Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

TL;DR

This paper introduces a semi-supervised MIMO detection method using a cycle-consistent GAN that models transmission and detection processes, reducing training overhead and improving performance over existing methods.

Contribution

The paper proposes a novel CycleGAN-based semi-supervised MIMO detector that models transmission and detection in a bidirectional loop, enhancing detection accuracy with less labeled data.

Findings

Outperforms existing semi-blind deep learning detection methods in BER and rate.

Effectively reduces training overhead by using semi-supervised learning.

Handles nonlinear power amplifier effects better than traditional detectors.

Abstract

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter.