Near-Optimal Low-Complexity MIMO Detection via Structured Reduced-Search Enumeration

TL;DR

This paper introduces a structured reduced-search method for MIMO detection that achieves near-ML performance with linear complexity, enabling efficient high-order MIMO detection in practical systems.

Contribution

The paper presents a novel structured reduced-search enumeration technique that significantly reduces complexity while maintaining near-ML detection performance in MIMO systems.

Findings

List sizes of 3|X| to 8|X| achieve near-ML performance.

Method performs well up to 8x8 MIMO systems.

Simulation results confirm robustness under high channel conditions.

Abstract

Maximum-likelihood (ML) detection in high-order MIMO systems is computationally prohibitive due to exponential complexity in the number of transmit layers and constellation size. In this white paper, we demonstrate that for practical MIMO dimensions (up to 8x8) and modulation orders, near-ML hard-decision performance can be achieved using a structured reduced-search strategy with complexity linear in constellation size. Extensive simulations over i.i.d. Rayleigh fading channels show that list sizes of 3|X| for 3x3, 4|X| for 4x4, and 8|X| for 8x8 systems closely match full ML performance, even under high channel condition numbers, |X| being the constellation size. In addition, we provide a trellis based interpretation of the method. We further discuss implications for soft LLR generation and FEC interaction.