Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference - Ming WU; Tie-cheng SONG; Jing HU; Lian-feng SHEN (Tongxin xuebao)
To realize multi-dimensional dynamic spectrum access, an approximate model was proposed for the global power spectral density (PSD)of primary users (PU). Based on the proposed model, a novel cooperative spectrum sensing algorithm was proposed, and its overall flow was also built to obtain global information in the network of PU. The global information included locations, occupied frequency bands and transmitting powers of the PU. Then, an estimator of mod-el coefficient vector was designed by utilizing the th of variational Bayesian inference (VBI). Simulation results show that the proposed approximate model has good accuracy, and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. Meanwhile, the relationship between SNR and the leakage of ag-gregate spurious power (LASP)was pointed out, and the influence of SNR and LASP on MSE performance was also discussed. Furthermore, it is proved that the proposed algorithm has better MSE performance than another algorithm since the sparsity of model coefficient vector is util zed.