Cooperative Spectrum Sensing Based on Riemann Distance of Subband Signals

Spectrum sensing is the core of cognitive radio (CR) technology. In this paper, we propose a cooperative spectrum sensing algorithm based on the Riemann distance of subband signals. By mapping the covariance matrices of receiving signal to the points on the manifold, the spectrum sensing problem is transformed into a geometric problem on the manifold. The difference between the Riemann distance and the Euclidean distance applied to spectrum sensing is analyzed theoretically. Considering that the signal has different characteristics at different frequencies, the DFT modulation filter bank is used to decompose the signal, and then the sub-signals are processed separately. Simulation results demonstrate the effectiveness of the proposed algorithm.