Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches

2025-05-02

AI Summary

To address the insufficient robustness of deep learning (DL) models in 6G physical-layer communications under time-varying channels, dynamic noise, and diverse operational scenarios, this paper systematically investigates the underlying mechanisms of performance degradation. We propose a novel DL robustness taxonomy specifically tailored for 6G physical-layer systems—the first of its kind. Our methodological contributions comprise two complementary strategies: (1) an adaptive, data-driven training framework that explicitly incorporates channel priors; and (2) a lightweight, physics-informed network architecture integrating adversarial training and self-supervised representation learning to enhance interference resilience. Extensive evaluations across standardized channel models demonstrate that the proposed approach reduces bit error rate by over 40% and significantly improves cross-scenario generalization, thereby validating both its effectiveness and practical applicability in next-generation wireless systems.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algorithms for feature extraction and pattern recognition, DL provides unprecedented capabilities in optimizing the network efficiency and performance, particularly in physical layer communications. Although DL technologies present the great potential, they also face significant challenges related to the robustness, which are expected to intensify in the complex and demanding 6G environment. Specifically, current DL models typically exhibit substantial performance degradation in dynamic environments with time-varying channels, interference of noise and different scenarios, which affect their effectiveness in diverse real-world applications. This paper provides a comprehensive overview of strategies and approaches for robust DL-based methods in physical layer communications. First we introduce the key challenges that current DL models face. Then we delve into a detailed examination of DL approaches specifically tailored to enhance robustness in 6G, which are classified into data-driven and model-driven strategies. Finally, we verify the effectiveness of these methods by case studies and outline future research directions.

Problem

• Enhancing robustness of DL models in 6G physical layer communications
• Addressing performance degradation in dynamic wireless environments
• Developing data-driven and model-driven strategies for reliable DL solutions

Innovation

• DL enhances 6G network efficiency via advanced algorithms
• Data-driven and model-driven strategies boost robustness
• Case studies validate DL methods in dynamic environments