Rapidly Time-Varying Channel Estimation for Full-Duplex Amplify-and-Forward One-Way Relay Networks (original) (raw)

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.

Training-Based Synchronization and Channel Estimation in AF Two-Way Relaying Networks

2014

Two-way relaying networks (TWRNs) allow for more bandwidth efficient use of the available spectrum since they allow for simultaneous information exchange between two users with the assistance of an intermediate relay node. However, due to superposition of signals at the relay node, the received signal at the user terminals is affected by multiple impairments, i.e., channel gains, timing offsets, and carrier frequency offsets, that need to be jointly estimated and compensated. This paper presents a training-based system model for amplify-and-forward (AF) TWRNs in the presence of multiple impairments and proposes maximum likelihood and differential evolution based algorithms for joint estimation of these impairments. The Cramer-Rao lower bounds (CRLBs) for the joint estimation of multiple impairments are derived. A minimum mean-square error based receiver is then proposed to compensate the effect of multiple impairments and decode each user's signal. Simulation results show that t...