Approximate MIMO Iterative Processing with Adjustable Complexity Requirements

TL;DR

This paper proposes a practical iterative decoding framework for MIMO systems that approximates soft information exchange, enabling adjustable complexity decoding while maintaining near-optimal performance, especially under favorable channel conditions.

Contribution

It introduces an approximate soft-information exchange method for MIMO iterative decoding that reduces complexity and adapts to transmission conditions without sacrificing performance.

Findings

Achieves near-optimal BER with reduced decoding complexity

Provides adjustable complexity based on channel conditions

Maintains performance close to exact soft information exchange

Abstract

Targeting always the best achievable bit error rate (BER) performance in iterative receivers operating over multiple-input multiple-output (MIMO) channels may result in significant waste of resources, especially when the achievable BER is orders of magnitude better than the target performance (e.g., under good channel conditions and at high signal-to-noise ratio (SNR)). In contrast to the typical iterative schemes, a practical iterative decoding framework that approximates the soft-information exchange is proposed which allows reduced complexity sphere and channel decoding, adjustable to the transmission conditions and the required bit error rate. With the proposed approximate soft information exchange the performance of the exact soft information can still be reached with significant complexity gains.