Conference Paper

Spectrum Sensing via Energy Detector in Low SNR

Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, AB, Canada
DOI: 10.1109/icc.2011.5963316 Conference: Communications (ICC), 2011 IEEE International Conference on
Source: IEEE Xplore


As required in the IEEE 802.22 proposal, spectrum sensing techniques should be capable enough to sense the primary signal with very low receiver sensitivity such as at -116 dBm. In this paper, the detection performance of an energy detector used for spectrum sensing in cognitive radio networks is investigated under such very low signal-to-noise ratio (SNR) levels. The analysis focuses on the derivation of a closed-form expression for the average missed-detection probability over Rayleigh fading and Nakagami-m fading channels. Subsequently, the detection threshold is optimized for minimizing the total error rate. The analysis is validated by numerical and simulation results. The sensing requirements defined in IEEE 802.22 are also discussed with numerical examples.

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Available from: Chinthananda Tellambura
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    • "In the condition of noise uncertainty or When noise fluctuates, this method is likely to fail to distinguish between the absence and the presence of the signal especially if its level is much lower than noise [5]. It gives a good performance at high SNR, but its performance is very poor at low SNR [6]-[10]. "
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    ABSTRACT: Despite its simplicity, drawback for the application of energy detection for spectrum sensing in cognitive radio is its poor performance in noise of uncertain power. In this paper we proposed a HOS based spectrum sensing method for cognitive radio network that give better performance than energy detection in noise of uncertain power. The proposed method based on distribution analysis as test statistic. The distribution of received signal traveling via wireless channel tends to have non gaussian distribution which will be different to the Gaussian noise. Sensing algorithm was tested using captured DTV signal. Result shows that our method performs well in the noise of uncertain power even at low SNR.
    Full-text · Conference Paper · May 2014
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    • "Related proofs are provided in the Appendix. Some preliminary results of this paper have been presented in [30]. "
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    ABSTRACT: For spectrum sensing in cognitive radio networks, the IEEE 802.22 standard requires the detection of primary signals with a signal-to-noise ratio (SNR) as low as -20 dB and receiver sensitivity as low as -116 dBm. Under such low-SNR levels, the performance of a conventional energy detector is analyzed in this paper. The analysis includes novel expressions for missed-detection probability and area under the receiver operating characteristic (ROC) curve. Thus, a unified framework covering fading channels, square-law diversity combining and cooperative spectrum-sensing scenarios is developed. The detection threshold is optimized to minimize the total error rate subject to bounded false alarm and missed-detection probabilities, which outperforms traditional detection threshold selection. Numerical results and Monte-Carlo simulation results with the IEEE 802.22 sensing requirements are provided and discussed.
    Full-text · Article · Jan 2014 · IEEE Transactions on Vehicular Technology
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    • "According to [13], it is not necessary for all secondary users to cooperate in the network to achieve the optimum performance, and secondary users with the highest primary user's signal-to-noise ratio (SNR) are participated in spectrum sensing. In [14] the detection performance for spectrum sensing in cognitive radio networks in low SNR is investigated. But both of these works have no analytical expressions for their problems. "
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    ABSTRACT: In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in cognitive sensor networks. We consider minimizing energy consumption and improving spectrum sensing performance simultaneously. For this purpose, we employ the energy detector for spectrum sensing and formulate the problem of sensor selection in order to achieve energy efficiency in spectrum sensing while reducing complexity. Due to the NP-complete nature of the problem, we simplify the problem to a more tractable form through mapping assignment indices from integer to the real domain. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing. In the next step, to save more energy, the decision node (DN) selection procedure is proposed to address the problem of direct transmissions to fusion center. Then, the problem of joint sensing node selection and DN selection is analyzed and an efficient solution is extracted based on the convex optimization framework. The novelty of the proposed work is to address the selection of the best sensing nodes while minimizing energy consumption. Simulation results show that significant energy is saved due to the proposed schemes in different scenarios.
    Full-text · Article · May 2013 · IEEE Sensors Journal
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