Sub-Nyquist Wideband Spectrum Sensing Based on Analog to Information Converter for Cognitive Radio

Mashhour, Marwa; Hussein, Aziza I.; Mogahed, Hussein Sh.

doi:10.1016/j.procs.2021.02.018

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development of analog information conversion technology based on compressed sensing has broadened the way of solving the problem of broadband signal acquisition [8,9]. Existing simulation information conversion systems with relatively mature development include nonuniform sampling (NUS) [10][11][12], random demodulator (RD) [13][14][15][16][17] system, modulated wideband converter (MWC) [18][19][20], multicoset sampling [21], Nyquist folding receiver (NYFR) [22], and finite rate of innovation (FRI) [23]. However, the research on the compressed sampling technology has focused on the compressed sampling and reconstruction technology of the signal from the beginning, lacking the research on estimating the signal parameters directly using the compressed sampling information without reconstructing the original signal.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

Meng

Wang

et al. 2023

Journal of Sensors

View full text Add to dashboard Cite

The compressed sampling theory provides a good idea for sampling broadband linear frequency-modulated (LFM) signal, but the signal reconstruction after sampling should be considered when using the compressed sampling system to sample the signal. For the problem of estimating the parameters of the LFM signal under the condition of nonreconstruction, a method combining random demodulation (RD) and waveform matching (WM) dictionary is proposed in this paper. Firstly, the RD system is used to collect the compressed sampling data of LFM signal. In order to ensure the validity of the compressed sampling data, the value of the compressed sampling rate R in the RD system is discussed. Then, the feasibility of using compressed sampling data to estimate LFM signal parameters is theoretically demonstrated. According to the construction principle of waveform matching dictionary, the corresponding relationship between sparse representation coefficients, dictionary atoms, and signal parameters can be determined. Using this theory, the LFM signal parameters can be estimated. Finally, simulation and real signal experiments verify that the proposed method can realize the estimation of initial frequency and chirp rate of LFM signal when some prior information is known.

show abstract

Section: Introductionmentioning

confidence: 99%

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

Meng

Wang

et al. 2023

Journal of Sensors

View full text Add to dashboard Cite

show abstract

Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff

Arshid

Zhang

Hussain

et al. 2022

Egyptian Informatics Journal

View full text Add to dashboard Cite

Research on LFM signal parameter estimation method based on Gabor transform to improve MWC system

Meng¹,

Meng²,

Wang³

2023

AIP Advances

View full text Add to dashboard Cite

The “compressed sensing” theory is the foundation for the compressed sampling system’s design. In addition to the sparse representation and observation matrix, more studies in compressed sensing theory focus on signal reconstruction and recovery. Only a small number of research studies estimate the original signal parameter information using the compressed sample data. In this research, we propose a linear frequency modulation (LFM) signal parameter estimation approach based on the Gabor transform for the enhanced Modulated Wideband Converter (MWC) system, which can directly estimate the parameters of an LFM signal utilizing compressed sampling data. Based on the MWC system prototype, a better MWC system based on Gabor transform is created by fusing the Gabor transform with compressed sensing theory, and the system’s structure, function, parameter settings, and viability are all examined. Finally, we show through a simulation experiment that the proposed method is capable of accurately estimating the parameter information of a huge broadband LFM signal by merely restoring Gabor coefficients.

show abstract

Sub-Nyquist Wideband Spectrum Sensing Based on Analog to Information Converter for Cognitive Radio

Cited by 5 publications

References 25 publications

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff

Research on LFM signal parameter estimation method based on Gabor transform to improve MWC system

Contact Info

Product

Resources

About