A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks

Wang, Lichun; Wang, Chung-Wei; Feng, Kai-Ten

doi:10.1109/mwc.2011.6108330

Cited by 70 publications

(58 citation statements)

References 12 publications

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“…The important issue of modeling the spectrum handoff in presence of multiple interruptions due to arrival of primary users was analyzed in 17,[19][20][21] . For the analysis of spectrum handoff, two approaches were adopted in research literature depending on the timing of the selection of the channels to be used at the time of handoff.…”

Section: Handoff Issues In Cognitive Radio Networkmentioning

confidence: 99%

Spectrum Handoff in Cognitive Radio Networks: A Survey

Lala

Balkhi

Mir

2017

Orient. J. Comp. Sci. and Technol

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Cognitive radio (CR) is a promising solution to improve the spectrum utilization by enabling unlicensed users to exploit the spectrum in an opportunistic manner. Spectrum handoff is a different type of handoff in CR necessitated by the reappearance of primary user (PU) in the licensed band presently occupied by the secondary users (SUs). Spectrum handoff procedures aim to help the SUs to vacate the occupied licensed spectrum and find suitable target channel to resume the unfinished transmission. The purpose of spectrum mobility management in cognitive radio networks is to make sure that the transitions are made smoothly and rapidly such that the applications running on a cognitive user perceive minimum performance degradation during a spectrum handoff. In this paper, we will survey the literature on spectrum handoff in cognitive radio networks.

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Section: Handoff Issues In Cognitive Radio Networkmentioning

confidence: 99%

Spectrum Handoff in Cognitive Radio Networks: A Survey

Lala

Balkhi

Mir

2017

Orient. J. Comp. Sci. and Technol

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“…If Y is exponentially distributed with parameter α, we can assume an M/G/1 queue for the interruptions. Note that, as discussed in the introduction, this alternative approach provides an approximation because we know that the real distribution of the busy periods in an M/G/1 queue is a complicated function built on the Bessel function [9], which cannot exactly be matched to R. We discussed this alternative approach in this paper for completeness and as an extension of the analysis provided in [6,12,13] for multiple classes of traffic with preemptive and non-preemptive service disciplines. Using this alternative approach, it is possible to find approximate results for the preemptive and nonpreemptive service disciplines as follows.…”

Section: Alternative Approach For Priority Queueing Analysismentioning

confidence: 99%

“…In [6,[12][13][14], queueing models for an OSA network with a single class of traffic were derived using a similar approach as in [7][8][9][10][11], whereby the server interruption periods for the cognitive radio users are busy periods generated by the preemptive primary traffic. This approach has several major deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Priority queueing models for cognitive radio networks with traffic differentiation

Azarfar

Frigon

Sansò

2014

J Wireless Com Network

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In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and used to illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation.

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“…Much work in recent years has been devoted to energy savings and energy harvesting in wireless communications [7][8][9][10]. Smart techniques have been suggested to adapt transmission to the energy and channel conditions so as to save energy and adapt the transmission power cooperatively so as to minimise the energy consumption per successfully transmitted packet [11][12][13][14][15][16]. Specifically in this article we study a node that has a data storage backlog (i.e., "data buffer") and has an energy storage device (i.e., "energy buffer").…”

Section: Introductionmentioning

confidence: 99%

Synchronising Energy Harvesting and Data Packets in a Wireless Sensor

Gelenbe

2015

Energies

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We consider a wireless sensor node that gathers energy through harvesting and reaps data through sensing. The node has a wireless transmitter that sends out a data packet whenever there is at least one "energy packet" and one "data packet", where an energy packet represents the amount of accumulated energy at the node that can allow the transmission of a data packet. We show that such a system is unstable when both the energy storage space and the data backlog buffer approach infinity, and we obtain the stable stationary solution when both buffers are finite. We then show that if a single energy packet is not sufficient to transmit a data packet, there are conditions under which the system is stable, and we provide the explicit expression for the joint probability distribution of the number of energy and data packets in the system. Since the two flows of energy and data can be viewed as flows that are instantaneously synchronised, this paper also provides a mathematical analysis of a fundamental problem in computer science related to the stability of the "join" synchronisation primitive.

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A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks

Cited by 70 publications

References 12 publications

Spectrum Handoff in Cognitive Radio Networks: A Survey

Spectrum Handoff in Cognitive Radio Networks: A Survey

Priority queueing models for cognitive radio networks with traffic differentiation

Synchronising Energy Harvesting and Data Packets in a Wireless Sensor

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