基于因子图的分布式变分稀疏贝叶斯压缩感知简

提出了一种基于因子图的分布式变分稀疏贝叶斯压缩感知算法。该算法利用因子图和变分方法将全局感知问题分解为简单的局部问题，通过认知用户邻居间的置信传播实现“软融合”，使每个认知用户能够获得全局最优估计。且充分利用邻居间传递的信息所具有的时间和空间二维相关性，提高认知用户在低信噪比下的感知性能。同时，算法在迭代过程中自适应地删除不收敛的超参数及对应的基函数，降低通信负载。实验结果表明：该方法在低采样率和低信噪比下有较好的感知性能。

Distributed variational sparse Bayesian compressedsensing based on factor graphs

A distributed variational sparse Bayesian compressed spectrum sensing algorithm based on factor graph was proposed,which decomposed the global spectrum sensing problem into local problem based on factor and variation.Belief propagation was used for the statistical inference of the spectrum occupancy,to implement the “soft fusion”.The temporal and spatial correlation information providing two-dimensional redundancies was exchanged among cooperative cognitive users to improve the detection performance under low SNR.Meanwhile,the algorithm prunes the divergence of hyper-parameters and the corresponding basis functions for reducing the load of communication.The simulation results show that this method can effectively achieve performance of spectrum sensing under a low sampling rate and the low SNR.