Efficient wideband spectrum sensing using random projection

Subspace based spectrum estimation is a powerful technique for wideband spectrum sensing. Unlike narrowband sensing, wideband sensing requires no prior information about the band-structure or bandwidth of the primary users of the spectrum, thus making it an attractive alternative to narrowband spectrum sensing. Typically, subspace based techniques require eigen-decomposition of the sample covariance matrix, which is computationally very expensive. As the expected number of primary users in the system increases, the size of the covariance matrix increases, thus increasing the spectrum sensing time, resulting in the reduction of overall throughput. In this paper, an efficient approach to perform subspace based spectrum sensing via random projection is proposed. In the proposed approach, spectral decomposition of a significantly lower order matrix is required for wideband spectrum sensing. The time complexity of the proposed approach is shown to be much better than conventional subspace based techniques. In addition to improved time complexity, the regularization imposed via the low rank approximation, improves the spectrum sensing performance of the proposed approach over the conventional subspace based approach, especially with limited observations. Simulations results are provided to demonstrate the effectiveness of the proposed approach.