Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.
This paper investigates the performance of an underlay cognitive relay system where secondary users（SUs） suffer from a primary outage probability constraint and spectrum-sharing interference imposed by a primary user（PU）. In particular, we consider a secondary multi-relay network operating in the selection decode-and-forward（SDF） mode and propose a best-relay selection criterion which takes into account the spectrum-sharing constraint and interference. Based on these assumptions, the closed-form expression of the outage probability of secondary transmissions is derived. We find that a floor of the outage probability occurs in high signal-to-noise ratio（SNR） regions due to the joint effect of the constraint and the interference from the PU. In addition, we propose a generalized definition of the diversity gain for such systems and show that a full diversity order is achieved. Simulation results verify our theoretical solutions.
