AI-Driven Communication Techniques for Advanced Wireless Networks: From MIMO to Semantic Communication and Beyond
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 201
Editor
Interests: artificial intelligence; data analysis and feature extraction; intelligent communication and other new generation wireless communication; network and communication security
Special Issue Information
Dear Colleagues,
This collection centers on AI-driven communication techniques that enhance signal processing, transmission reliability, and semantic fidelity across advanced wireless networks. It explores the evolution from traditional MIMO systems to intelligent, task-oriented communication paradigms.
The scope includes deep learning-based signal enhancement (e.g., speech denoising), intelligent encoding and decoding, multimodal data transmission, semantic communication for task-aware networks, and AI-optimized physical layer designs for applications like digital human synthesis and immersive multimedia.
The issue aims to demonstrate how AI techniques—particularly deep learning—can unify signal processing and semantic understanding to improve both transmission efficiency and application-level fidelity. By integrating denoising, feature extraction, and neural rendering within a communication framework, it shows that intelligent preprocessing and end-to-end optimization are essential for next-generation wireless services requiring high reliability and perceptual quality.
This collection supplements existing literature by bridging physical layer optimization and application-aware semantic communication. While prior works often treat signal processing and content generation separately, this issue highlights their interdependence: cleaner signal transmission enables higher-fidelity reconstruction at the receiver (e.g., lip-sync accuracy in digital humans). It extends beyond conventional MIMO and channel coding by incorporating AI modules that preserve semantic meaning, addressing real-world degradation, and enabling task-oriented communication for immersive applications.
Dr. Lin Zhang
Guest Editor
Manuscript Submission Information
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Keywords
- AI-driven communication
- semantic communication
- deep learning
- wireless networks
- MIMO
- signal enhancement
- multimodal transmission
- task-oriented communication
- neural rendering
- speech denoising
- physical layer optimization
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