Automatic Clustering Collaborative Compressed Spectrum Sensing in Wide-Band Heterogeneous Cognitive Radio Networks

Abstract

There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.