IEICE TRANSACTIONS on Fundamentals
Distributed Collaborative Spectrum Sensing Using 1-Bit Compressive Sensing in Cognitive Radio Networks
Summary :
In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing. However, the problem becomes quite challenging in wideband spectrum sensing due to high sampling pressure, limited power and computing resources, and serious channel fading. To overcome these challenges, this paper proposes a distributed collaborative spectrum sensing scheme based on 1-bit compressive sensing (CS). Each secondary user (SU) performs local 1-bit CS and obtains support estimate information from the signal reconstruction. To utilize joint sparsity and achieve spatial diversity, the support estimate information among the network is fused via the average consensus technique based on distributed computation and one-hop communications. Then the fused result on support estimate is used as priori information to guide the next local signal reconstruction, which is implemented via our proposed weighted binary iterative hard thresholding (BIHT) algorithm. The local signal reconstruction and the distributed fusion of support information are alternately carried out until reliable spectrum detection is achieved. Simulations testify the effectiveness of our proposed scheme in distributed CR networks.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.1 pp.382-388
- Publication Date
- 2020/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAL2076
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Shengnan YAN
Yanshan University,Hebei Key Laboratory of Information Transmission and Signal Processing
Mingxin LIU
Yanshan University,Hebei Key Laboratory of Information Transmission and Signal Processing
Jingjing SI
Yanshan University,Hebei Key Laboratory of Information Transmission and Signal Processing
Keyword
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Shengnan YAN, Mingxin LIU, Jingjing SI, "Distributed Collaborative Spectrum Sensing Using 1-Bit Compressive Sensing in Cognitive Radio Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 1, pp. 382-388, January 2020, doi: 10.1587/transfun.2019EAL2076.
Abstract: In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing. However, the problem becomes quite challenging in wideband spectrum sensing due to high sampling pressure, limited power and computing resources, and serious channel fading. To overcome these challenges, this paper proposes a distributed collaborative spectrum sensing scheme based on 1-bit compressive sensing (CS). Each secondary user (SU) performs local 1-bit CS and obtains support estimate information from the signal reconstruction. To utilize joint sparsity and achieve spatial diversity, the support estimate information among the network is fused via the average consensus technique based on distributed computation and one-hop communications. Then the fused result on support estimate is used as priori information to guide the next local signal reconstruction, which is implemented via our proposed weighted binary iterative hard thresholding (BIHT) algorithm. The local signal reconstruction and the distributed fusion of support information are alternately carried out until reliable spectrum detection is achieved. Simulations testify the effectiveness of our proposed scheme in distributed CR networks.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAL2076/_p
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@ARTICLE{e103-a_1_382,
author={Shengnan YAN, Mingxin LIU, Jingjing SI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Collaborative Spectrum Sensing Using 1-Bit Compressive Sensing in Cognitive Radio Networks},
year={2020},
volume={E103-A},
number={1},
pages={382-388},
abstract={In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing. However, the problem becomes quite challenging in wideband spectrum sensing due to high sampling pressure, limited power and computing resources, and serious channel fading. To overcome these challenges, this paper proposes a distributed collaborative spectrum sensing scheme based on 1-bit compressive sensing (CS). Each secondary user (SU) performs local 1-bit CS and obtains support estimate information from the signal reconstruction. To utilize joint sparsity and achieve spatial diversity, the support estimate information among the network is fused via the average consensus technique based on distributed computation and one-hop communications. Then the fused result on support estimate is used as priori information to guide the next local signal reconstruction, which is implemented via our proposed weighted binary iterative hard thresholding (BIHT) algorithm. The local signal reconstruction and the distributed fusion of support information are alternately carried out until reliable spectrum detection is achieved. Simulations testify the effectiveness of our proposed scheme in distributed CR networks.},
keywords={},
doi={10.1587/transfun.2019EAL2076},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - Distributed Collaborative Spectrum Sensing Using 1-Bit Compressive Sensing in Cognitive Radio Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 382
EP - 388
AU - Shengnan YAN
AU - Mingxin LIU
AU - Jingjing SI
PY - 2020
DO - 10.1587/transfun.2019EAL2076
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2020
AB - In cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing. However, the problem becomes quite challenging in wideband spectrum sensing due to high sampling pressure, limited power and computing resources, and serious channel fading. To overcome these challenges, this paper proposes a distributed collaborative spectrum sensing scheme based on 1-bit compressive sensing (CS). Each secondary user (SU) performs local 1-bit CS and obtains support estimate information from the signal reconstruction. To utilize joint sparsity and achieve spatial diversity, the support estimate information among the network is fused via the average consensus technique based on distributed computation and one-hop communications. Then the fused result on support estimate is used as priori information to guide the next local signal reconstruction, which is implemented via our proposed weighted binary iterative hard thresholding (BIHT) algorithm. The local signal reconstruction and the distributed fusion of support information are alternately carried out until reliable spectrum detection is achieved. Simulations testify the effectiveness of our proposed scheme in distributed CR networks.
ER -
English
