Cooperative Wideband Spectrum Sensing Based on Sub-Nyquist Sparse Fast Fourier Transform

López-Parrado, Alexander; Velasco-Medina, Jaime

doi:10.1109/tcsii.2015.2483278

Cited by 30 publications

(6 citation statements)

References 18 publications

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“…∞ represents the square of the maximum value of each error in the frequency domain. Compared with l 2 /l 2 , l ∞ /l 2 has a limitation on each amplitude in the frequency domain, so that each amplitude in the frequency domain output by the algorithm can be well estimated [12]- [16].…”

Section: ) Error Constraint Principlementioning

confidence: 99%

“…In order to avoid two or more large coefficients falling in the same basket, it is required that the large coefficients be distributed uniformly and randomly. For general signals, it is difficult to meet this condition, so the fourier spectrum needs to be randomly permuted to separate the large coefficients from each other [8], [12]- [15], [25].…”

Section: ) Frequency Coefficients Sub-basketsmentioning

confidence: 99%

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Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

Zhang

Xie

et al. 2022

IEEE Access

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The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes. In order to solve the above problems, the shockwave signal acquisition technology based on Sparse Fourier Transform is proposed in this paper. The specific method is to use the sparse characteristics of the frequency domain of the transient shockwave signal to collect the frequency domain by downsampling, reduce the amount of acquired data. The simulation results of the above experiments are compared with the simulation results of Compressed Sensing low sampling observation. The experimental results show that the time-domain acquisition accuracy error of the proposed framework is less than 10 −4 when the Nyquist sampling rate is less than 8 times, and the mean relative error in time domain is only 0.01%. And compared with the acquisition framework based on Compressed Sensing theory, the reconstructed mean absolute error of the proposed method is reduced by 88.66% in the case of the same downsampling multiple. Moreover, this method does not need to store the high-dimensional random observation matrix similar to Compressed Sensing, which overcomes the defect of hardware implementation difficulty.

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Section: ) Error Constraint Principlementioning

confidence: 99%

Section: ) Frequency Coefficients Sub-basketsmentioning

confidence: 99%

Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

Zhang

Xie

et al. 2022

IEEE Access

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“…Hence, in recent years, new techniques are developed for sampling signals with special features. Some of these techniques, which are known as sub-Nyquist methods, include multi-rate sampling [1], modulated wideband converter [2], and multi-coset sampling [3].…”

Section: Introductionmentioning

confidence: 99%

Cooperative wideband spectrum sensing in cognitive radio based on sparse real‐valued fast Fourier transform

Khayyeri

Mohammadi

2020

IET Communications

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“…Moreover, an algorithm for broadband spectrum sensing based on sFFT is presented [19], which uses two sampling channels: Its implementation was carried out by using the software-defined radio (SDR) platform USRP [15], with a bandwidth up to 900 MHz. In [20], an algorithm to perform cooperative wideband spectrum sensing (and its hardware implementation) is presented, based on MC sampling: It is robust to noise, overcoming the performance of the algorithm presented in [19]. On the other hand, another doctoral thesis is presented by the Georgia Institute of Technology [19], which describes an asynchronous multirate sampling system, similar to the MC sampling: Its hardware implementation can achieve a bandwidth up to 18 GHz.…”

Section: Introductionmentioning

confidence: 99%

Testbed for Sub-Nyquist Wideband Spectrum Monitoring

Lee

López-Parrado

Tasamá

et al. 2019

rev.ing.univ.Medellin

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Radioelectric spectrum management is a concern for today’s world, mainly due to the misuse that has been given to this resource through the years, especially on the UHF band. To address this problem, a testbed for sub-Nyquist Wideband Spectrum Monitoring was built, that includes a web interface to remotely measure occupancy of the UHF band. To achieve the above, an RF interface that allows tuning UHF frequencies with an instantaneous bandwidth of 95 MHz was built. Afterwards, a Random Demodulator was connected, and then an embedded system performed sub--Nyquist sampling and spectrum recovery. The embedded system connected to an information system that serves a web page, through which remote users can perform UHF band monitoring. Experimental results showed that spectrum sensing can be achieved by using different algorithms on certain sparse spectra. In addition, the aforementioned web interface allowed simultaneous user connections, in order to perform independent measurements by sharing a hardware subsystem.

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Cooperative Wideband Spectrum Sensing Based on Sub-Nyquist Sparse Fast Fourier Transform

Cited by 30 publications

References 18 publications

Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

Cooperative wideband spectrum sensing in cognitive radio based on sparse real‐valued fast Fourier transform

Testbed for Sub-Nyquist Wideband Spectrum Monitoring

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