Adaptive Compressive Sensing And Tracking Of Dynamic Sparse Spectrum

The sparisty of the received signal is time-varying in many wireless communication applications, such as wideband wireless spectrum sensing, communication reconnaissance and so on. It is proved that reconstruction error is monotonically reduced according to the increasing of compressive ratio. A novel spectrum sensing algorithm based on compressive sampling is proposed, which can adjust the compressive rate according to the sparsity of the spectrum adaptively. This algorithm is composed of two parts: compressive sampling and compressive rate adaptivity. Firstly, the sparse spectrum is reconstructed and the error between the reconstructed spectrum and real spectrum is estimated. Secondly, the compressive rate of the next time slot is adjusted in the help of the approximate linear relationship between the error and the compressive rate. The simulation results illustrate that the new algorithm can sense and track the slow time-varying sparse wideband validly, and it can reduce the compressive sampling rate remarkably.