Dynamics Spectrum Sharing Environment Using Deep Learning Techniques

Abstract

The recent fast expansion of mobile communication services has resulted in a scarcity of spectrum resources. The challenge of multidimensional resource allocation in cognitive radio systems is addressed in this work. Complicated and dynamic Spectrum Sharing SS systems might be vulnerable to a variety of possible security and privacy vulnerabilities, necessitating protection techniques that are adaptable, dependable, and scalable. Methods based on machine learning (ML) have repeatedly been proposed to overcome these challenges. We present a complete assessment of the current progress of ML-based SS approaches, the most crucial security challenges, and the accompanying protection mechanisms in this paper. We develop cutting-edge methodologies for improving the performance of SS communication systems in a variety of critical areas, such as ML-based cognitive radio networks (CRNs), ML-based database assisted SS networks, ML-based LTE-U networks, ML-based ambient backscatter networks, and other ML-based SS solutions. The results of the simulation trials show that the suggested strategy may successfully boost the user's incentive while reducing collisions. In terms of reward, the suggested strategy beats opportunistic multichannel ALOHA by around 10% and 30%, respectively, for the single SU and multi-SU scenarios.