End-to-End Deep Learning in Wireless Communication Systems: A Tutorial Review

2026-03-09

AI Summary

This work presents a systematic review of deep learning–based end-to-end physical layer design for wireless communications, addressing the limitations of traditional block-wise optimization approaches that struggle with nonlinearities, hardware impairments, and complex network environments. Central to this framework is an autoencoder architecture that enables joint optimization of modulation, encoding/decoding, and channel estimation within a unified neural network. The approach demonstrates superior performance across canonical scenarios—including point-to-point, multiple-access, and interference channels—effectively bridging the gap between theoretical models and practical deployment constraints. By integrating signal processing and communication theory with data-driven learning, this paradigm offers a promising technical pathway and outlines key research directions for next-generation wireless systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The physical layer (PHY) in wireless communication systems has traditionally relied on model-based methods that are often optimized individually as independent blocks to perform tasks such as modulation, coding, and channel estimation. However, these approaches face challenges when it comes to capturing real-world nonlinearities, hardware imperfections, and increasing complexity in modern networks. This paper surveys advancements in applying deep learning (DL) for end-to-end PHY optimization by incorporating the autoencoder (AE) model as a powerful end-to-end DL framework to enable joint transmitter and receiver optimization and address challenges like dynamic channel conditions and scalability. We review cutting-edge DL models; their applications in PHY tasks such as modulation, error correction, and channel estimation; and their deployment in real-world scenarios, including point-to-point communication, multiple access, and interference channels. This work highlights the benefits of learning-based approaches over traditional methods, offering a comprehensive resource for researchers and engineers looking to innovate in next-generation wireless systems. Key insights and future directions are discussed to bridge the gap between theory and practical implementation.

Problem

• wireless communication
• physical layer
• nonlinearities
• hardware imperfections
• end-to-end optimization

Innovation

• end-to-end deep learning
• autoencoder
• physical layer optimization
• wireless communication
• joint transceiver design