Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

TL;DR

This paper introduces a deep learning-enhanced sphere decoding method for large MIMO systems that predicts path metrics to reduce computational complexity while maintaining near-optimal performance.

Contribution

It presents a novel neural network-based approach to predict minimum path metrics, optimizing search order and initial radius in sphere decoding for large MIMO systems.

Findings

Significant reduction in computational complexity compared to conventional SD.

Achieves near-optimal detection performance.

Effective early termination scheme based on predicted metrics.

Abstract

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input--multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the ``deep'' paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.