Link Availability Prediction for Cognitive Radio Ad Hoc Networks

Hou, Ling; Yeung, Kwan L.; Wong, Kin-Yeung

doi:10.4236/jcc.2014.26003

Cited by 3 publications

(2 citation statements)

References 22 publications

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Section: Spectrum Inference In the Geographic Spatial Domainmentioning

confidence: 99%

“…In [203], the SVM was invoked both for predicting the handoff point and the idle channels with a high precision. In addition to the spectrum availability, the link availability between the CRs was also inferred by [204], where by invoking this novel approach, a reliable path can be found for dynamic routing in CRNs. In [205], a location predictor is proposed, where the historical changes of the PU's geographic locations are represented by a directed graph having weighted edges.…”

Section: Spectrum Inference For Mobilitymentioning

confidence: 99%

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Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

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Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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Section: Spectrum Inference In the Geographic Spatial Domainmentioning

confidence: 99%

Section: Spectrum Inference For Mobilitymentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

View full text Add to dashboard Cite

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Enhancement of DSR and AODV Protocols Using Link Availability Prediction

Malwe

Taneja

Biswas

2017

Wireless Pers Commun

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A Bayesian model for mobility prediction in wireless sensor networks

Somaa

Adjih

Korbi

et al. 2016

2016 International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

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Abstract-Wireless sensor networks (WSN) have specific features such as low transmission range, stringent energy consumption constraints, limited memory and processing power. For this reason, tailored protocols have been proposed, optimizing network protocols with respect to various assumptions: one commonly exploited property of WSN is the stability of the topology due to permanent installation of sensor nodes. However, in some applications, some of the wireless sensor nodes might be mobile, for instance when they are associated with users. In that case, specific extensions of WSN protocols need to be designed. Then a first step is the characterization of nodes' mobility. This is the focus of this article: we propose a general method of mobility estimation for wireless sensor networks. Namely, using a Bayesian framework, we derive a mobility prediction model to estimate the node velocity (starting from no knowledge) from observed events. In this article, we focus on events represented by the most minimal information: mere observation of link duration with neighbors. Simulations illustrate that, even with such limited information, the mobility can be well inferred, and results show the good performance of the method.

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Link Availability Prediction for Cognitive Radio Ad Hoc Networks

Cited by 3 publications

References 22 publications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Enhancement of DSR and AODV Protocols Using Link Availability Prediction

A Bayesian model for mobility prediction in wireless sensor networks

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