Groupwise Neighbor Examination for Tabu Search Detection in Large MIMO systems

TL;DR

This paper introduces a neighbor-grouped TS algorithm for large MIMO systems that significantly reduces computational complexity without sacrificing detection performance, especially in high-order modulation schemes.

Contribution

The paper proposes a novel neighbor-grouped TS detection method and a channel ordering scheme that together reduce complexity in large MIMO systems with high-order modulation.

Findings

Achieves up to 85% complexity reduction

No performance loss compared to conventional TS

Effective in high-order modulation schemes

Abstract

In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the metrics of all neighboring vectors are computed to determine the best one to move to. This strategy requires high computational complexity, especially in large MIMO systems with high-order modulation schemes such as 16- and 64-QAM signaling. This paper proposes a novel reduced-complexity TS detection algorithm called neighbor-grouped TS (NG-TS), which divides the neighbors into groups and finds the best neighbor by using a simplified cost function. Furthermore, based on the complexity analysis of NG-TS, we propose a channel ordering scheme that further reduces its complexity. Simulation results show that the proposed NG-TS with channel ordering can achieve up to 85% complexity reduction with respect to the conventional TS algorithm with no performance loss in both low- and higher-order modulation schemes.