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Spectrum sensing using distributed sequential detection via noisy reporting MAC

Abstract : This paper considers cooperative spectrum sensing algorithms for Cognitive Radios which focus on reducing the number of samples to make a reliable detection. We propose algorithms based on decentralized sequential hypothesis testing in which the Cognitive Radios sequentially collect the observations, make local decisions and send them to the fusion center for further processing to make a final decision on spectrum usage. The reporting channel between the Cognitive Radios and the fusion center is assumed more realistically as a Multiple Access Channel (MAC) with receiver noise. Furthermore the communication for reporting is limited, thereby reducing the communication cost. We start with an algorithm where the fusion center uses an SPRT-like (Sequential Probability Ratio Test) procedure and theoretically analyse its performance. Asymptotically, its performance is close to the optimal centralized test without fusion center noise. We further modify this algorithm to improve its performance at practical operating points. Later we generalize these algorithms to handle uncertainties in SNR and fading.
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Contributor : Jithin Sreedharan Connect in order to contact the contributor
Submitted on : Friday, December 12, 2014 - 12:25:38 AM
Last modification on : Thursday, January 20, 2022 - 4:17:38 PM

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Jithin K. Sreedharan, Vinod Sharma. Spectrum sensing using distributed sequential detection via noisy reporting MAC. Signal Processing, Elsevier, 2015, 106, pp.159-173. ⟨10.1016/j.sigpro.2014.07.009⟩. ⟨hal-01094268⟩



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