The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Filter Bank Spectrum Sensing for Cognitive Radios
Abstract
The primary task in any cognitive radio (CR) network is to dynamically explore the radio spectrum and reliably determine portion(s) of the frequency band that may be used for the communication link(s). Accordingly, each CR node in the network has to be equipped with a spectrum analyzer. In this paper, we propose filter banks as a tool for spectrum sensing in CR systems. Various choices of filter banks are suggested and their performance are evaluated theoretically and through numerical examples. Moreover, the proposed spectrum analyzer is contrasted with the Thomson's multitaper (MT) method - a method that in the recent literature has been recognized as the best choice for spectrum sensing in CR systems. A novel derivation of the MT method that facilitates our comparisons as well as reveals an important aspect of the MT method that has been less emphasized in the recent literature is also presented.
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Cites
Probability, Random Variables, and Stochastic Processes.
Abrahams, J., & Papoulis, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288754 · Cited by 16,226
Haykin, S. (1986) · Cited by 12,603
Welch, P. (1967) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1967.1161901 · Cited by 12,277
Cognitive radio: brain-empowered wireless communications
Haykin, S. (2005) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2004.839380 · Cited by 11,916
Rogers, S. (1996) · Control Engineering Practice · doi:10.1016/0967-0661(96)82838-3 · Cited by 9,278
Multirate Systems and Filter Banks
Vaidyanathan, P. P. (1992) · CaltechAUTHORS (California Institute of Technology) · Cited by 5,457
Probability, random variables and stochastic processes
Proakis, J. (1985) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1985.1164715 · Cited by 4,568
Spectrum estimation and harmonic analysis
Thomson, D. J. (1982) · Proceedings of the IEEE · doi:10.1109/proc.1982.12433 · Cited by 4,418
Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - I
Slepian, D., & Pollak, H. O. (1961) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1961.tb03976.x · Cited by 2,492 · cited 1×
“Filters designed in this way are known as prolate filters and their coefficients form sequences that are called discrete-time prolate spherical sequences or, in short, Slepian sequences [15]–[18].”
BackgroundProlate Spheroidal Wave Functions, Fourier Analysis, and Uncertainty-V: The Discrete Case
Slepian, D. (1978) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1978.tb02104.x · Cited by 1,741
Cited by
Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances
Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2183771 · Cited by 1,116
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Ali, A., & Hamouda, W. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2631080 · Cited by 624
A Survey on Machine-Learning Techniques in Cognitive Radios
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.100412.00017 · Cited by 580
Signal Processing in Cognitive Radio
Ma, J., Li, G. Y., & Juang, B. H. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2015707 · Cited by 533
Metadata
- Authors
- Farhang-Boroujeny, B.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2008-05
- Volume
- 56
- Issue
- 5
- Pages
- 1801-1811
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2110702117
- Cited by
- 398
- References
- 19
- FWCI
- 53.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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