In this paper, we propose a model-driven deep learning network for
multiple-input multiple-output (MIMO) detection. The structure of the network
is specially designed by unfolding the iterative algorithm. Some trainable
parameters are optimized through deep learning techniques to improve the
detection performance. Since the number of trainable variables of the network
is equal to that of the layers, the network can be easily trained within a very
short time. Furthermore, the network can handle time-varying channel with only
a single training. Numerical results show that the proposed approach can
improve the performance of the iterative algorithm significantly under Rayleigh
and correlated MIMO channels.

本文提出了一种基于模型驱动的深度学习网络用于多输入多输出（MIMO）检测，通过深度学习技术优化网络的可训练参数以提高检测性能；由于网络可训练变量的数量等于层数，因此可以在很短的时间内轻松训练网络，并且网络可以处理时变信道，通过数值结果表明该方法可以显著提高 Rayleigh 和相关 MIMO 信道下迭代算法的性能。

面向 MIMO 检测的模型驱动深度学习网络

A Model-Driven Deep Learning Network for MIMO Detection

In this article, we use deep neural networks (DNNs) to develop a wireless
end-to-end communication system, in which DNNs are employed for all
signal-related functionalities, such as encoding, decoding, modulation, and
equalization. However, accurate instantaneous channel transfer function,
\emph{i.e.}, the channel state information (CSI), is necessary to compute the
gradient of the DNN representing. In many communication systems, the channel
transfer function is hard to obtain in advance and varies with time and
location. In this article, this constraint is released by developing a channel
agnostic end-to-end system that does not rely on any prior information about
the channel. We use a conditional generative adversarial net (GAN) to represent
the channel effects, where the encoded signal of the transmitter will serve as
the conditioning information. In addition, in order to deal with the
time-varying channel, the received signal corresponding to the pilot data can
also be added as a part of the conditioning information. From the simulation
results, the proposed method is effective on additive white Gaussian noise
(AWGN) and Rayleigh fading channels, which opens a new door for building
data-driven communication systems.

本文提出了使用深度神经网络的端到端通信系统，并使用条件生成对抗网络表示了通道效应，从而构建了一种不需要先前信息的通道不可知的端到端系统。