the mean-squared error (MSE) of the signal waveform estimation. Numerical simulations are carried out to demonstrate the performance of the proposed joint source and relay beamforming algorithm.

In this paper, we address the issue of multiaccess communication through multi-hop linear non-regenerative relays, where all users, all relay nodes, and the destination node may have multiple antennas. Using a linear minimal mean-squared error (MMSE) receiver at the destination node, we demonstrate that the optimal amplifying matrix at each relay node can be viewed as a linear MMSE filter concatenated with another linear filter. As a consequence, the MSE matrix of the signal waveform estimation at the destination node is decomposed into the sum of the MSE matrices at all relay nodes. We show that at a high signal-to-noise ratio (SNR) environment, this MSE matrix decomposition significantly simplifies the solution to the problem of optimizing the source precoding matrices and relay amplifying matrices. Simulation results show that even at the low to medium SNR range, the simplified optimization algorithms have only a marginal performance degradation but a greatly reduced computational complexity and signalling overhead compared with the existing optimal iterative algorithm, and thus are of great interest for practical relay systems.

For two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, the uplink-downlink duality has been recently investigated. In this paper, we establish the duality between uplink and downlink multi-hop AF-MIMO relay channels with any number of hops and any number of antennas at each node, which is a further generalization of several previously established results. We show that in the downlink relay system, signal-to-interference-noise ratios (SINRs) identical to those in the uplink relay system, and vice versa, can be achieved by two approaches. First, with the same total network transmission power constraint, one simply applies Hermitian transposed uplink relay amplifying matrices at relay nodes in the downlink system. Second, with transmission power constraint at each node of the relay network, one can use scaled and Hermitian transposed uplink relay amplifying matrices in the downlink system, with scaling factors obtained by switching power constraints at different nodes of the uplink system. As an application of the uplink-downlink duality, we propose an optimal design of the source precoding matrix and relay amplifying matrices for multi-hop MIMO relay system with a dirty paper coding (DPC) transmitter at the source node.

In this letter, we address multiuser multi-hop multiple-input multiple-output (MIMO) relay communication systems with correlated MIMO fading channels. In particular, we consider the practical scenario where the channel fading is fast and thus the instantaneous channel state information (CSI) is only available at the destination node, but unknown at all users and all relay nodes. We derive the structure of the optimal user precoding matrices and relay amplifying matrices that maximizes the users-destination ergodic sum mutual information. Compared with existing works, our results are more general, since we address multiuser scenarios, consider MIMO relays with a finite dimension, and take into account the noise vector at each relay node.

In this paper, we design the optimal precoding matrices for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay networks. Specifically, we consider a dual-hop relay network and minimize the total power consumed by source and relay under predetermined quality of service (QoS) constraints, i.e., mean square error (MSE) constraints. By using majorization theory, we simplify the matrix-valued problem into a scalar-valued one. Since the problem is non-convex, we then propose two convex suboptimal problems that provide the upper and lower bound of the original objectives. Numerical results demonstrate that the lower bound and the upper bound are tight in high signal-to-noise ratio (SNR).

In this paper, we study multi-hop non-regenerative multiple-input multiple-output (MIMO) relay communications with any number of hops. We design the optimal source precoding matrix and the optimal relay amplifying matrices for such relay network where a nonlinear decision feedback equalizer (DFE) based on the minimal mean-squared error (MMSE) criterion is used at the destination node. We show that when the composite objective function is Schur-convex, the MMSE-DFE receiver together with the optimal source and relay matrices enable an arbitrary number of source symbols to be transmitted at one time, and yield a significantly improved performance compared with non-regenerative MIMO relay systems using linear receivers at the destination.

In this paper, a multicarrier multi-hop multiple-input multiple-output (MIMO) relay system is investigated. A linear non-regenerative strategy is applied at each relay node. We show that for Schur-concave objective functions, the optimal source precoding matrix, the optimal relay amplifying matrices and the optimal receiving matrix jointly diagonalize the multicarrier multi-hop MIMO relay channel. And for Schur-convex objectives, such joint diagonalization along with a rotation of the source precoding matrix is also shown to be optimal. Using the optimal structure of the source and relay matrices, the multi-hop relay design problem boils down to the issue of power loading among the resulting parallel multi-hop single-input single-output (SISO) relay channels. This paper provides additional details of the multicarrier version of some of our recent results.

In this letter we propose linear non-regenerative multicarrier multiple-input multiple-output (MIMO) relay technique that aims to minimize the mean-squared error (MSE) of the signal waveform estimation at the destination. We generalize the existing result on the structure of the optimal relay amplifying matrix by considering the direct source-destination link. To minimize the MSE, a power loading algorithm is developed which has a significantly reduced computational complexity compared with existing techniques.

