分布式的1bit压缩频谱感知算法

doi:10.3969/j.issn.1000-0801.2014.09.015

Abstract

Abstract:

Since the actual sparsity of spectrum is unknown and time-varying, information transmit frequently between nodes in the distributed spectrum sensing network consumes communication bandwidth. To relieve the network communication bandwidth pressure, a spectrum algorithm based on 1 bit compressed sensing and distributed model was proposed. The sensing data of the nodes was compressive sampled and quantified in 1 bit, and then the data was fused in fusion node, through the mode of JSM-2. Finally the spectrum was reconstructed by the BIHT algorithm to implement spectrum sensing. Simulation results show that the proposed method has better spectrum detection performance with a few samples in low SNR, so it is a practical method of spectrum sensing.

Key words: compressed sensing, spectrum sensing, 1 bit quantization

Zhijin Zhao,Weikang Hu,Junwei Hu. 1 bit Compressive Spectrum Sensing Algoritbm Based on Distributed Model[J]. Telecommunications Science, 2014, 30(9): 106-110.

Figures/Tables 3

References 13

1	Mitola J . Cognitive radio: an integrated agent architecture for software defined radio. Stockholm: Sweden Royal Institute of Technology（RTH）, 2000
2	Donoho D L . Compressed sensing. IEEE Transactions on Information Theory, 2006,52(4): 1289～1306.
3	Cand E S E J , Romberg J , Tao T . Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006,52(2): 489～509
4	Boufounos P T , Baraniuk R G . 1 bit compressive sensing. Proceedings of the 42nd Annual Conference on Information Sciences and Systems（CISS）, Princeton, NJ, USA 2008: 16～21
5	Jacques L , Laska J N , Boufounos P T , et al. Robust 1 bit compressive sensing via binary stable embeddings of sparse vectors. IEEE Transactions on Information Theory, 2011,59(4): 2082～2102
6	Yan M , Yang Y , Osher S . Robust 1 bit compressive sensing using adaptive outlier pursuit. IEEE Transactions on Signal Processing, 2012,60(7): 3868～3875
	Baraniuk R G . Compressive sensing. IEEE Signal Processing Magazine, 2007,24(4): 118～121
8	Goyal V K , Kova V C , Evi C J , et al. Quantized frame expansions with erasures. Applied and Computational Harmonic Analysis, 2001,10(3): 203～233
9	Plan Y , Vershynin R . One bit compressed sensing by linear programming. Communications on Pure and Applied Mathematics, 2013,36(4): 1～18
10	Laska J N , Baraniuk R G . Regime change: bit-depth versus measurement-rate in compressive sensing. IEEE Transactions on Signal Processing, 2012,60(7): 3496～3505
11	Polo Y L , Wang Y , Pandharipande A , et al. Compressive wide-band spectrum sensing. Proceedings of IEEE International Conference on Speech and Signal Processing, Taipei, Taiwan, China, 2009: 2337～2340
12	Baron D , Duarte M F , Wakin M B , et al. Distributed compressive sensing. ArXiv Preprint ArXiv:0901.3403, 2009
13	Tropp J A , Gilbert A C , Strauss M J . Simultaneous sparse approximation via greedy pursuit. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, 2005: 721～724

Metrics

Recommended 0

No Suggested Reading articles found!

[1]	Yuanmeng FAN, Shunlan LIU. Spectrum sensing algorithm based on stochastic resonance and non-central F-distribution [J]. Telecommunications Science, 2023, 39(1): 42-50.
[2]	Chaojie CHEN, Wei XIE, Linwei HE, Qiang TIAN, Runping HE, Zhefei WANG. Anti-collision online early warning method for construction machinery around transmission line [J]. Telecommunications Science, 2022, 38(5): 95-103.
[3]	Xuan YU, Xuan GENG. A reduced-complexity compressed sensing channel estimation for underwater acoustic channel [J]. Telecommunications Science, 2021, 37(3): 114-124.
[4]	Zhongjie JIA, Ming JIN, Xiaoqun SONG. A multi-band spectrum sensing method based on sticky hidden Markov model [J]. Telecommunications Science, 2021, 37(1): 48-57.
[5]	Bowei WANG,Jin TAN. Compressed sensing subspace pursuit algorithm based on two stagewise weak selection [J]. Telecommunications Science, 2020, 36(5): 83-92.
[6]	Wuji ZHANG,Shunlan LIU,Jianrong BAO. Opportunistic cooperative spectrum sensing based on eigenvalue ratio of random matrix [J]. Telecommunications Science, 2020, 36(2): 61-67.
[7]	Shunlan LIU, Yide XIAO, Jianrong BAO. Dual threshold cooperative spectrum sensing algorithm based on stochastic resonance [J]. Telecommunications Science, 2020, 36(12): 33-40.
[8]	Shunlan LIU,Jing WANG,Jianrong BAO. Covariance matrix opportunistic cooperative spectrum sensing of high detection probability [J]. Telecommunications Science, 2019, 35(1): 67-73.
[9]	Ying ZHOU,Lihua YANG,Longxiang YANG,Meng NI. Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing [J]. Telecommunications Science, 2019, 35(1): 74-80.
[10]	Jie XIAO,Yuebin CHEN,Chutian CHEN,Ting ZHENG,Jiwu QIAN. Variable threshold energy detection algorithm based on trust degree [J]. Telecommunications Science, 2018, 34(8): 129-135.
[11]	Zhiqiang BAO,Yanrui CUI,Xiao XU. A two level multi-antenna blind spectrum detection about spatial spectrum [J]. Telecommunications Science, 2018, 34(3): 59-66.
[12]	Xuemei YANG,Xi HE,Jiapin XU. Spectrum sensing algorithm based on the eigenvalue of Wishart random matrix [J]. Telecommunications Science, 2017, 33(9): 69-75.
[13]	Kai LUO,Junwei HUANG,Pengguang ZHOU,Hao XU,Can WU. A low-complexity detection algorithm for quadrature spatial modulation systems [J]. Telecommunications Science, 2017, 33(5): 75-81.
[14]	Zhiyong HE. Modified absolute value cumulating spectrum sensing algorithm in cognitive radio [J]. Telecommunications Science, 2017, 33(2): 98-103.
[15]	Guangyue LU,Hang SU. Survey on SSDF attack and defense for distributed cooperative cognitive radio [J]. Telecommunications Science, 2017, 33(1): 95-105.

1 bit Compressive Spectrum Sensing Algoritbm Based on Distributed Model

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 13

Related Articles 15

Metrics

Recommended 0