Understanding Deep MIMO Detection

TL;DR

This paper analyzes deep learning-based MIMO detection methods, providing theoretical insights into their performance, comparing data-driven and model-driven architectures, and validating findings through simulations for various signal systems.

Contribution

It offers the first comprehensive performance analysis of DL-based MIMO detectors, clarifying their strengths, weaknesses, and guiding principles for network design.

Findings

Data-driven DL detector approaches MAP performance with enough training.

Model-driven DL detector requires less training but depends on the iterative algorithm.

Simulations confirm analytical insights and effectiveness in linear and nonlinear systems.

Abstract

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most DL-based detection algorithms are lack of theoretical interpretation on internal mechanisms and could not provide general guidance on network design. In this paper, we analyze the performance of DL-based MIMO detection to better understand its strengths and weaknesses. We investigate two different architectures: a data-driven DL detector with a neural network activated by rectifier linear unit (ReLU) function and a model-driven DL detector from unfolding a traditional iterative detection algorithm. We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes model expert knowledge to alleviate the impact of channels and establish a relatively reliable detection method with a small set of training data. Due to its model specific property, the performance of model-driven DL detector is largely determined by the underlying iterative detection algorithm, which is usually suboptimal compared to the MAP detector. Simulation results confirm our analytical results and demonstrate the effectiveness of DL-based MIMO detection for both linear and nonlinear signal systems.