Capacity maximization in eigen-MIMO with channel estimation and CSI feedback-link throughput constraint

Abstract

Water-filled eigenchannels offer the highest multi-input multi-output MIMO information-theoretic capacity, but digital techniques such as quadrature amplitude modulation and finite block lengths will degrade the capacity from the Shannon limit to the capacity of a digital link. Furthermore, eigen-MIMO requires channel overheads, such as estimating the channel state information CSI and feeding it back to the transmitter, which further compromise the capacity. In this paper, the joint influence of channel estimation and imperfect feedback on the information-theoretic capacity and the practicable capacity is analyzed. The channel is modeled as static over a MIMO channel block. In each block, the forward channel is used for CSI estimation and for the payload data transmission. In the back direction, the channel is used to feed back a quantized form of the CSI to the transmitter with a throughput constraint. These three channel usages are combined into an effective simplex channel simplifying the capacity analysis. The capacities are formulated as functions of the link parameters, enabling optimization of the number of training symbols, the feedback duration, and the power allocation for training and data transfer, with the criterion of maximum capacity. The results presented are subject to the usual approximations used in communications theory. Copyright © 2012 John Wiley & Sons, Ltd.