AI-Powered RF Spectrum Management for Next-Generation Wireless Networks
Keywords:
RF Spectrum Management, AI, Machine Learning, Deep Learning, Reinforcement Learning, Spectrum Allocation, Interference Mitigation, Cognitive Radio Networks, Next-Generation Wireless Networks, 5G, Spectrum Efficiency.Abstract
The rapid growth of wireless technology has led to a sharp rise in demand for radio frequency (RF) spectrum which is essential for current wireless networks. With more people requiring fast and dependable wireless service, the older methods for managing radio frequencies are no longer meeting the demand. The paper examines the role AI can play in handling radio frequency spectrum for advanced wireless networks like 5G and the next generation. Leveraging AI approaches like machine learning (ML), deep learning (DL) and reinforcement learning (RL), we propose algorithms for efficiently using the radio spectrum, eliminating interference and checking the spectrum in real-time. Our strategy focuses on using the spectrum efficiently, making the network function well and giving everyone an even chance with available resources. This section looks at AI helping CRNs, as artificial agents decide on and use the most suitable frequency bands to decrease interference and boost communication efficiency in the network. The simulation outcomes prove that the proposed AI-based approach for managing the spectrum results in improved efficiency and better performance of the network.
