Full-duplex relay based on block diagonalisation in multiple-input multiple-output relay systems

In this paper, a comprehensive design and analysis of multiple-input multiple-output (MIMO) full-duplex (FD) relaying systems in a multi-cell environment is investigated, where a multi-antenna amplify-and-forward FD relay station serves multiple half-duplex (HD) multi-antenna users. The pivotal obstacles of loopback self-interference (LI) and multiple co-channel interferers (CCI) at the relay and destination when employing FD relaying in cellular networks are addressed. In contrast to the HD relaying mode, the CCI in the FD relaying mode is predicted to double since the uplink and downlink communications are simultaneously scheduled via the same channel. In this paper, the optimal layout of transmit (receive) precoding (decoding) weight vectors which maximizes the overall signal-to-interference-plus-noise ratio is constructed by a suitable optimization problem, and then a closed-form sub-optimal formula based on null space projection is presented. The proposed hop-by-hop rank-1 zero-forcing (ZF) beamforming vectors are based on added ZF constraints used to suppress the LI and CCI channels at the relay and destination, i.e., the source and relay perform transmit ZF beamforming, while the relay and destination employ receive ZF combining. To this end, unified accurate expressions for the outage probability and ergodic capacity are derived in closed form. In addition, simpler tight lower bound formulas for the outage probability and ergodic capacity are presented. Moreover, the asymptotic approximations for outage probability are considered to gain insights into system behavior in terms of the diversity order and array gain. Numerical and simulation results show the accuracy of the presented exact analytical expressions and the tightness of the lower bound expressions. The case of hop-by-hop maximum-ratio transmission/maximal-ratio combining beamforming is included for comparison purposes. Furthermore, our results show that while multi-antenna terminals improve the system performance, the detrimental effect of CCI on FD relaying is clearly seen. Therefore, our findings unveil that MIMO FD relaying could significantly improve the system performance compared to its conventional MIMO HD relaying counterpart.

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.

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.

In this paper, capacity performance analysis is presented for multiple-input multiple-output (MIMO) relay channels with transmit antenna selection and maximal-ratio combining receive (TAS/MRC) in amplify-and-forward (AF) relay networks operating over flat Rayleigh fading channels. Assuming that the source and destination are equipped with Ns and Nd antennas, respectively, and communicate with each other with the help of a single-antenna relay, we derive the cumulative distribution function (CDF), probability density function (PDF) and moment generation function (MGF) for the system end-to-end SNR. Based on these, we then present closed-form analytical expressions for the calculation of the ergodic capacity and outage capacity for the AF MIMO relay channels with TAS/MRC. Numerical results show that all the derived analytical expressions are very tight and are valid for evaluations of the ergodic and outage capacity under various antenna configurations. The results also show how the number of transmit antennas and receives antennas affect the overall channel capacity under fading channel environments.

In this paper, we investigate the fading two-hop full-duplex (FD) relay channel with self- interference, which is comprised of a source, an FD relay impaired by self-interference, and a destination, where a direct source-destination link does not exist. For this channel, we propose two buffer-aided relaying schemes with adaptive reception-transmission at the FD relay for the cases when the source and the relay both perform adaptive-rate and fixed-rate transmission, respectively. The proposed buffer-aided relaying schemes enable the FD relay to adaptively select to either receive, transmit, or simultaneously receive and transmit in a given time slot based on the qualities of the receiving, transmitting, and self-interference channels; a degree-of-freedom unavailable without buffer-aided relaying. Our numerical results show that significant performance gains are achieved using the proposed buffer-aided relaying schemes compared to conventional FD relaying, where the FD relay is forced to always simultaneously receive and transmit, and to buffer-aided half-duplex relaying, where the half-duplex relay cannot simultaneously receive and transmit. The main conclusion of this work is that FD relaying systems without buffer-aided relaying miss-out on significant performance gains.

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, we consider interference multiple-input multiple-output (MIMO) relay systems where multiple pairs of transmitters–receivers simultaneously communicate through a single relay node and where all nodes are equipped with multiple antennas. We propose a new iterative algorithm to jointly optimize the relay precoding matrix and the receiver matrices of such a system based on the minimum sum mean-squared error (MSE) criterion. The optimal structure of the relay precoding matrix is developed to decrease the computational complexity of transceiver optimization. Simulation results demonstrate that the proposed transceiver optimization algorithm has a better MSE and bit-error-rate (BER) performance and a faster convergence rate than existing works.

Multiple input multiple output (MIMO) relay networks are wireless communication systems comprising of multiple nodes, each of which is equipped with multiple antennas. Information theories have shown that using multiple nodes to simultaneously relay a message can improve the capacity of source-to-destination communications. In this paper, we propose new relaying schemes for MIMO relay networks. The major concept behind the proposed schemes is to transform each of the MIMO relay channels into an equivalent triangular channel with positive real diagonal entries. By doing so, the resultant MIMO relay channel can simultaneously offer both distributed array gain (diversity gain obtained among relay nodes) and intranode array gain (diversity gain realized by multiple antennas of individual relay node) while maintaining the maximum spatial multiplexing gain (number of parallel data pipes). Based on this concept, three relaying schemes are derived that perform QR decomposition and phase control. Numerical results confirm that at least one of the proposed schemes outperforms the amplify-and-forward and the zero-forcing relaying schemes under various conditions. Moreover, we show that ratios of noise power level at relay and destination node have a great impact on capacities.

