Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Analysis of Spectrum Sensing over Imperfect Channel conditions in Cognitive Vehicular Networks

Version 1 : Received: 25 December 2017 / Approved: 25 December 2017 / Online: 25 December 2017 (10:42:53 CET)

How to cite: Qian, X.; Hao, L.; Ni, D.; Tran, Q.T. Analysis of Spectrum Sensing over Imperfect Channel conditions in Cognitive Vehicular Networks. Preprints 2017, 2017120179. https://doi.org/10.20944/preprints201712.0179.v1 Qian, X.; Hao, L.; Ni, D.; Tran, Q.T. Analysis of Spectrum Sensing over Imperfect Channel conditions in Cognitive Vehicular Networks. Preprints 2017, 2017120179. https://doi.org/10.20944/preprints201712.0179.v1

Abstract

An explosive growth in vehicular wireless services and applications gives rise to spectrum resource starvation. Cognitive radio has been used to vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicles mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channels condition on spectrum sensing performance under temporally correlated Rayleigh sensing channel. For local and cooperative sensing, we derive some alternative expressions for average probability of miss detection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios.

Keywords

cognitive radio; cognitive vehicular networks; spectrum sensing; sensing/reporting channel; correlated rayleigh fading channel; hard fusion

Subject

Engineering, Electrical and Electronic Engineering

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