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