SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection With Low and Fixed Complexity

TL;DR

SUMIS is a novel MIMO detection method that achieves near-optimal soft-input soft-output performance with fixed, low complexity, predictable runtime, and high parallelizability, suitable for various practical wireless system configurations.

Contribution

The paper introduces SUMIS, a new MIMO detection algorithm that offers near-optimal performance with fixed complexity and no search or branching, enabling efficient parallel implementation.

Findings

SUMIS achieves near-optimal detection performance in various MIMO scenarios.

The algorithm has predictable, fixed computational complexity.

SUMIS performs well with large antenna systems and different channel knowledge conditions.

Abstract

The fundamental problem of our interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.