Modeling and Forecasting Secondary User Activity in Cognitive Radio Networks

Akter, Lutfa; Natarajan, Balasubramaniam; Scoglio, Caterina

doi:10.1109/icccn.2008.ecp.50

Cited by 12 publications

(13 citation statements)

References 10 publications

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“…As in [8], the forecast process only involves a table-lookup to determine the next state at each instant from the current state estimate based on (36). β is fixed at 0.006 for this simulation.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, we consider a cognitive radio network traffic model where, (1) the PU follows a continuous time Markov chain; (2) multiple secondary users simultaneously use a spectral band when the PU is absent; (3) secondary users can arrive or depart in bulk resulting in an Erlangian traffic model. Unlike prior efforts in modeling and forecasting that are limited to Poisson traffic [8], we propose a Kalman filter based estimate for the number of secondary users from power level measurements for a more general Erlangian traffic model. We then use this estimate to determine an upper bound predictor of the number of secondary users in a spectral band.…”

Section: Introductionmentioning

confidence: 99%

“…SYSTEM MODEL As in [8], we assume that each channel in a CRN can be used by either a PU or one or more secondary users (once it is determined that the channel will not be used by a PU). We also assume that the PU follows Poisson arrival process with arrival and departure rates, λ p and μ p , respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, PU follows a two-state ON-OFF Markov process. In this work, unlike in [8], we assume that secondary users can arrive and leave the network as a bulk or group. This implies that non-nearest neighbor transitions are allowed.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Forecasting Secondary User Activity Considering Bulk Arrival and Bulk Departure Traffic Model

Akter

Natarajan

2009

2009 IEEE 70th Vehicular Technology Conference Fall

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Abstract-In this paper, we consider an Erlangian process model for secondary user (SU) traffic in a cognitive radio network (CRN). Unlike prior efforts that focused on Poisson traffic models, the model captures bulk arrival and departure scenarios in a frequency band that is open for opportunistic use by multiple secondary users. Specifically, we develop a Kalman filter based estimation and forecasting strategy for the number of secondary users. Using simulated data, we demonstrate that the proposed approach provides a robust upper bound prediction on the number of secondary users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Forecasting Secondary User Activity Considering Bulk Arrival and Bulk Departure Traffic Model

Akter

Natarajan

2009

2009 IEEE 70th Vehicular Technology Conference Fall

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“…The output characteristics of the model are then compared with real-life measurements at different frequency bands. Moreover, a Kalman filter based approach was proposed in [3] to model SUs' activity, where a continuous-time MC was used to forecast the number of SUs.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Occupancy and Residual Service Analysis in CRNs Using a Multi-Server Queueing Model

Zaman

Abbas

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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Cognitive radio technology enables secondary users (SUs) to opportunistically access the unused or sparsely utilized spectrum by primary users (PUs) without causing any harmful interference to PUs. Consequently, spectrum occupancy modeling appears as an essential task in cognitive radio networks (CRNs). In this paper, we model spectrum occupancy using a queueing theory based approach in order to evaluate the performance of CRNs in terms of network capacity and number of cognitive radio users waiting for services etc. The queue adopted in this model has variable service capacity and can be considered as a multi-service queue with server failure where each channel acts as a server. When a channel is occupied by a PU, it is regarded as a server failure. Using the probability generating function, the closed-form expressions for various performance parameters are derived. Numerical results on the remaining services for SUs and the expected number of SUs as the CRN capacity varies are illustrated.978-1-4799-8088-8/15/$31.00 ©2015 IEEE

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References

2012

Cognitive Radio Communications and Networking

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Modeling and Forecasting Secondary User Activity in Cognitive Radio Networks

Cited by 12 publications

References 10 publications

Modeling and Forecasting Secondary User Activity Considering Bulk Arrival and Bulk Departure Traffic Model

Modeling and Forecasting Secondary User Activity Considering Bulk Arrival and Bulk Departure Traffic Model

Spectrum Occupancy and Residual Service Analysis in CRNs Using a Multi-Server Queueing Model

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