Deep Learning-Aided Tabu Search Detection for Large MIMO Systems

TL;DR

This paper introduces a deep learning-enhanced tabu search detection method for large MIMO systems, significantly reducing computational complexity while maintaining detection performance.

Contribution

It proposes a novel deep neural network (FS-Net) for initial solution approximation and integrates it into an improved tabu search algorithm with adaptive termination.

Findings

Achieves approximately 90% complexity reduction for 32x32 MIMO systems.

Maintains similar detection performance as existing algorithms.

Demonstrates effectiveness through simulation results.

Abstract

In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net. Furthermore, in this algorithm, an adaptive early termination algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a $32 \times 32$ MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance.