Recent Advances in AI-Enabled Wireless Communications and Networks

Dear Colleagues,

Artificial intelligence (AI)-enabled wireless communication and networks refer to the integration of AI technologies into wireless communication systems to enhance their performance, efficiency, and capabilities. This integration is a rapidly evolving field with the potential to revolutionize how we interact with classic communication principals and how networks are designed and operated. In recent years, research works have provided promising results in applying AI to different use cases, ranging from physical to application layers, from edge to core network services and applications. However, although the generalized large model shows artificial intelligence technology has rapidly developed in recent years, it still contradicts with future communication systems in terms of green, real-time performance guarantees and other requirements. The aim of this Special Issue is to publish recent research achievements in this field, thereby promoting the harmonious integration of AI technologies into future wireless communications systems. Topics of interest include (but are not limited to) the following:

• The machine-learning-driven design and optimization of modulation and coding schemes;
• Machine learning techniques for channel estimation, channel modeling, and channel prediction;
• Machine-learning-driven techniques for radio environment awareness;
• Machine learning frameworks for joint communication and control; 
• Machine learning for positioning and location-based services;
• Machine learning in complex network setups; 
• Deep Learning for Wireless Communications;
• Deep Unfolding for Wireless Communications and Networks;
• Deep learning for Semantic and Goal-Oriented Communications;
• Deep learning for Physical-Layer Secure Communications;
• Deep Learning for integrated communications and sensing;
• Deep learning for transceiver design and channel decoding;
• End-to-end learning-based wireless communication systems;
• Transfer Learning for Wireless Communications and Networks;
• Distributed Learning for Wireless Communications;
• Distributed Optimization and Resource Allocation for Wireless Communications;
• The compression of neural networks for low-complexity hardware implementation;
• Wireless transmission and protocol optimization for machine learning; 
• Machine learning techniques for physical-layer security;
• Generative and large-language-model-based approaches to communications.

Dr. Zhuo Sun
Dr. Fabrizio Granelli
Prof. Dr. Wenbin Guo
Guest Editors

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• machine learning
• deep learning
• wireless networks
• wireless communications
• AI-enabled wireless communications and networks