Cognitive radio based spectrum sharing: Evaluating channel availability via traffic pattern prediction

Li, Xiukui; Zekavat, S.A.

doi:10.1109/jcn.2009.6391385

Cited by 19 publications

(9 citation statements)

References 12 publications

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“…Given the estimated statistical model, spectrum occupancy prediction at future instances is achievable. Such models examine several statistical techniques with a major literature focus on Markov processes [10,45], Poisson processes [12,13], Bayesian models [9,14], neural networks [5,11,46], linear regression [15,16], space vector machine [47], pattern mining [48,49], and dictionary-based prediction [9]. In a sequential prediction framework, these techniques represent different parametrised predictor classes.…”

Section: Statistical Occupancy Modellingmentioning

confidence: 99%

“…Explicit duration semi-Markov chains with generalised distribution of duty cycle time modelled primary user's inter-arrival time in [85,86], while continuous time Markov chain modelled primary user behaviour in [87,88]. Moreover, hidden Markov model received wide attention in spectrum occupancy prediction literature [9,14,83,[89][90][91][92][93]. Liu et al addressed the prediction confidence, and the error of a continuous time Markov chain model with Erlang-2 distribution model for primary user's activity [94].…”

Section: Hidden Markov Model (Hmm) Is Partially Observablementioning

confidence: 99%

“…In this review, we address both implicit and explicit formulations as statistical SOP models. Statistical SOP models proposed for spectrum occupancy analysis include Poisson processes [12,13], Bayesian prediction [9,14], and linear regression [15,16]. Machine learning-based techniques have also been proposed for model learning including neural networks, time regression, and space vector machines [5,17,18].…”

Section: Introductionmentioning

confidence: 99%

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Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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Section: Statistical Occupancy Modellingmentioning

confidence: 99%

Section: Hidden Markov Model (Hmm) Is Partially Observablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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“…Xiukui Li and Seyed A. (Reza) Zekavat [5] have accepted that different channels which are licensed to PUs, from which SUs can choose any idle channel for correspondence. A method is recommended that empowers SUs to assess direct accessibility in cognitive radio systems when SUs exist together with PUs.…”

Section: Literature Surveymentioning

confidence: 99%

Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

Nathani¹,

Manna²

2017

Adv. sci. technol. eng. syst. j.

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The Cognitive Radio usage has been estimated as non-emergency service with low volume traffic. Present work proposes an infrastructure based Cognitive Radio network and probability of success of CR traffic in licensed band. The Cognitive Radio nodes will form cluster. The cluster nodes will communicate on Industrial, Scientific and Medical band using IPv6 over Low-Power Wireless Personal Area Network based protocol from sensor to Gateway Cluster Head. For Cognitive Radio-Media Access Control protocol for Gateway to Cognitive Radio-Base Station communication, it will use vacant channels of licensed band. Standalone secondary users of Cognitive Radio Network shall be considered as a Gateway with one user. The Gateway will handle multi-channel multi radio for communication with Base Station. Cognitive Radio Network operators shall define various traffic data accumulation counters at Base Station for storing signal strength, Carrier-to-Interference and Noise Ratio, etc. parameters and record channel occupied/vacant status. The researches has been done so far using hour as interval is too long for parameters like holding time expressed in minutes and hence channel vacant/occupied status time is only probabilistically calculated. In the present work, an infrastructure based architecture has been proposed which polls channel status each minute in contrary to hourly polling of data. The Gateways of the Cognitive Radio Network shall monitor status of each Primary User periodically inside its working range and shall inform to Cognitive Radio-Base Station for preparation of minutewise database. For simulation, the occupancy data for all primary user channels were pulled in one minute interval from a live mobile network. Hourly traffic data and minutewise holding times has been analyzed to optimize the parameters of Seasonal Auto Regressive Integrated Moving Average prediction model. The blocking probability of an incoming Cognitive Radio call has been assessed as a function of instantaneous occupancy and the blocksize of trunk servers.

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“…The authors focused on the effect of transmission radius of the nodes and dynamic channel assignment algorithm. Xiukui et al [6] demonstrated that the PU and SU can coexist in a network and the SU can directly access the channels allocated for the PU. The calls of the PU are closely monitored by the SU.…”

Section: Introductionmentioning

confidence: 99%

A New Channel Assignment Method in Cognitive Radio System

Gowda¹,

Vijayakumar²

2018

IJCT

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In this paper, a novel method to estimate the unsuccessful probability is proposed by combining the unnecessary probability and the blocking probability. The Unnecessary probability is the probability when the mobile node is unnecessarily handed over and the blocking probability is the one when the network is not able to assign the channel to the mobile now. A new design of new channel assignment method for cognitive radio system is presented. The first fit method and the random assignment methods are also implemented for the cognitive radio application as a baseline. The PU and SU call blocking are modeled for all the three methods. It is shown that advantages of first fit method and the random assignment methods are combined into the new method, namely, selective assignment method. Simulation are conducted for the various network parameters like number of channels, number of links, load on each link, percentage of the PU versus SU calls etc. It is shown that selective assignment method yields betterresults than that of the first fit method and the random assignment methods. Finally, the blocking probabilities are combined with the unnecessary handover probabilities of a three-node network to determine the unsuccessful handover probabilities of the first fit, random assignment and selective assignment methods.

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Cognitive radio based spectrum sharing: Evaluating channel availability via traffic pattern prediction

Cited by 19 publications

References 12 publications

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

A New Channel Assignment Method in Cognitive Radio System

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