DQN Based Distributed Cooperative Spectrum Sensing for Multiband Multiuser CRN

While 5G networks are still being deployed and optimized worldwide, research and development efforts are underway for 6G technology. Massive machine-type communication of 5G has tremendously improved voice and data communication quality. However, it cannot meet future technology requirements in 2030 and beyond. To meet the future demands of users, 6G will provide a high data rate with high reliability and ultra-low latency by accommodating the large number of users in the network. However, the spectrum scarcity problem is one of the key restrictions in achieving future users' needs. Cognitive radio technology helps the 6G network to address the shortage of frequency bands for allocation of channels on dynamic basis. This paper gives detailed review cooperative multi-band spectrum sensing for centralized and distributed systems. It demonstrates that distributed cooperative multi-band sensing dominates over other centralized sensing techniques due to the non-requirement of fusion centers and would be more appropriate in applications like the Internet of Vehicles, unmanned aerial vehicles, Vehicular ad-hoc networks and Machine learning and Reinforcement learning-based cooperative multi-band spectrum sensing techniques determine spectrum holes with very high accuracy.. In proposed work, Multiband Multiuser CRN environment in created in python and performance of distributed cooperative spectrum sensing is determined using deep Q Networks.