Enhancing Wireless Physical Layer Performance with Deep-Learning Techniques: A Comprehensive Review | ||
| JES. Journal of Engineering Sciences | ||
| Articles in Press, Accepted Manuscript, Available Online from 21 September 2025 | ||
| Document Type: Review Paper | ||
| DOI: 10.21608/jesaun.2025.415134.1696 | ||
| Authors | ||
| Eman Ismail* ; Mohamed Abo Zahhad; Abdelhay Ali; Osama o Elnahas | ||
| Department of Electrical Engineering, Faculty of Engineering, Assuit University, Assuit, Egypt | ||
| Abstract | ||
| The physical layer (PHY) is fundamental to wireless communication systems, enabling robust signal transmission in complex and dynamic environments. Traditional PHY designs follow a block-based, model-driven approach where components—such as channel coding, modulation, and channel estimation—are optimized independently under simplified assumptions, often leading to performance degradation in real-world scenarios. Deep Learning (DL) offers a data-driven alternative capable of learning complex, nonlinear mappings directly from data, improving adaptability and accuracy under diverse channel conditions. This paper reviews recent advances (2020–2025) in applying DL to enhance key PHY functions, including channel estimation, signal detection, modulation classification, coding/decoding, beamforming, and physical layer security. We examine various neural architectures—such as CNNs, RNNs, autoencoders, GANs, and reinforcement learning agents— highlighting their roles in next-generation networks (5G, B5G, 6G). While DL demonstrates superior performance over conventional methods in adaptability, spectral efficiency, and robustness, challenges remain in computational complexity, interpretability, training data scarcity, and generalization across environments. The review synthesizes state-of-the-art methods, identifies open issues, and outlines future research trends—such as model-driven DL, transfer/meta-learning, edge intelligence, and crosslayer optimization—towards building intelligent, adaptive, and scalable PHY designs for future wireless systems. | ||
| Keywords | ||
| deep learning; physical layer; AI in wireless communications | ||
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