Support Vector Machine Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) technology is a promising solution to meet the spectrum requirements of UAVs. Cooperative spectrum sensing (CSS) is considered as an effective paradigm to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high communication overhead, resulting in a significant performance degradation. Therefore, we propose a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance the cooperative performance and efficiency of the PU detection. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machine (SVM) in dynamic selection enables the selection of the optimal voting rule based on diverse sensing parameters. This dynamic selection process follows the characteristic of the different PU scenarios to ensure the performance and efficiency. Finally, simulation results demonstrate that the superiority of the proposed DS1 and SVM dynamic selection with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.