Cyclic Feature Based Wideband Spectrum Sensing Using Compressive Sampling

Dynamic spectrum access has emerged as a promising paradigm for improving the Dynamic spectrum utilization efficiency of wireless networks. To enable this new paradigm, fast and accurate spectrum sensing has to be performed over very wide bandwidth in noisy channel environments under energy constraints. Cyclic feature based sensing approach works well under 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 sparsity structure in the two-dimensional cyclic spectrum domain. A technical 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 sparse 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-based detectors are developed to estimate the spectrum occupancy when multiple sources are present. Simulation shows that the proposed spectrum sensing algorithms can substantially reduce sampling rate with little performance loss, and is robust to the unpredictable noise uncertainty in wireless networks.