The stringent performance requirements of future wireless networks, such as ultra-high data rates, extremely high reliability and low latency, are spurring worldwide studies on defining the next-generation multiple-input multiple-output (MIMO) transceivers. For the design of advanced transceivers in wireless communications, optimization approaches often leading to iterative algorithms have achieved great success for MIMO transceivers. However, these algorithms generally require a large number of iterations to converge, which entails considerable computational complexity and often requires fine-tuning of various parameters. With the development of deep learning, approximating the iterative algorithms with deep neural networks (DNNs) can significantly reduce the computational time. However, DNNs typically lead to black-box solvers, which requires amounts of data and extensive training time. To further overcome these challenges, deep-unfolding has emerged which incorporates the benefits of both deep learning and iterative algorithms, by unfolding the iterative algorithm into a layer-wise structure analogous to DNNs. In this article, we first go through the framework of deep-unfolding for transceiver design with matrix parameters and its recent advancements. Then, some endeavors in applying deep-unfolding approaches in next-generation advanced transceiver design are presented. Moreover, some open issues for future research are highlighted.

翻译：未来无线网络对极致性能的要求，例如超高速率、极高可靠性与低延迟，正推动全球范围内关于定义下一代多输入多输出（MIMO）收发机的研究。在无线通信中先进收发机的设计上，常导出迭代算法的优化方法已为MIMO收发机取得了巨大成功。然而，这些算法通常需要大量迭代才能收敛，这带来了显著的计算复杂度，且常需精细调节各类参数。随着深度学习的发展，利用深度神经网络（DNN）近似迭代算法可大幅降低计算时间。然而，DNN通常导致黑箱求解器，需要大量数据与漫长的训练时间。为进一步克服这些挑战，深度展开技术应运而生，它将迭代算法展开为类似于DNN的逐层结构，融合了深度学习与迭代算法的优势。本文首先梳理了基于矩阵参数的收发机设计深度展开框架及其最新进展。随后，介绍了深度展开方法在下一代先进收发机设计中的若干应用尝试。最后，指出了未来研究中值得关注的一些开放性问题。