Research On Sparse Wideband Spectrum Sensing Methods In Cognitive Radio

Wideband spectrum sensing is the key technology in cognitive radio,which is required to keep sensing the surrounding spectrum environment and detecting the occupancy of the entire frequency band to assist the dynamic allocation of spectrum,thereby alleviating the problem of shortage of spectrum resources and low spectrum utilization.The sensing of wideband signals brings great challenges to Analog to Digital Converters（ADC）working under the Nyquist sampling theorem,signals with wide bandwidth need to be sampled by high-speed ADCs with high power consumption and high price.Making full use of the sparsity of the signal is critical to solve the problem of wideband signal sampling.Based on the compressed sensing theory or Sparse Fast Fourier Transform（SFFT）algorithm,sub-Nyquist sampling technology can break through the bottleneck of Nyquist sampling theorem and realize spectrum sensing of sparse wideband signals.Many sub-Nyquist based spectrum sensing schemes use a multichannel sampling structure.Employing multiple sampling channels means that the process of signal reconstruction is inevitably affected by the timing skew between channels.To solve this problem,based on multi-coset sampling,this thesis proposed a timing skew calibration method and a corresponding sampling structure.The main research contents of this thesis are as follows:1.In order to realize sub-Nyquist spectrum sensing,the SFFT algorithm based on spectrum rearrangement and phase rotation are investigated,and applied to wideband spectrum sensing.In this thesis,we use multiple sampling channels to replace multiple rounds of loops in the SFFT algorithm to reduce the running time of the algorithm while sampling at the sub-Nyquist rate.2.Sparse wideband spectrum sensing methods based on compressed sensing theory having been investigated.Multi-coset sampling,multi-rate sampling and their corresponding sensing models have been analyzed.The spectrum reconstruction methods applicable to the above two sampling structures have been discussed.Simulations of wideband spectrum sensing algorithms based on multi-coset sampling and multi-rate sampling have been carried out.3.In order to solve the problem of timing skew in the multi-channel sampling methods,this thesis has proposed a timing skew calibration method and the corresponding sampling structure.Based on the multi-coset sampling structure,the signal sampling model considering the existence of channel timing skew is constructed.The influence of timing skew on the multi-coset sampling process is analyzed.The proposed scheme is composed of two sets of multi-coset samples to obtain the quadrature and the in-phase signals at the sub-Nyquist sampling rate.Based on the spectral relationship between the two sets of samples,the unknown timing skew is estimated and used to calibrate the sensing model to achieve better spectrum reconstruction performance.Simulation experiments show that the proposed scheme can reconstruct the signal spectrum well even in the presence of significant channel timing skew.Robust spectrum sensing can be accomplished at sub-Nyquist rates.