Federated Learning-Based Cooperative Spectrum Sensing in Cognitive Radio
Zhibo Chen, Yiqun Xu, Hongbin Wang, Daoxing Guo
Abstract
Deep cooperative sensing is a cooperative spectrum sensing (CSS) algorithm based on a deep neural network (DNN). Since training DNN requires a large amount of sample data, what is worse existing algorithms directly send training data to the fusion center (FC), which greatly occupies the transmission channel. Motivated by this, in this letter, we introduce the federated learning framework to CSS and propose a federated learning-based spectrum sensing (FLSS) algorithm. In the federated learning framework, there is no need to gather data together. Each secondary user (SU) uses local data to train the neural network model and sends the gradient to FC to integrate the parameters. This framework can perform collaborative training while ensuring local data privacy and greatly reducing the traffic load between SU and FC. Besides, we adopt an efficient network model ShufflenetV2 to reduce the number of parameters and improve training efficiency. Simulation results demonstrate that the FLSS can achieve a detection probability of 98.78% with a false alarm probability of 1% at SNR = −15dB.