Deep-Unfolding for Next-Generation Transceivers
Qiyu Hu Yunlong Cai Guangyi Zhang Guanding Yu Geoffrey Ye Li
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.