Cyclic Feature based Wideband Spectrum Sensing using Compressive Sampling

To enable dynamic spectrum access in cognitive radio networks, fast and accurate spectrum sensing has to be performed over very wide bandwidth in noisy channel environments under energy constraints. The cyclic feature based sensing approach is robust to noise uncertainty, but requires very high sampling rates in the wideband regime and hence incurs high energy consumption and hardware costs. This paper aims to alleviate the sampling requirements of cyclic detectors by utilizing the compressive sampling principle and exploiting the unique sparsity structure in the two-dimensional cyclic spectrum domain. The main challenge lies in the fact that the compressive samples collected in the time domain does not have a direct linear relationship with the two-dimensional cyclic spectrum of interest, which is a major departure from existing sparse signal recovery techniques for linear sampling systems. This paper solves this challenge by reformulating the vectorized cyclic spectrum into a linear form of the autocorrelation of the compressed samples. Further, based on the recovered cyclic spectrum, new cyclic feature based detectors are developed to simultaneously identify the spectrum occupancy of multiple active sources over the entire wide band. Simulation shows that the proposed spectrum sensing scheme can substantially alleviate the sampling rate requirements with little performance loss, and is robust to low SNR conditions and unpredictable noise uncertainty in wireless networks.