Traffic pattern-adaptive channel allocation in cognitive radio networks via multi-scale windowing.

A major challenge in enhancing the performance of multi-user cognitive radio networks lies in accurately characterizing the dynamic service arrivals of secondary users (SUs) for optimal spectrum utilization. To enhance protocol adaptability in complex traffic environments, this paper proposes a Traffic Pattern-Adaptive Allocation (TPA) protocol. Integrating Markov modeling with queueing theory, TPA employs multi-scale windows to concurrently measure SU traffic arrivals. By incorporating an adaptive window weight adjustment mechanism, the protocol achieves a granular characterization of the SU arrival process. Furthermore, it constructs a Probability Allocation Vector to dynamically map traffic states to channel allocation strategies, enabling automatic adjustment of resource policies in response to traffic fluctuations. Experimental results demonstrate that, compared to the Maximum Throughput Allocation protocol, TPA delivers higher throughput and lower packet rejection rates under complex traffic dynamics. This approach thus offers a robust solution for addressing the stochastic nature of user demands in next-generation cognitive radio systems.