Abstract
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.
Data availability
The data generated and analyzed in this study are real and were produced by the authors. The datasets used to support the findings of this research are available from the corresponding author upon reasonable request.
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M.Z. (Zhang Min) conceived the overall research direction, provided critical supervision and guidance for the project, and revised the manuscript for important intellectual content; Z.W. (Wu Ziru, corresponding author) designed the specific research methodology, led the experimental implementation, analyzed the core data, wrote the main body of the manuscript, and coordinated the revision process; J.B. (Bai Jinyuan) performed supplementary experiments, processed data sets and prepared all figures and tables, and assisted with the drafting of the introduction section; B.Z. (Zhang Bo) conducted the literature review and assisted with experimental validation. All authors reviewed and approved the final version of the manuscript.
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Min, Z., Ziru, W., Jinyuan, B. et al. Traffic pattern-adaptive channel allocation in cognitive radio networks via multi-scale windowing. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41417-2
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DOI: https://doi.org/10.1038/s41598-026-41417-2
