Cooperative Spectrum Prediction-Driven Sensing for Energy Constrained Cognitive Radio Networks

Spectrum prediction based sensing schemes minimize the overall energy consumption of the sensing module in cognitive radio networks (CRNs) by predicting the status of spectrum before performing actual physical sensing. But, the performance of independent or local prediction models suffer from inaccuracies. Cooperative mode of spectrum prediction is found to be suitable to overcome the issues of local prediction models. In this work, we propose a cooperative spectrum prediction-driven sensing scheme for energy constrained cognitive radio networks to reduce the energy consumption while maintaining the spectral efficiency. The proposed scheme first employs a long short term memory network technique to perform local spectrum prediction, which identifies the status of a channel before actual sensing to improve energy efficiency. Thereafter, a parallel fusion based cooperative spectrum prediction model is applied to minimize the errors induced in local prediction model. Finally, the resultant cooperative prediction model is combined with a spectrum sensing framework to perform sensing operation when the cooperative spectrum prediction results to an indeterminate state in order to enhance the spectral efficiency. Simulation results show the efficacy of the proposed scheme in terms of spectral efficiency and energy efficiency compared to similar schemes from literature.

Automated summary

The proposed cooperative spectrum prediction-driven sensing scheme aims to reduce energy consumption while maintaining spectral efficiency in cognitive radio networks. It utilizes long short term memory network for local spectrum prediction and a parallel fusion cooperative spectrum prediction model to minimize errors. The combined model enhances spectral efficiency by performing sensing operation based on cooperative spectrum prediction results.