A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing [PDF]
Sensors, 2023 Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR).
Adam Olesiński, Zbigniew Piotrowski
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Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms [PDF]
Sensors, 2023 Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches.
Gilles Burel +3 more
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A Policy for Optimizing Sub-Band Selection Sequences in Wideband Spectrum Sensing [PDF]
Sensors, 2019 With the development of wireless communication technology, cognitive radio needs to solve the spectrum sensing problem of wideband wireless signals. Due to performance limitation of electronic components, it is difficult to complete spectrum sensing of ...
Yangyi Chen, Shaojing Su, Junyu Wei
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Efficient Wideband Spectrum Sensing with Maximal Spectral Efficiency for LEO Mobile Satellite Systems [PDF]
Sensors, 2017 The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary ...
Feilong Li +4 more
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A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing [PDF]
Sensors, 2018 The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and ...
Zhuhua Hu +4 more
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Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform [PDF]
The Scientific World Journal, 2014 Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention.
Yulin Wang, Gengxin Zhang
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A Robust Method Based on Deep Learning for Compressive Spectrum Sensing [PDF]
SensorsIn cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). However, traditional reconstruction algorithms exhibit suboptimal performance, and conventional WSS methods fail to fully capture the ...
Haoye Zeng +3 more
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Bayesian Compressive Sensing Wideband Spectrum Detection Based on Energy Efficiency [PDF]
Jisuanji gongcheng, 2016 In Cognitive Radio Network(CRN),wideband spectrum detection based on Compressive Sensing(CS) only focuses on spectral efficiency.Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the ...
WANG Zan,XU Xiaorong,YAO Yingbiao
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Distributed Wideband Spectrum Sensing
CoRR, 2015 6 pages, 6 figures, submitted to ieee Globecom ...
Thomas Kealy +2 more
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Cooperative wideband spectrum sensing with multi-bit hard decision in cognitive radio [PDF]
, 2013 Cognitive radio offers an increasingly attractive solution to overcome the underutilization problem. A sensor network based cooperative wideband spectrum sensing is proposed in this paper. The purpose of the sensor network is to determine the frequencies
Ammar, M. +4 more
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