Tensor-Based Joint Channel Estimation and Symbol Detection for AF MIMO Relay Networks

Abstract

A study on the joint channel and symbol estimation issue is provided for two hop relay networks which employ the amplify-and-forward (AF) relaying approach. The encoding scheme at the source node introduces the time-domain spreading with a time-varying linear constellation precoding. Then, a set of amplifying factors matrices is utilized by the relays to amplify and forward the received data to the destination. The received signal at the destination can be constructed as a fourth-order tensor model, which is referred to as the nested parallel factor (PARAFAC) model. And then, we present a novel Levenberg-Marquardt (LM) algorithm based on this tensor model. The proposed method does not require complex signal processing at the relay, which effectively reduces the burden of relay. As a semi-blind method, which does not require the pilot signal, the proposed receiver can jointly recover the channels and information symbols. Moreover, the proposed semi-blind receiver is robust as it can work in different wireless channel scenarios. Simulations are conducted to demonstrate the efficiency of the proposed semi-blind approach.