AI-Driven Signal Processing and Resource Allocation in Wireless Networks

Dear Colleagues,

This special issue highlights recent advances in applying artificial intelligence to signal processing and resource allocation in modern wireless networks. With the rapid evolution toward 6G and data-driven wireless environments, conventional communication and optimization techniques face growing limitations in scalability, adaptability, and efficiency. AI and machine learning have emerged as key enablers for addressing these challenges by providing intelligent, data-driven, and autonomous solutions.

The special issue presents innovative methodologies and system designs that leverage AI for enhanced channel estimation, interference management, beamforming, spectrum utilization, and resource scheduling. Contributions include learning-based physical layer techniques, reinforcement-learning-driven network control, energy-efficient and secure algorithmic strategies, and practical frameworks for real-time implementation. The topics of particular interest include, but are not limited to:

• AI-enhanced physical-layer signal processing
• Machine learning for channel estimation, prediction, and channel state feedback
• Intelligent beamforming, precoding, and MIMO optimization
• Deep learning-based modulation, detection, and decoding
• Reinforcement learning for wireless resource allocation and scheduling
• Federated, distributed, and edge learning architectures for wireless networks
• Energy-efficient and green AI solutions for wireless systems
• AI-driven interference management and power control
• Joint communication, sensing, and computation resource optimization
• Trustworthy, secure, and privacy-preserving AI for wireless networks
• Digital twins and data-driven simulation platforms for wireless communications
• AI-enabled mmWave, THz, RIS-assisted, and massive MIMO communications

I look forward to receiving your contributions.

Dr. Zuhaib Khan
Guest Editor

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