Reliable and Low-Complexity MIMO Detector Selection using Neural Network

TL;DR

This paper introduces a neural network-based method for dynamically selecting low-complexity MIMO detectors in 5G systems, significantly reducing computational effort while maintaining error rates.

Contribution

It formulates a high-dimensional detector selection problem, decomposes it, and trains a neural network to reliably choose detectors based on channel conditions, reducing complexity by about 10 times.

Findings

Reduced MIMO detection complexity by approximately 10x.

Maintained BLER close to that of dimension-reduced ML detection.

Validated performance improvements in 5G NR simulations.

Abstract

In this paper, we propose to dynamically select a MIMO detector using neural network for each resource element (RE) in the transport block of 5G NR/LTE communication system. The objective is to minimize the computational complexity of MIMO detection while keeping the transport block error rate (BLER) close to the BLER when dimension-reduced maximum-likelihood (DR-ML) detection is used. A detector selection problem is formulated to achieve this objective. However, since the problem is high dimensional and NP-hard, we first decompose the problem into smaller problems and train a multi-layer perceptron (MLP) network to obtain the solution. The MLP network is trained to select a low-complexity, yet reliable, detector using instantaneous channel condition in the RE. We first propose a method to generate a labeled dataset to select a low-complexity detector. Then, the MLP is trained twice using quasi-Newton method to select a reliable detector for each RE. The performance of online detector selection is evaluated in 5G NR link level simulator in terms of BLER and the complexity is quantified in terms of the number of Euclidean distance (ED) computations and the number of real additions and multiplication. Results show that the computational complexity in the MIMO detector can be reduced by ~10X using the proposed method.