IEEE 802.22: The first cognitive radio wireless regional area network standard

Stevenson, Chris; Chouinard, Gérald; Lei, Zhongding; Hu, Wendong; Shellhammer, Stephen J.; Caldwell, Winston

doi:10.1109/mcom.2009.4752688

Cited by 914 publications

(547 citation statements)

References 2 publications

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“…For numerical evaluations, the parameters are set according to Table I. The values of SNR and sampling frequency are adopted from [5], and P min d and P max f a are chosen according to IEEE 802.22 standard [23]. In simulation evaluations, the average throughput has been computed after simulating the scenario for 100 time slots.…”

Section: Numerical Resultsmentioning

confidence: 99%

Analytical and learning‐based spectrum sensing time optimisation in cognitive radio systems

2013

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PostprintThis is the accepted version of a paper published in IET Communications. This paper has been peerreviewed but does not include the final publisher proof-corrections or journal pagination.Citation for the original published paper (version of record):Analytical and learning-based spectrum sensing time optimisation in cognitive radio systems. AbstractIn this paper, the average throughput maximization of a secondary user by optimizing its spectrum sensing time is formulated, assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. The energy consumed to find a transmission opportunity is evaluated, and a discussion on the impacts of the number of the primary users on the secondary user throughput and consumed energy are presented.In order to avoid the challenges associated with the analytical method, as a second solution, a systematic adaptive neural network-based sensing time optimization approach is also proposed. The proposed scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance, and cooperation of the artificial neural networks used in the proposed method are explained in detail, and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimization scheme. Index TermsCognitive radio, spectrum handover, artificial neural networks, maximum secondary user's throughput.

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Section: Numerical Resultsmentioning

confidence: 99%

Analytical and learning‐based spectrum sensing time optimisation in cognitive radio systems

2013

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“…As an example, the IEEE 802.11af [40] and the IEEE 802.22 [41] standards take advantage of the TV white spaces to support wireless local area networks (WLANs) and wireless regional area networks (WRANs), respectively. Although opportunistic spectrum access for CR Networks (CRNs) has been extensively studied [42][43][44][45], the results may not be directly applied to VANETs due to the high mobility of vehicles.…”

Section: Opportunistic Spectrum Utilization For Vanetsmentioning

confidence: 99%

Opportunistic Spectrum Utilization in Vehicular Communication Networks

Cheng¹,

Shen²

2016

SpringerBriefs in Electrical and Computer Engineering

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“…The channel switching simulator has been set up to operate under various physical layer parameters that are based on the IEEE 802.22 wireless regional area network standard (WRAN) for CR [14]. The standard specifies a number of adaptive modulation Θ and coding rate r modes.…”

Section: Physical Layer Considerationsmentioning

confidence: 99%

Prediction based channel allocation performance for cognitive radio

Barnes

Maharaj

2014

AEU - International Journal of Electronics and Communications

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The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.

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IEEE 802.22: The first cognitive radio wireless regional area network standard

Cited by 914 publications

References 2 publications

Analytical and learning‐based spectrum sensing time optimisation in cognitive radio systems

Analytical and learning‐based spectrum sensing time optimisation in cognitive radio systems

Opportunistic Spectrum Utilization in Vehicular Communication Networks

Prediction based channel allocation performance for cognitive radio

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