Simultaneous Estimation of Multi-Relay MIMO Channels

This paper addresses training-based channel estimation
in distributed amplify-and-forward (AF) multi-input multi-output
(MIMO) multi-relay networks. To reduce channel estimation overhead
and delay, a training algorithm that allows for simultaneous
estimation of the entire MIMO cooperative network’s channel
parameters at the destination node is proposed. The exact Cram´er-
Rao lower bound (CRLB) for the problem is presented in closedform.
Channel estimators that are capable of estimating the overall
source-relay-destination channel parameters at the destination are
also derived. Numerical results show that while reducing delay,
the proposed channel estimators are close to the derived CRLB
over a wide range of signal-to-noise ratio values and outperform
existing channel estimation methods. Finally, extensive simulations
demonstrate that the proposed training method and channel estimators
can be effectively deployed in combination with cooperative
optimization algorithms to significantly enhance the performance
of AF relaying MIMO systems in terms of average-bit-error-rate.