Two-Way MIMO Relay Precoder Design with Channel State Information

The joint design of the multi-stream in MIMO relay sigle/multiple user system is investigated. Under the inter-stream interference, a constrained optimization problem with respect to the unknown relay precoder matrix is formulated with a goal of minimizing the total relay transmit power subject to quality of service (QoS) requirement on each data stream. As the original opitimization problem is non-convex, the matrix expression of the cost function and constrained requirement is converting to a scalar optimization problem and the optimal relay precoder and receive filter are obtained. The optimal design of the multiple user relay system under QoS requirement is also provided. By using a reasonable approximation and converting to the generalized Rayleigh form for the cost function, a suboptimal relay precoder matrix for QoS optimization with relay transmit power constraint is obtained. Simulation results demonstrate that for single user model, the minimize relay power will increase with Signal to Interference plus Noise Ratio(SINR) constraint value becoming larger, and for multiple users model, more users in our algorithm will make the MSE gain more obvious as increasing transmitting SNR.

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence.

Random unitary beamforming is one of the schemes reducing the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In multiple-input multiple-output (MIMO) systems, throughput performance is greatly improved using AMC (adaptive modulation and coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, transmitter has only partial channel state information (CSI) of each receiver. Thus, it is difficult for random unitary beamforming to use conventional power allocation methods that use full CSI at all the receivers. In this paper, we propose a new scheduling algorithm with power allocation for downlink random unitary beamforming that improves throughput performance without full CSI. We provide numerical results of the proposed scheduling algorithm and compare them to those of the conventional random unitary beamforming scheduling algorithm.

Considering that wireless unidirectional full-duplex (FD) transmission is one of the FD transmissions over 5G mobile wireless networks, this paper proposes a joint receive and transmit relay beamformer design for systems consisting of a source, a FD relay, an intended destination, and an eavesdropper. The source and destination are single-antenna nodes, whereas the FD relay is a multi-antenna node. Assuming that full channel state information (CSI) is available and the eavesdropper processes two blocks of received data at a time, the joint optimization problem is reformulated as a semidefinite relaxation (SDR) problem (in terms of relay transmit beamformer), which is tight in the sense that the rank-one beamformer can always be recovered from the SDR solution. In order to significantly minimize the computational cost of the SDR implementation, a novel semi-analytical approach based on dual optimization is proposed to obtain the optimum transmit beamformer. When the number of data blocks is arbitrary, we propose a successive convex approximation approach for solving the original non-convex optimization problem and show that a stationary solution can be efficiently obtained. We then propose a distributionally robust beamformer design for the case of an arbitrary number of blocks, assuming that the means and covariance matrices of the eavesdropper's channels are known. Computer simulations show that the proposed semi-analytical solution matches with that obtained from numerical optimization of the SDR problem, the FD scheme significantly outperforms its half-duplex counterpart, and the proposed robust design provides robustness against CSI uncertainty.

In this paper, we characterize the ergodic capacity of Multiple Input Multiple Output (MIMO) Rayleigh fading channels with full channel state information (CSI) at both the transmitter (CSI-T) and the receiver (CSI-R) at asymptotically low signal-to-noise ratio (SNR). A simple analytical expression of the capacity is derived for any number of transmit and receive antennas. This characterization clearly shows the substantial gain in terms of capacity over the no CSI-T case and gives a good insight on the effect of the number of antennas used. In addition, an On-Off transmission scheme is proposed and is shown to be asymptotically capacity-achieving.

This paper investigates the linear precoder optimization for packet retransmissions in nonregenerative multi-input multi-output (MIMO) relay systems. To fully utilize the time diversity provided by automatic repeat request (ARQ), progressive linear precoders are designed with the objective of maximizing the mutual information delivered by multiple transmissions of the same packet. Firstly, the relay-only precoder is considered when the channel state information (CSI) is only available at the relay and the destination. We derive the form of the optimal ARQ precoder by restating the optimization problem as a matrix diagonalization problem, then the power allocation is formulated as a convex optimization problem where the Karush-Kuhn-Tucker conditions are used to obtain the optimal solution. Furthermore, we analyze the asymptotic performance when the two hops' signal-to-noise ratio (SNR) goes to infinity or zero respectively. Secondly, the joint source and relay precoder is studied assuming the CSI known at all nodes. The ARQ precoder is derived by matrix diagonalization and channel pairing between previous subchannels and current subchannels. Then the power allocation at the source and relay is formulated as a nonconvex multi-variable optimization problem where we employ an iterative algorithm to obtain the suboptimal solution. Moreover, the asymptotic performance is also analyzed with the two hops' SNR being extremely large or small, based on which the simplified method of searching channel pairing matrix is proposed. Numerical results show that the proposed precoders can improve the system performance significantly, especially in the joint source and relay precoder scenario.

