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

TL;DR

This paper reviews deep unfolding techniques in wireless communication, highlighting their interpretability, integration of domain knowledge, and recent advancements across various tasks relevant to 6G technologies.

Contribution

It provides a comprehensive overview of deep unfolding applications in wireless communication, emphasizing recent developments and future challenges.

Findings

Deep unfolding improves interpretability of neural networks in communication tasks.

Applications include signal detection, channel estimation, and beamforming for 6G.

Identifies challenges and proposes future directions for deep unfolding methods.

Abstract

The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhances interpretability by transforming complex iterative algorithms into structured layers of deep neural networks (DNNs). This approach seamlessly integrates domain knowledge with deep learning (DL), leveraging the strengths of both methods to simplify complex signal processing tasks in communication systems. To provide a solid foundation, we first present a brief overview of DL and deep unfolding. We then explore the applications of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, sensing and communication, power allocation, and 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.