A cooperative power scheduling algorithm developed by Wang, Krunz and Cui is extended for an ad hoc network of MIMO links. This algorithm, referred to as price-based iterative water filling (PIWF) algorithm, is a distributed algorithm by which each link computes its power scheduling through an iterative and cooperative process. The cooperation among all links is achieved by adaptive price factors applied by each link. Compared to a centralized power scheduling algorithm, the PIWF algorithm is much more efficient in computation although not as efficient in network throughput. Compared to a non-cooperative counter-part by Demirkol and Ingram where all price factors are zero, the PIWF algorithm requires additional in-network computation but is more efficient in network throughput.

In this paper, we study multi-hop non-regenerative multiple-input multiple-output (MIMO) relay communications with any number of hops. We design the optimal source precoding matrix and the optimal relay amplifying matrices for such relay network where a nonlinear minimal mean-squared error (MMSE)-decision feedback equalizer (DFE) is used at the destination node. We first derive the structure of the optimal source and relay matrices. Then based on the link between most commonly used MIMO system design objectives and the diagonal elements of the MSE matrix, we classify the objective functions into two categories: Schur-convex and Schur-concave composite objective functions. We show that when the composite objective function is Schur-convex, the MMSE-DFE receiver together with the optimal source and relay matrices enable an arbitrary number of source symbols to be transmitted at one time, and yield a significantly improved BER performance compared with non-regenerative MIMO relay systems using linear receivers at the destination. We also show that for Schur-concave composite objective functions, the optimal source and relay matrices, and the optimal feed-forward matrix at the destination node jointly diagonalize the multi-hop MIMO relay channel, and thus in such case, the nonlinear MMSE-DFE receiver is essentially equivalent to a linear MMSE receiver.

In this paper we propose non-regenerative multi-carrier multiple-input multiple-output (MIMO) relay techniques that minimize the mean-squared error (MSE) of the signal waveform estimation. We establish the closed-form optimal precoding matrices at the source and relay nodes in the absence of the direct source-destination link. Interestingly, we show that the proposed precoding matrices jointly convert the multicarrier MIMO relay channel into parallel single-input single-output (SISO) relay channels. In order to reduce the computational complexity of the optimal algorithm, a suboptimal precoding approach based on an upper-bound of the MSE expression is developed. Numerical examples illustrate a significant performance improvement of the proposed algorithms over the existing techniques.

In this paper we propose non-regenerative multi- carrier multiple-input multiple-output (MIMO) relay techniques that minimize the mean-squared error (MSE) of the signal waveform estimation. In particular, we consider the practical scenario where the direct source-destination link can not be neglected and develop an alternating technique to minimize the signal MSE. In order to reduce the computational complexity of the alternating algorithm, a suboptimal non-alternating preceding approach is proposed. Numerical examples illustrate a significant performance improvement of the proposed algorithms compared with the existing techniques.

For a two-hop linear non-regenerative multiple-input multiple-output (MIMO) relay system where the direct link between source and destination is negligible, the optimal design of the source and relay matrices has been recently established for a broad class of objective functions. The optimal source and relay matrices jointly diagonalize the MIMO relay system into a set of parallel scalar channels. In this paper, we show that this diagonalization is also optimal for a multihop MIMO relay system with any number of hops, which is a further generalization of several previously established results. Specifically, for Schur-concave objective functions, the optimal source precoding matrix, the optimal relay amplifying matrices and the optimal receiving matrix jointly diagonalize the multihop MIMO relay channel. And for Schur-convex objectives, such joint diagonalization along with a rotation of the source precoding matrix is also shown to be optimal. We also analyze the system performance when each node has the same transmission power budget and the same asymptotically large number of antennas. The asymptotic analysis shows a good agreement with numerical results under a finite number of antennas.

We present an optimal power scheduling scheme to maximize the throughput of a set of distributed multiple-input multiple-output (MIMO) wireless links. This scheme exploits both spatial and temporal freedoms of the source covariance matrices of all MIMO links. In particular, the source covariance matrix of each MIMO link is allowed to vary within a block of time (and/or frequency) slots. This scheme, also referred to as space-time power scheduling, optimizes an integration of link scheduling and power control for MIMO links. The computational problem involved in this scheme is non-convex. However, a gradient-projection algorithm developed for this scheme consistently yields a higher capacity than all other existing schemes.

We examine signaling strategies for wireless MIMO networks with interference. Previous approaches have focused on maximizing either individual or total throughput, resulting in an inefficient or potentially unfair allocation of resources. We propose two methods motivated by game-theoretic results. First, we extend the non-cooperative Nash equilibrium proposed in previous literature. Second, we present a cooperative method based on the Nash bargaining solution which provides an axiomatic arbitration scheme. Simulation results show that the Nash bargaining solution provides a fair allocation of resources without significantly sacrificing total throughput.

