MIMO APP Receiver Processing with Performance-Determined Complexity

TL;DR

This paper proposes a flexible MIMO receiver processing framework that adapts complexity based on transmission conditions and target error rates, achieving significant resource savings while maintaining performance.

Contribution

It introduces a novel adaptive processing framework for MIMO receivers that adjusts complexity according to channel conditions and error rate requirements.

Findings

Significant complexity reduction at the detector and decoder levels.

Minor modifications enable adaptive complexity control.

Maintains near-optimal performance with reduced resource use.

Abstract

Typical receiver processing, targeting always the best achievable bit error rate performance, can result in a waste of resources, especially, when the transmission conditions are such that the best performance is orders of magnitude better than the required. In this work, a processing framework is proposed which allows adjusting the processing requirements to the transmission conditions and the required bit error rate. It applies a-posteriori probability receivers operating over multiple-input multiple-output channels. It is demonstrated that significant complexity savings can be achieved both at the soft, sphere-decoder based detector and the channel decoder with only minor modifications.