Rethinking Autocorrelation for Deep Spectrum Sensing in Cognitive Radio Networks
Keunhong Chae, Jungin Park, Yusung Kim
Abstract
We design a novel learning-based spectrum sensing model. Under the insight that an autocorrelation curve yields richer information than a single sum of received signal powers for detecting the presence of a primary user, we propose a convolutional neural network-based deep learning model, called deep spectrum sensing (DSS), that receives an autocorrelation curve as input. Extensive simulation results show that our DSS model has a higher performance than existing deep-learning-based models that use raw signals or spectrograms as an input. Furthermore, DSS can be trained with much smaller amounts of data than the existing models, and is a lighter model compared with the existing models. Finally, we evaluate the effectiveness of the DSS implementation over a real testbed consisting of universal software radio peripheral and GNU radio packages. The experimental results are consistent with the simulation performance.