AI and Machine Learning Approaches to Adaptive Signal Processing in Future Wireless Networks

The rapid expansion of wireless communication networks, driven by the increasing demand for high-speed connectivity and the exponential growth of IoT devices, presents significant challenges to traditional signal processing methods. As Beyond 5G (B5G) and 6G technologies continue to evolve, wireless networks must address issues related to spectrum congestion, dynamic channel conditions, and interference management while maintaining low latency and high energy efficiency. Traditional signal processing approaches struggle to adapt to these dynamic environments, necessitating AI-driven adaptive signal processing frameworks. This study investigates the integration of artificial intelligence (AI) and machine learning (ML) in adaptive signal processing, focusing on Blind Spot Awareness Sensing (BSS), Edge Learning (EL), and Radio Frequency (RF) signal reflection. By using unsupervised learning for blind spectrum sensing, federated learning for distributed optimization, and AI-driven RF reflection techniques for wireless sensing, it is demonstrated that AI models enhance detection precision, optimize spectrum utilization, and improve anti-interference performance.