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

S. Deka,K. Deka,Nhan Thanh Nguyen,Sanjeev Sharma,Vimal Bhatia,N. Rajatheva

Published 2025 in IEEE Internet of Things Journal

ABSTRACT

The massive surge in device connectivity demands higher data rates, increased capacity with low latency, and high throughput. Hence, to provide ultrareliable, 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 (ISAC), 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.

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