Research On Wideband Spectrum Sensing Algorithm Based On Multichannel Sub-nyquist Sampling

As an intelligent spectrum sharing technology, cognitive radio (CR)can effectively solve the problem of spectrum sparsity by searching and using the free frequency bands in the licensed spectrum. With the rapid development of wireless communication services, the demand for spectrum resources is increasing significantly. In order to detect and use more blank-spectrum, wideband spectrum sensing has become a hot research topic. The difficulty of wideband spectrum sensing is the need of high speed analog-to-digital converter (ADC) for wideband signal sampling. In recent years, wideband spectrum sensing approaches based on sub-Nyquist sampling had gotten much attention, because it can reduce the sampling rate below the Nyquist rate. However, most of the sub-Nyquist sampling-based wideband sensing approaches need to use highly nonlinear methods for realizing wideband spectral reconstruction,resulting in high computational complexity. In this work, we mainly discuss the problem of high computational complexity of wideband sensing approaches based on sub-Nyquist sampling.The research work is sponsored by Chinese National Nature Science Foundation named "Research on the broadband spectrum sensing and control of the polarization region". The main contents and achievements of the work are summarized as follows:(1) The wideband spectrum sensing techniques are reviewed. Firstly,the spectrum sensing model is introduced. Then, the research statuses of narrowband and wideband spectrum sensing techniques are summarized,respectively. Finally, sub-Nyquist sampling-based wideband spectrum sensing techniques are classified and analyzed in depth. In addition,wideband spectrum sensing algorithms based on multichannel sampling are particularly introduced, which is one of the main categories of the sub-Nyquist sampling-based wideband spectrum sensing techniques. And the reviewing provides theoretical basis of the following research.(2) In order to solve the problem of the high computational complexity of reconstructing the wideband using highly nonlinear algorithms, this paper proposes a novel wideband spectrum sensing algorithm based on two-coset coprime sampling that can reconstruct the wideband power spectrum using a linear algorithm. This algorithm first estimates the power spectrum of the wideband signal by employing the coprime sampling scheme, then performs detection via a subband-bin energy detector. The cornerstone of this approach is that it can generate a Nyquist space sampled autocorrelation from the sub-Nyquist samples by implementing the coprime sampling scheme. Demonstrated by the simulations, the proposed algorithm has a better performance under high compression ratio and a better robustness against noise compared with the wideband sensing algorithm based on orthogonal matching pursuit. Also it is illustrated that this algorithm has the advantage over computational complexity by analyzing.(3) Because highly non-linear reconstruction methods are used to reconstruct the whole wideband spectrum, which require high computational complexity; This work proposes a two-step wideband sensing algorithm based on multicoset sampling, which only needs to reconstruct part of the wideband spectrum. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step,as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed.Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in the fine sensing step.(4) In order to solve the problem of the high computational complexity of recovering the signal or power spectrum, this work proposes a novel multicoset sampling-based wideband sensing algorithm with no recovery of spectral, where the location of occupied subband is identified via a maximum inner product method, thus reducing computational complexity significantly. Compared with the existing spectral recovery algorithms, the proposed algorithm maintains an excellent sensing performance with several orders of magnitude lower computational complexity.(5) In order to solve the problem of the high computational complexity of spectral recovery, this work proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e.,orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.