A multiple-input multiple-output (MIMO) relay network is composed of source, relay, and destination nodes, each of which is equipped with multiple antennas. Network information theory has shown that the use of multiple relay nodes in source and destination (S-D) communication makes the capacity of the S-D system logarithmically increase with the number of relay nodes. In [13], we proposed a MIMO relay scheme for a relay network with single source and destination pair; each of the multiple relay nodes performs QR decomposition of backward and forward channels in conjunction with phase control (QR-P-QR). In this paper, we extend this scheme to the MIMO relay network with multiple source and destination pairs. For this aim, we use the group nulling approach to decompose the multiple S-D MIMO relay channel to parallel independent S-D MIMO relay channels, and apply the QR-P-QR scheme in each of the decomposed MIMO relay links. Numerical examples show that the proposed relay scheme offers higher capacity than the existing zero-forcing (ZF) relaying scheme

Power optimization for maximum channel capacity in MIMO relay system

A two-hop multiple-input multiple-output (MIMO) relay network which comprises a multiple antenna source, an amplify-and-forward MIMO relay and many potential users are studied in this paper. Consider the achievable sum rate as the performance metric, a joint design method for the processing units of the BS and relay node is proposed. The optimal structures are given, which decompose the multiuser MIMO relay channel into several parallel single-input single-output relay channels. With these structures, the signal-to-noise ratio at the destination users is derived; and the power allocation is proved to be a convex problem. We also show that high sum rate can be achieved by pairing each link according to its magnitude. The sum rate of three broadcast strategies, time division multiple access (TDMA) to the strongest user, dirty paper coding (DPC), and beamforming (BF) are investigated. The sum rate bounds of these strategies and the sum capacity (achieved by DPC) gain over TDMA and BF are given. With these results, it can be easily obtained that how far away TDMA and BF are from being optimal in terms of the achievable sum rate.

In this paper, the performance of dual-hop multiple-input multiple-output (MIMO) relay system with transmit beamforming and maximum-ratio combining scheme (MIMO-MRC) for each hop is investigated. Both the relay and destination are subjected to multiple co-channel interferences (CCIs). An exact closed-form outage probability expression is derived for the Rayleigh fading channels. Then, to render more insights into the combined effects of multiple antennas and CCIs, the asymptotic behavior of the outage probability is also presented and discussed. Based on the asymptotic expressions, we explicitly characterize the system diversity order. The results show that the diversity order is essentially directly proportional to the number of antennas at the relay. Furthermore, employing both exact optimal power allocation (OPA) and asymptotic OPA methods, we determine the optimal power allocation (OPA) such that the outage probability is minimized. We show that OPA offers superior performance over uniform power allocation, and highlight a pivotal design choice for maximizing system performance without investing additional resources.

In this paper, the mutual information outage probability (P) for dual-hop decode-and-forward multiple-input multiple-output (MIMO) relay channel is obtained. Rayleigh distribution is used to model MIMO channels, and both full-duplex and half-duplex modes are investigated. Co-channel interference at relay and destination nodes has been considered. Our assumption is based on the result of [1], which establishes that MIMO channel mutual information can be well approximated by a Gaussian distribution. We have found a closed form expression for P given as a function of bivariate Gaussian cumulative density function. The results show for low-medium interference regime that full-duplex performs better than half-duplex mode. On the other hand, when relay self-interference increases, half-duplex outperforms full-duplex mode. Regarding the number of antennas, full-duplex mode performs better than half-duplex mode when number of antennas increases, even in high interference scenario. All the results are validated by numerical Monte Carlo simulations.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

A 2-D shooting-and-bouncing (SBR) ray-tracing method considering ray reflect, transmission and diffraction is used to analyze antenna spacing effect on capacity complementary cumulative distribution functions(CCDFS) of indoor non-line-of-sight (NLOS) MIMO channel and indoor multiple-input multiple-output (MIMO) channel with line-of-sight (LOS) path. The results show that the capacities of MIMO channel with LOS path are less than NLOS MIMO channel capacities in general. But whether in indoor MIMO channel with LOS path or in NLOS indoor MIMO channel, capacities CCDFS of indoor MIMO systems with antenna spacing of 5/spl times/ can reach the theoretical independent and identically distribution (i.i.d) Rayleigh capacities nearly fully. With the decrease of antenna spacing, the capacities dropped also. And the less antenna spacing, the more difference between indoor NLOS MIMO channel capacities and capacities of MIMO channel with LOS path.