Research On Broadband Spectrum Sensing Based On Modulated Broadband Converter

With the advent of big data,5G and the Internet of Things era,the demand for data is increasing rapidly,and the transmission of data depends on limited spectrum resources.The realization of spectrum sharing is inseparable from the observation and sensing of the spectrum.The Nyquist sampling rate is the minimum requirement of traditional spectrum acquisition.In the high frequency 5G era,the high sampling rate is a challenge to hardware and resource utilization.Compressed sensing is an undersampling method that solves the problem of high sampling rates.At present,there are two main methods for implementing undersampling in hardware:one is to modulate a wideband converter;the other is to multiply sample.The modulation wideband converter is better than multi-set sampling in application scenarios and device requirements.Therefore,this paper uses modulation broadband converter to perform wideband spectrum sensing.The following research is carried out:Firstly,the modulation wideband converter needs pseudo-random sequence to perform spectrum shifting on the signal.In this paper,we study the sparse random sequence,binary random sequence,cyclic sequence and Gaussian sequence,and the difficulty and the ability of different sequences to be generated in hardware.The advantages and disadvantages are compared.Because the four sequences correspond to the four corresponding observation matrices,the reconstruction performance of the four observation matrices is analyzed and compared by simulation experiments.In the application,a suitable matrix can be selected according to the actual situation.Secondly,combining with the characteristics of real-life perception,this paper explores how to better reconstruct the signal.This paper proposes an improved self-applying matching pursuit reconstruction(Improve Adaptive)in understanding the Orthogonal Matching Pursuit(OMP)algorithm.The Orthogonal Matching Pursuit(IAOMP)algorithm aborts the iteration by setting two different thresholds.Through experimental verification,the IAOMP algorithm proposed in this paper is equivalent to the OMP algorithm,but it excludes the dependence on sparsity and is superior to other greedy algorithms that require sparsity.Compared with the AOMP algorithm,a limit threshold is added.The time overhead is small,and the reconstruction error is small under the same conditions.Finally,in order to solve the problem that the modulation wideband converter(MWC)needs to adjust the number of channels of signal processing according to the number of users when it is aware of multiple users,this paper designs a system that can automatically increase the number of channels based on feedback mechanism-adaptive modulation.Broadband Converter System(AMWC);At the same time,considering the practical application of sub-band number,sparsity and bandwidth in the application,the signal sparsity adaptive orthogonal matching pursuit algorithm(AOMP)is introduced to realize the adaptive sensing of spectrum.It can be seen from simulation experiments that AMWC combined with AOMP algorithm can not only achieve accurate spectrum recovery,but also adjust the channel number according to the number of signal sub-bands,which is superior to fixed channel number system in perceptual performance and computational complexity.