Comprehensive Review of Deep Unfolding Techniques for Next-Generation Wireless Communication Systems

Abstract

The massive surge in device connectivity demands higher data rates, increased capacity with low latency and high throughput. Hence, to provide ultra-reliable, low-latency communication with ubiquitous connectivity for Internet-of-Things (IoT) devices, next-generation wireless communication leverages the incorporation of machine learning tools. However, standard data-driven models often need large datasets and lack interpretability. To overcome this, model-driven deep learning (DL) approaches combine domain knowledge with learning to improve accuracy and efficiency. Deep unfolding is a model-driven method that turns iterative algorithms into deep neural network (DNN) layers. It keeps the structure of traditional algorithms while allowing end-to-end learning. This makes deep unfolding both interpretable and effective for solving complex signal processing problems in wireless systems. We first present a brief overview of the general architecture of deep unfolding to provide a solid foundation. We also provide an example to outline the steps involved in unfolding a conventional iterative algorithm. We then explore the application of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, integrated sensing and communication, power allocation, and physical layer security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios. © 2014 IEEE.