Near-Optimal Low-Complexity MIMO Detection via Structured Reduced-Search Enumeration
This addresses the problem of efficient MIMO detection for wireless communication systems, representing an incremental improvement over existing methods.
The paper tackles the high computational complexity of maximum-likelihood detection in MIMO systems by proposing a structured reduced-search enumeration method, achieving near-ML performance with linear complexity in constellation size, as shown in simulations for up to 8x8 systems with specific list sizes.
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.