A space-time optimal power schedule for multiple distributed MIMO links without the knowledge of channel state information at transmitting nodes is proposed. This new approach exploits both the spatial and temporal freedoms of distributed MIMO links. A readily computable expression for the ergodic sum capacity of the MIMO links is derived. Based on this expression, a projected gradient algorithm is developed to optimize the power allocation. For a symmetric set of MIMO links, it is observed that the space-time optimal power schedule reduces to a uniform isotropic power schedule when nominal interference is low, or to an orthogonal isotropic power schedule when nominal interference is high. Furthermore, the transition region between the latter two schedules is seen to be very small in terms of nominal interference-to-noise ratio.

In this paper, an interesting relationship between the worst-case optimization-based and probability-constrained approaches to the robust adaptive beamformer design is found both in the cases of Gaussian and non-Gaussian steering vector mismatch. The established relationship demonstrates that the probabilistic beamformer design may be approximately interpreted in terms of the worst-case design, and quantifies the parameters of the latter design in terms of the beamformer outage probability.

Two related problems of the design of robust adaptive beamformers and multiuser multiple-input multiple-output (MIMO) receivers are considered. A popular recent solution to these problems is based on the worst-case performance optimization. Unfortunately, in practical applications the actual worst case occurs with a very low probability and, as a result, the worst-case based designs may be overly conservative. As a less conservative alternative to the worst-case designs, the so-called probabilistically constrained designs are introduced. The latter approach guarantees that the distortionless response constraint is satisfied for a mismatched array response with a certain selected probability. Improved flexibility and performance of the robust probabilistically constrained designs with respect to the worst-case designs are illustrated via simulations.

Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs.

In the orthogonal frequency-division multiplexing (OFDM) scheme, some subcarriers may be subject to a deep fading. Adaptive techniques can be applied to mitigate this effect if the channel-state information (CSI) is available at the transmitter. In this paper, we study the performance of an OFDM-based communication system whose transmitter has only one bit of CSI per subcarrier, obtained through a low-rate feedback. Three adaptive approaches are considered to exploit such a CSI feedback: adaptive subcarrier selection; adaptive power allocation (APA); and adaptive modulation selection (AMS). Under the conditions of a constant raw data rate and perfect feedback channel, the performance of these approaches are analyzed and compared in terms of raw bit-error rate. It is shown that one-bit CSI feedback can greatly enhance the system performance. Moreover, imperfections of the feedback channel are considered, and their impact on the performance of these techniques is studied. It is shown that by exploiting the knowledge that the feedback channel is imperfect, the performance of the APA and AMS techniques can be substantially improved

In this paper, we improve the performance of robust linear receivers for multi-user multiple-input multiple-output (MIMO) wireless systems by exploiting the inherent structure of orthogonal space-time block codes (OSTBC). This particular structure results in a worst-case optimization problem with structured uncertainty set. Exploiting this structure, an improved robust linear receiver with a combination of fixed diagonal loading and adaptive non-diagonal loading of the data covariance matrix is obtained.

In the orthogonal frequency division multiplexing (OFDM) scheme, some subcarriers may be subject to a deep fading. Adaptive techniques can be applied to mitigate this effect if the channel state information (CSI) is available at the transmitter. In this paper, we study the performance of an OFDM-based communication system whose transmitter has only one bit (of CSI per subcarrier that is obtained through a low rate feedback. Three adaptive approaches are considered to exploit such a CSI feedback: adaptive subcarrier selection, adaptive power allocation and adaptive modulation selection. Under the condition of constant raw data rate, the performance of these approaches is analyzed and compared in terms of raw bit error rate (BER). We have found that one-bit CSI feedback can greatly enhance the system performance. Among the three approaches, the adaptive subcarrier selection approach is found to have the lowest BER when the feedback is perfect.

The problem of joint space-time decoding and interference rejection in multiple-access MIMO wireless communication systems is considered in the case of erroneous or limited channel state information (CSI) at the receiver. Linear beamforming-type techniques that have an improved robustness in such an imperfect CSI case are proposed.

Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher-way arrays. It decomposes a given array in a sum of multilinear terms. PARAFAC analysis generalizes and unifies common array processing models (like joint diagonalization and ESPRIT); it has found numerous applications from blind multiuser detection and multi-dimensional harmonic retrieval to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on alternating least squares (ALS) optimization, which is matched to Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. We develop an iterative algorithm for least absolute error fitting of general multilinear models, based on efficient interior point methods for linear programming (LP). We also benchmark its performance in Laplacian, Cauchy, and Gaussian noise environments, versus the respective CRBs and the commonly used ALS algorithm.