In a wireless multi-user system using multiple antennas, full multiplexing gain can be achieved through perfect channel state information (CSI) at the base station (BS). However, because of the limitations on the feedback rate and of standard requirements, such as the 3GPP, it is difficult to obtain perfect CSI, or even partial CSI with the extremely large number of feedback bits needed at the region of high signal-to-noise ratios (SNR) in practical systems. Instead of a feedback channel resource, this paper considers another resource for the system, a cooperation channel, which can be used by users to participate in cooperation in a broadcast fading channel. The cooperation among users in a multi-user system has the effect of transforming it into a cooperative point-to-point MIMO system. This paper analyzes the cooperation channel gain and the mobile power used in a cooperation channel to maintain a constant rate gap against the zero-forcing beamforming scheme with full CSI at the BS. In addition, the performance results of this proposed cooperation strategy are provided by computer simulations.

Random unitary beamforming is one of the schemes that can reduce the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In Multiple-Input Multiple-Output (MIMO) systems, throughput performance is greatly improved using AMC (Adaptive Modulation and Coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, the transmitter has only partial channel state information (CSI) of each receiver. Thus, it is difficult for random unitary beamforming to use conventional power allocation methods that assumes that all receivers has full CSI. In this paper, we propose a new scheduling algorithm with power allocation for downlink random unitary beamforming that improves throughput performance without full CSI. We provide numerical results of the proposed scheduling algorithm and compare them to those of the conventional random unitary beamforming scheduling algorithm. We show that random unitary beamforming achieves the best system throughput performance with two transmit antennas. We also show that the proposed algorithm attains higher throughput with the small increase of feedback than the random unitary beamforming scheduling algorithm.

In this letter, energy-efficient transmission and power allocation for fixed-gain amplify-and-forward relay networks with partial channel state information (CSI) are studied. In the energy-efficiency problem, the total power consumed is minimized while keeping the signal-to-noise-ratio (SNR) above a certain threshold. In the dual problem of power allocation, the end-to-end SNR is maximized under individual and global power constraints. Closed-form expressions for the optimal source and relay powers and the Lagrangian multiplier are obtained. Numerical results show that the optimal power allocation with partial CSI provides comparable performance as optimal power allocation with full CSI at low SNR.

This paper proposes a novel approach for maximising the energy efficiency (EE) of the two-way two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system by optimising its sources and relay precoders. More precisely, we derive a closed-form expression of the optimal source precoders (for a known relay precoder) and obtain the optimal relay precoder (for known source precoders) through quasiconvex optimisation. An alternating optimisation process is then utilised to maximize the EE of the system. Simulation results show that our novel approach can improve the EE of two-way MIMO-AF systems by ∽50% or more when compared with existing EE-based one-wav or two-wav MTMO-AF precoding schemes.

In this paper, we consider minimum-mean-square error (MMSE) training-based channel estimation for two-hop multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) relaying systems. The channel estimation process is divided into two main phases. The relay-destination channel is estimated in the first phase and can be obtained using well-known point-to-point MIMO OFDM estimation methods. In the second phase, the source-relay channel is estimated at the destination with the use of a known training sequence that is transmitted from the source and forwarded to the destination by a nonregenerative relay. To obtain an estimate of the source-relay channel, the source training sequence, relay precoder, and destination processor, require to be optimized. To solve this problem, we first derive an iterative algorithm that involves sequentially solving a number of convex optimization problems to update the source, relay, and destination design variables. Since the iterative algorithm may be too computationally expensive for practical implementation, we then derive simplified solutions that have reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, we propose a robust collaborative relay beamforming scheme in the presence of imperfect channel state information (CSI) for a multiple single-antenna two-way relays network with reciprocal channels. Many existing beamforming schemes have been investigated using perfect CSI for relay-aided systems. However, in practical communication systems the channel estimation is always inaccurate at relay nodes, leading to the deviation between the true CSI and the estimated CSI. Aiming to tackle the performance degradation caused by the CSI mismatch, a constrained optimization problem corresponding to the robust relay beamforming vector is formulated, in which the channel estimation error is characterized by the stochastic error model. Herein the commonly used performance metric, expected weighted sum mean squared error, is adopted as design objective to evaluate the overall link reliability. It is found that due to the intractable expression of the cost function, we can not resort to the traditional methods for solving the involved optimization problem. Whereas, by exploring the lower bound of the cost function using Jensen's inequality and the Taylor series expansion, the original optimization problem is recast to a convex optimization in the form of Rayleigh---Ritz ratio, which results in a closed-form robust relay beamforming solution. Numerical results verify the robustness and superiority of the proposed scheme against CSI errors in terms of system sum rate and average BER.

Media-based modulation (MBM) is an attractive modulation scheme that offers MIMO benefits by placing digitally controlled parasitic elements as radio frequency (RF) mirrors near the transmit antenna. Different realizations of the ON/OFF status of these mirrors result in different channel fades which constitute the channel modulation alphabet. In this paper, we investigate full-duplex (FD) communication using MBM. First, we study full-duplex MBM (FD-MBM) scheme with full channel state information (CSI). Our numerical results show that, for the same spectral efficiency, FD-MBM outperforms FD with conventional modulation (FD-CM). Next, in order to relax the full CSI assumption, we investigate FD with differential MBM (FD-DMBM) with no CSI/partial CSI, where partial CSI refers to the availability of either self-interference CSI or desired-signal CSI. We propose detectors for the FD-DMBM with no CSI/partial CSI and illustrate their bit error performance.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011