AI-Driven Signal Processing in Communications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 89
Special Issue Editors
Interests: network information theory; information theoretic security; privacy protection; signal processing for communications
Interests: data compression; signal quantization; channel coding; semantic communications; deep learning; AI-empowered communications
Interests: ultra-reliable and low-latency communications; wireless extended reality; cross-layer optimization; physical layer security
Special Issue Information
Dear Colleagues,
The relentless evolution of communication systems, spurred by the demands of 5G, Beyond 5G, and the emerging 6G era, necessitates a paradigm shift in how we approach signal processing. Artificial intelligence (AI), encompassing machine learning, deep learning, and reinforcement learning, has emerged as a transformative force, offering powerful tools to overcome traditional limitations and unlock unprecedented performance in communication signal processing. From the physical layer intricacies to network-wide optimizations, AI is enabling intelligent adaptation, robust operation in complex environments, and the efficient handling of massive data streams inherent in modern wireless and mobile communications.
This Special Issue in Electronics aims to be a premier platform for disseminating cutting-edge research on the theoretical foundations, novel algorithms, practical implementations, and future challenges of AI-driven signal processing in communications. We seek contributions that fundamentally advance how signals are generated, transmitted, received, and interpreted using AI techniques. We are particularly interested in work that addresses the unique signal processing challenges posed by dynamic channel conditions, massive connectivity, ultra-low latency requirements, extreme bandwidths, and the need for energy-efficient solutions. Topics of interest include, but are not limited to, the following:
- AI-driven signal processing for wireless and wireline communications;
- Machine/deep learning for physical layer design in Beyond 5G and 6G access and core networks;
- AI-based channel estimation, prediction, and equalization;
- Intelligent signal detection and interference management/mitigation;
- AI-powered resource allocation and network optimization;
- Machine learning for waveform design, modulation, and coding;
- AI applications in massive MIMO, smart antennas, and beamforming;
- Signal processing for AI-enabled Internet of Everything (IoE) and IoT networks;
- AI-driven signal processing in autonomous driving, V2X solutions, and vehicular networks;
- Energy-efficient and low-latency AI signal processing for edge computing in communications;
- AI for semantic communications and holographic communication signal processing;
- Security and privacy in AI-driven communication signal processing (e.g., adversarial attacks and defenses);
- Big data analytics and AI for communication signal intelligence;
- AI-driven integrated sensing and communication (ISAC);
- AI-driven designs for reconfigurable intelligent surfaces (RIS) and movable/fluid antennas.
Dr. Tao Guo
Dr. Huihui Wu
Dr. Xiaoyu Zhao
Dr. Mingjie Shao
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI) in communications
- machine learning for signal processing
- deep learning for wireless communications
- intelligent signal processing
- 5G/6G signal processing
- AI-based channel estimation
- AI for physical layer communications
- reinforcement learning in communications
- semantic communications signal processing
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