Channel estimation for multi-way quantized distributed wireless relaying

Abstract

A key focus of wireless communications research is on solutions that catalyze broadband access everywhere at anytime. Relaying has been introduced as a solution to enable communication between users who suffer from poor channel conditions. Distributed relay networks are a special case where spatial diversity is obtained by using a relay consisting of many geographically dispersed nodes. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit data simultaneously through the uplink to the relay nodes of the relay. Each node independently quantizes the observed signal to a few bits and broadcasts these bits through the band-limited downlink channel to the users. In this paper, we consider a multi-way quantized distributed relay network where the relay facilitates the communication between many users. For decoding purposes, we develop algorithms that can be employed by the users to estimate their uplink channels as well as the uplink channels observed by all other users when nodes perform simple sign quantization. A near maximum likelihood (nearML) channel estimator is derived. In addition, other sub-optimal estimators that are more computationally efficient than the nearML technique are also presented. Via simulation, we compare the performance of the proposed channel estimators.