基于变分贝叶斯推断的新型全局频谱协作感知算法

为了实现多维动态频谱接入，首先给出了主用户的全局功率谱近似模型，并构建了新型全局频谱协作感知算法的总体流程，以获得主用户网络中占用频段、功率及位置等全局信息。接着利用变分贝叶斯推断技术，设计了相应的模型系数向量估计器。仿真结果表明，该方法采用的近似模型具有较好的准确性，相应的系数向量估计算法具有较高的有效性和收敛稳定性，同时指明了信噪比和泄漏总虚假功率的关系以及两者对均方误差性能的影响。此外，还证明了该方法通过利用系数向量θ的稀疏性，而在均方误差性能上具有较大优势。

Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference

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 model coefficient vector was designed by utilizing the theory 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 aggregate 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 utilized.