Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Cognitive Radio (CR) aims to optimize the utilization of limited and scarce radio spectrum by detecting the presence or absence of primary users through spectrum sensing. However, CR users face significant challenges, including the impact of deep fading effects, which extend the sensing time needed to detect primary users effectively. To address these challenges, we propose a Spectrum Prediction-Channel Allocation (SP-CA) algorithm, which consists of three distinct phases. The first phase involves the use of clustering mechanisms to select a spectrum coordinator, which helps manage the spectrum allocation process more efficiently. In the second phase, an Eigenvalue-based detection method is employed to improve the sensing accuracy of secondary users by better distinguishing between primary and secondary users. In the third phase, a Bayesian inference approach is used to mitigate performance degradation for secondary users by adapting the detection strategy based on observed conditions. The Eigenvalue-based detection method is compared with the Energy detection method, showing superior performance in terms of varying false alarm rates and sample sizes. Simulation results demonstrate that the SP-CA algorithm outperforms existing methods by reducing the sensing time and enhancing the overall accuracy of spectrum sensing. This approach provides a more efficient and reliable solution for spectrum management in CR networks.

 Keywords: Cognitive radio, spectrum sensing, spectrum prediction, Eigenvalue-based detection, clustering algorithms.

.