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Erschienen in:
Introduction to Compressed Sensing and Sparse Filtering Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

6. Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks

verfasst von : Hongjian Sun, Arumugam Nallanathan, Jing Jiang

Erschienen in: Compressed Sensing & Sparse Filtering

Verlag: Springer Berlin Heidelberg

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Abstract

Cognitive radio has become one of the most promising solutions for addressing the spectral under-utilization problem in wireless communication systems. As a key technology, spectrum sensing enables cognitive radios to find spectrum holes and improve spectral utilization efficiency. To exploit more spectral opportunities, wideband spectrum sensing approaches should be adopted to search multiple frequency bands at a time. However, wideband spectrum sensing systems are difficult to design, due to either high implementation complexity or high financial/energy costs. Sub-Nyquist sampling and compressed sensing play crucial roles in the efficient implementation of wideband spectrum sensing in cognitive radios. In this chapter, Sect. 6.1 presents the fundamentals of cognitive radios. A literature review of spectrum sensing algorithms is given in Sect. 6.2. Wideband spectrum sensing algorithms are then discussed in Sect. 6.3. Special attention is paid to the use of Sub-Nyquist sampling and compressed sensing techniques for realizing wideband spectrum sensing. Finally, Sect. 6.4 shows an adaptive compressed sensing approach for wideband spectrum sensing in cognitive radio networks.

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Vorheriges Kapitel Nonnegative Tensor Decomposition
Nächstes Kapitel Sparse Nonlinear MIMO Filtering and Identification
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Literatur
1.
McHenry MA (2005) NSF spectrum occupancy measurements project summary. Technical Report, Shared Spectrum Company
2.
Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220CrossRef
3.
Akyildiz I, Lee W-Y, Vuran M, Mohanty S (2008) A survey on spectrum management in cognitive radio networks. IEEE Commun Mag 46(4):40–48CrossRef
4.
Mitola J (2000) Cognitive radio: an integrated agent architecture for software defined radio. Ph.D. dissertation, Department of Teleinformatics, Royal Institute of Technology Stockholm, Sweden, 8 May 2000
5.
Akyildiz IF, Lee W-Y, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159CrossRefMATH
6.
Ekram H, Bhargava VK (2007) Cognitive wireless communications networks. In: Bhargava VK (ed) Springer Publication, New York
7.
Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radios. Proc Asilomar Conf Signal Syst Comput 1:772–776
8.
Sun H, Laurenson D, Wang C-X (2010) Computationally tractable model of energy detection performance over slow fading channels. IEEE Commun Lett 14(10):924–926CrossRef
9.
Hossain E, Niyato D, Han Z (2009) Dynamic spectrum access and management in cognitive radio networks. Cambridge University Press, Cambridge
10.
Tian Z, Giannakis GB (2006) A wavelet approach to wideband spectrum sensing for cognitive radios. In: Proceedings of IEEE cognitive radio oriented wireless networks and communications, Mykonos Island, pp 1–5
11.
Proakis JG(2001) Digital communications, 4th edn. McGraw-Hill, New York
12.
Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11(1):116–130CrossRef
13.
Tian Z, Giannakis GB (2007) Compressed sensing for wideband cognitive radios. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, Hawaii, April 2007, pp 1357–1360
14.
Yoon M-H, Shin Y, Ryu H-K, Woo J-M (2010) Ultra-wideband loop antenna. Electron Lett 46(18): 1249–1251
15.
Hao Z-C, Hong J-S (2011) Highly selective ultra wideband bandpass filters with quasi-elliptic function response. IET Microwaves Antennas Propag 5(9):1103–1108CrossRef
16.
17.
Sun H, Chiu W-Y, Jiang J, Nallanathan A, Poor HV (2012) Wideband spectrum sensing with sub-Nyquist sampling in cognitive radios. IEEE Trans Sig Process 60(11):6068–6073
18.
Venkataramani R, Bresler Y (2000) Perfect reconstruction formulas and bounds on aliasing error in sub-Nyquist nonuniform sampling of multiband signals. IEEE Trans Inf Theory 46(6):2173–2183MathSciNetCrossRefMATH
19.
20.
Mishali M, Eldar YC (2009) Blind multiband signal reconstruction: compressed sensing for analog signals. IEEE Trans Signal Process 57(3):993–1009MathSciNetCrossRef
21.
Feldster A, Shapira Y, Horowitz M, Rosenthal A, Zach S, Singer L (2009) Optical under-sampling and reconstruction of several bandwidth-limited signals. J Lightwave Technol 27(8):1027–1033CrossRef
22.
Rosenthal A, Linden A, Horowitz M (2008) Multi-rate asynchronous sampling of sparse multi-band signals, arXiv.org:0807.1222
23.
Fleyer M, Rosenthal A, Linden A, Horowitz M (2008) Multirate synchronous sampling of sparse multiband signals, arXiv.org:0806.0579
24.
25.
Polo YL, Wang Y, Pandharipande A, Leus G (2009) Compressive wide-band spectrum sensing. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, Taipei, pp 2337–2340
26.
Tropp JA, Laska JN, Duarte MF, Romberg JK, Baraniuk R (2010) Beyond Nyquist: efficient sampling of sparse bandlimited signals. IEEE Trans Inf Theory 56(1):520–544MathSciNetCrossRef
27.
Mishali M, Eldar Y (2010) From theory to practice: sub-Nyquist sampling of sparse wideband analog signals. IEEE J Sel Top Signal Process 4(2):375–391
28.
29.
Laska J, Kirolos S, Massoud Y, Baraniuk R, Gilbert A, Iwen M, Strauss M (2006) Random sampling for analog-to-information conversion of wideband signals. In: Proceedings of IEEE DCAS, pp 119–122
30.
Laska JN, Kirolos S, Duarte MF, Ragheb TS, Baraniuk RG, Massoud Y (2007) Theory and implementation of an analog-to-information converter using random demodulation. Proc IEEE Int Symp Circ Syst ISCAS 2007(27–30):1959–1962
31.
32.
Haupt J, Bajwa WU, Rabbat M, Nowak R (2008) Compressed sensing for networked data. IEEE Signal Process Mag 25(2):92–101CrossRef
33.
Quan Z, Cui S, Sayed AH, Poor HV (2009) Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Trans Signal Process 57(3):1128–1140MathSciNetCrossRef
34.
Ward R (2009) Compressed sensing with cross validation. IEEE Trans Inf Theory 55(12):5773–5782CrossRef
35.
Boufounos P, Duarte M, Baraniuk R (2007) Sparse signal reconstruction from noisy compressive measurements using cross validation. In: Proceedings of IEEE/SP 14th workshop on statistical signal processing, Madison, pp 299–303
36.
Chaudhari S, Koivunen V, Poor HV (2009) Autocorrelation-based decentralized sequential detection of OFDM signals in cognitive radios. IEEE Trans Signal Process 57(7):2690–2700MathSciNetCrossRef
37.
Malioutov D, Sanghavi S, Willsky A (2010) Sequential compressed sensing. IEEE J Sel Top Signal Process 4(2):435–444CrossRef
38.
39.
Hazewinkel M (ed) (1987) Encyclopaedia of mathematics vol 1. Springer, New York
Metadaten
Titel
Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks
verfasst von
Hongjian Sun
Arumugam Nallanathan
Jing Jiang
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Compressed Sensing & Sparse Filtering

Print ISBN: 978-3-642-38397-7

Electronic ISBN: 978-3-642-38398-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-38398-4_6

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