Research On Wideband Compressive Spctrum Sensing Based On The Detection Of Sparsity

In recent years, with the growing of wireless spectrum resources, cognitive radio (CR) technology takes more and more people’s attention. As one of the most basic and most critical technology of cognitive radio, spectrum sensing has become a hot research point for researchers. In order to meet the needs of the development of the broadband wireless mobile communications, CR must continue to explore new methods to increase the range of wideband spectrum sensing.Due to the limitations of the Nyquist sampling rate, how to acquire wideband analog signal is the biggest challenge faced by a wideband spectrum sensing. Using signal sparsity or compressibility, Compressive sensing merges the sampling and compression process into one process. It collects only key information which is useful to recover the original signal, so that it can sample signal under the Nyquist rate, and then use some reconstruction algorithm to reconstruct the original signal. Compressive sensing can not only largely cut down the complexity of the broadband spectrum detection equipment, especially for ADC, but also offers a probability to wideband spectrum detection. In recent years, more and more researchers introduce the compressed sensing into wideband spectrum sensing, and has also made some achievements.This paper focus on some of the key technologies in wideband compressive spectrum sensing, which includes the acquisition and recovery of wideband signal, signal sparsity detection, cooperative spectrum detection method based on local multi-level judgment and so on.First, we study how to sample and reconstruct wideband analog signal. On signal acquisition, we mainly interested in the analog information converter (Analog-to-Information Converter, AIC), and on reconstruction algorithm, we represented some reconstruction algorithms, classify them and made some simulation. We also compared some key parameters, such as reconstruction time and reconstruction error, to identify the strengths and weaknesses of various algorithms, and then selected an appropriate method for our system.Secondly, in order to ensure that the compressed sensing technology can be accurately applied to wideband spectrum sensing, we proposed a new method for signal sparsity detection on account of the suppose that signal over wideband is always sparse or compressible. Then we conduct a feasibility analysis and simulation to the mentioned method. Simulation results show that the method can detect the sparsity of wideband signal, so that it can ensure that compressive sensing can accurately be applied to wideband spectrum sensing system.Finally, on account of the lower detection accuracy of hard decision in low SNR condition and the wide bandwidth of the common control channel needed for SUs to upload data of soft decision, we proposes a cooperative spectrum sensing method based on multi-level judgment which combines the hard decision with soft decision to improve the performance of wideband spectrum sensing system. Simulation results show that this algorithm presented better performance in the detection probability, and channel collision probability of PUs and SUs can be effectively avoided.