Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks
Deepak Upadhyay, Abhay B. Upadhyay, Nookala Venu, Kamlesh Kukreti · 2025
We conduct an extensive study on deep learningbased spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for $6 \mathbf{G}$ networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow’s communications.