Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

TL;DR

This paper introduces a deep learning-based heuristic search algorithm, HATS, that significantly improves signal detection efficiency in large-scale MIMO systems by reducing complexity while maintaining near-optimal performance.

Contribution

The paper proposes the HATS algorithm, combining deep neural networks with heuristic search to enable efficient, near-optimal detection in large-scale MIMO systems.

Findings

Achieves near-optimal bit error rate in large-scale MIMO systems.

Reduces search complexity by visiting fewer tree nodes.

Maintains bounded memory usage during detection.

Abstract

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at \url{https://github.com/skypitcher/hats}.