Intelligent MIMO Detection Using Meta Learning

TL;DR

This paper introduces a meta learning-based deep neural network approach for MIMO detection that adaptively adjusts the parameter K, achieving near-ML performance with reduced complexity and fast training capabilities.

Contribution

It proposes a novel meta learning framework to optimize the K parameter in MIMO detection, improving efficiency and performance over traditional fixed K methods.

Findings

Achieves near-ML detection performance

Reduces detection complexity close to linear detectors

Demonstrates fast training capability

Abstract

In a K-best detector for multiple-input-multiple-output(MIMO) systems, the value of K needs to be sufficiently large to achieve near-maximum-likelihood (ML) performance. By treating K as a variable that can be adjusted according to a fitting function of some learnable coefficients, an intelligent MIMO detection network based on deep neural networks (DNN) is proposed to reduce complexity of the detection algorithm with little performance degradation. In particular, the proposed intelligent detection algorithm uses meta learning to learn the coefficients of the fitting function for K to circumvent the problem of learning K directly. The idea of network fusion is used to combine the learning results of the meta learning component networks. Simulation results show that the proposed scheme achieves near-ML detection performance while its complexity is close to that of linear detectors. Besides, it also exhibits strong ability of fast training.