Disintegrated Channel Estimation in Filter-and-Forward Relay Networks

This paper investigates a disintegrated channel estimation technique required to accomplish the spatial diversity supported by cooperative relays. The relaying strategy considered herein is a filter-and-forward (FF) relaying method with superimposed training sequences for separately estimating the backhaul and access channels. To reduce inter-relay interference, a generalized filtering technique is proposed and investigated thoroughly. Unlike the interference suppression method commonly used in the conventional FF relay networks, the generalized filtering matrix essentially multiplexes the superimposed training sequences from different relays to the destination by time-division multiplexing, frequency-division multiplexing, and code-division multiplexing methods. The Bayesian Cramér-Rao lower bounds (BCRBs) for this channel estimation problem are derived as the estimation performance benchmarks. The mean square errors (MSEs) of the disintegrated channel estimates are also derived. Finally, the improvements offered by the proposed technique are verified by comprehensive computer simulations in conjunction with the calculations of the BCRBs and the MSEs derived in this paper.