Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2012-11-23
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Date
2012
Major/Subject
Mcode
Degree programme
Language
en
Pages
203
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 135/2012
Abstract
Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC.
Description
Supervising professor
Koivunen, Visa, Academy Prof.
Thesis advisor
Koivunen, Visa, Academy Prof.
Keywords
autocorrelation based detectors, censoring, cooperative detection, imperfect reporting channels, quantization, sequential tests
Other note
Parts
  • [Publication 1]: S. Chaudhari, J. Lundén, and V. Koivunen. Collaborative Autocorrelation-Based Spectrum Sensing of OFDM Signals in Cognitive Radios. In Proc. of the 42nd Annual Conference on Information Sciences and Systems (CISS), Princeton, USA, pp. 191-196, Mar. 19-21, 2008.
  • [Publication 2]: S. Chaudhari, V. Koivunen, and H. Poor. Distributed Autocorrelation-Based Sequential Detection of OFDM Signals in Cognitive Radios. In Proc. of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), Singapore, pp. 1-6, May 19-21, 2008.
  • [Publication 3]: S. Chaudhari, V. Koivunen, and H. Poor. Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios. IEEE Transactions on Signal Processing, vol. 57, pp. 2690-2700, Jul. 2009.
  • [Publication 4]: S. Chaudhari and V. Koivunen. Effect of Quantization and Channel Errors on Collaborative Spectrum Sensing. In Proc. of the 43rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, pp. 528-533, Nov. 1-4, 2009.
  • [Publication 5]: S. Chaudhari, J. Lundén, and V. Koivunen. BEP Walls for Collaborative Spectrum Sensing. In Proc. of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, pp. 2984-2987, May 22-27, 2011.
  • [Publication 6]: S. Chaudhari, J. Lundén, and V. Koivunen. Effects of Quantization on BEP Walls for Soft Decision Based Cooperative Sensing. In Proc. of the 12th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), San Francisco, USA, pp. 101-105, June 26-29 2011.
  • [Publication 7]: S. Chaudhari, J. Lundén, and V. Koivunen. Performance Limitations for Cooperative Spectrum Sensing with Reporting Channel Errors. In Proc. of the 22nd IEEE Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Toronto, Canada, pp. 337-342, Sep. 11-14, 2011.
  • [Publication 8]: S. Chaudhari, J. Lundén, V. Koivunen, and H. Poor. Cooperative Sensing with Imperfect Reporting Channels: Hard Decisions or Soft Decisions?. IEEE Transactions on Signal Processing, vol. 60, pp. 18-28, Jan. 2012.
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