Abstract:

The channel state vector is sparse under the conditions of low spectral efficiency. Based on compressed sensing technology, a low complexity collaborative spectrum sensing method is proposed to reduce the redundancy of each user in the cognitive radio networks. The processing speed and the efficiency of the single cognitive node are improved through the sparse measurement matrix. The fusion center reconstructs the observed data by the iterative algorithm of factor graph, dramatically reducing the computation. According to the frequency usage, the cognitive nodes adjust the sampling rate adaptively to ensure the maximum efficiency in the whole networks.

Key words: cognitive radio networks, collaborative spectrum sensing, compressed sensing, factor graph