Joint source and relay design for MIMO multi-relay systems using projected gradient approach

Apriana Toding,Yue Rong,Muhammad Ra Khandaker

doi:10.1186/1687-1499-2014-151

Abstract

Abstract In this paper, we develop the optimal source precoding matrix and relay amplifying matrices for non-regenerative multiple-input multiple-output (MIMO) relay communication systems with parallel relay nodes using the projected gradient (PG) approach. We show that the optimal relay amplifying matrices have a beamforming structure. Exploiting the structure of relay matrices, an iterative joint source and relay matrices optimization algorithm is developed to minimize the mean-squared error (MSE) of the signal waveform estimation at the destination using the PG approach. The performance of the proposed algorithm is demonstrated through numerical simulations.

Highlights

Multiple-input multiple-output (MIMO) relay communication systems have attracted much research interest and provided significant improvement in terms of both spectral efficiency and link reliability [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
We propose a jointly optimal source precoding matrix and relay amplifying matrices design for a two-hop non-regenerative multiple-input multiple-output (MIMO) relay network with multiple relay nodes using the projected gradient (PG) approach
3 Joint source and relay matrix optimization we address the joint source and relay matrix optimization problem for MIMO multi-relay systems with a linear minimum mean-squared error (MMSE) receiver at the destination node

Summary

Introduction

Multiple-input multiple-output (MIMO) relay communication systems have attracted much research interest and provided significant improvement in terms of both spectral efficiency and link reliability [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. Many works have studied the optimal relay amplifying matrix for the source-relay-destination channel. In [4,5,6], the optimal relay amplifying matrix was designed to minimize the mean-squared error (MSE) of the signal waveform estimation at the destination.

Results

Conclusion