Abstract:
The growing shortage of radio frequency spectrum, driven by the explosive increase in the number of wireless devices and data traffic volumes, makes Cognitive Radio (CR) technologies critically important for the future of telecommunications. This research addresses the challenge of dynamic spectrum management by developing a simulation model of a cognitive radio communication system based on the LTE network architecture. In contrast to existing solutions, the proposed model features a modular structure, allowing for the flexible integration and evaluation of various frequency resource occupancy prediction algorithms. The model is implemented in the MatLab environment and comprises three key modules: an LTE signal generation and processing module, which creates Radio Environment Maps (REM); a predictive neural network model training module; and a frequency resource occupancy prediction module. Particular focus is placed on utilizing the advanced Kolmogorov-Arnold Network (KAN) architecture for predicting unused Scheduling Blocks (SBs) within an LTE frame. Simulation results, encompassing 10,000 frames, demonstrated the high efficiency of the proposed approach. The KAN model achieved a prediction accuracy of 92.23% for identifying free frequency resources in a 10 ms frame. Comparative testing revealed that the KAN architecture outperforms the traditional LSTM network in accuracy by approximately 10% given an equal number of trainable parameters, and also converges faster during training. The practical significance of this work lies in providing a tool for accurate spectrum occupancy assessment and secondary user access planning, leading to a significant increase in the spectral efficiency and reliability of next-generation wireless networks.
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