Generalized Nearest Neighbor Decoding for MIMO Channels with Imperfect Channel State Information

TL;DR

This paper introduces a novel decoding scheme for MIMO channels with imperfect CSI, combining generalized nearest neighbor decoding and SIC, achieving near-capacity performance and outperforming traditional pilot-assisted methods.

Contribution

It proposes a new receiver architecture that effectively handles nonlinear input-output relationships under imperfect CSI, improving decoding efficiency over conventional linear schemes.

Findings

Achieves performance close to perfect CSI capacity.

Outperforms traditional pilot-assisted schemes.

Utilizes generalized mutual information for analysis.

Abstract

Information transmission over a multiple-input-multiple-output (MIMO) fading channel with imperfect channel state information (CSI) is investigated, under a new receiver architecture which combines the recently proposed generalized nearest neighbor decoding rule (GNNDR) and a successive procedure in the spirit of successive interference cancellation (SIC). Recognizing that the channel input-output relationship is a nonlinear mapping under imperfect CSI, the GNNDR is capable of extracting the information embedded in the joint observation of channel output and imperfect CSI more efficiently than the conventional linear scheme, as revealed by our achievable rate analysis via generalized mutual information (GMI). Numerical results indicate that the proposed scheme achieves performance close to the channel capacity with perfect CSI, and significantly outperforms the conventional pilot-assisted scheme, which first estimates the CSI and then uses the estimated CSI as the true one for coherent decoding.