Delay performance modeling and analysis in clustered cognitive radio networks

Adem, Nadia; Hamdaoui, Bechir

doi:10.1109/glocom.2014.7036806

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…In other words, jammer is not able to jam because of lack of access opportunities. Low P j might seem appealing, but the lack of access opportunities leads to higher transmission delay .…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

“…The service probability, derived in , is the probability that at least a channel is idle (which is given by

((), 1 - \frac{1}{{(1 + v / u)}^{N}})

) multiplied by the probability that the channel is idle for at least a time frame (which is, considering our channel model, given by

e^{- u T_{s}}

). In other words, the service probability is expressed as

μ = ((), 1 - \frac{1}{{(1 + v / u)}^{N}}) e^{- u T_{s}}

The probability that a user switches between channels at any frame is the probability that the last assigned channel is busy during that frame while there is another channel idle and offered load is greater than zero. The switching probability is written as

P_{sw} = β \sum_{i = 1}^{N - 1} ((), \binom{N - 1}{i}) \frac{1}{{(v / u + 1)}^{N - i}} \frac{1}{{(u / v + 1)}^{i}}

…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

“…Considering the power spectral density of the channel, the average power of intercarrier interference at the l t h subchannel I l is expressed as

I_{l} = \frac{4 E_{s} T_{s}^{2}}{π^{2} f_{m 1} \sqrt{a}} \sum_{\begin{matrix} i = 0 \\ i \neq l \end{matrix}}^{N_{sc} - 1} \frac{1}{{(l - i)}^{2}} ([], {falsefalse}_{\int}^{0} f^{2} \frac{1}{x} K ((), \frac{1}{x}) df + {falsefalse}_{\int}^{(1 - a) f_{m 1}} f^{2} K (x) df)

The probability of error, corresponding to detecting the symbol at the l t h subcarrier, is defined only if there exists at least one idle primary user channel. The lack of a channel access opportunity causes a service delay . The error probability conditioned on the subchannel gain is given by

P_{e | α_{l}} = \sum_{i = 1}^{N} ((), \binom{N}{i}) \frac{1}{{(v / u + 1)}^{N - i}} \frac{1}{{(u / v + 1)}^{i}} ([], \frac{ρ}{i} Q ((), \sqrt{\frac{2 E_{s} | α_{l} |^{2}}{ρ J + I_{l}}}) + ((), 1 - \frac{ρ}{i}) Q ((), \sqrt{2<...>}))

…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

“…The availability of resources to cognitive users varies over time depending on primary user behaviors. The process of identifying and exploiting spectrum access opportunities causes performance degradation . Achieving a desired quality of service in cognitive networks while handling a security attack, for example, jamming, is a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating jamming attacks in mobile cognitive networks through time hopping

Adem

Hamdaoui

Yavuz

2016

Wirel. Commun. Mob. Comput.

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5G wireless networks will support massive connectivity mainly due to device-to-device communications. An enabling technology for device-to-device links is the dynamical spectrum access. The devices, which are equipped with cognitive radios, are to be allowed to reuse spectrum occupied by cellular links. The dynamical spectrum availability makes cognitive users switch between channels. Switching leads to energy consumption, latency, and communication overhead in general. The performance degrades even more when the network is under jamming attack. This type of attack is one of the most detrimental attacks. Addressing jamming while maintaining a desired quality of service is a challenge. While existing anti-jamming mechanisms assume stationary users, in this paper, we propose and evaluate countermeasures for mobile cognitive users. We propose two time-based techniques, which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on switching to countermeasure jamming. We achieve analytical solutions of jamming, switching, and error probabilities. Based on our findings, the proposed techniques out perform other existing frequency-based techniques.

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“…In other words, jammer is not able to jam because of lack of access opportunities. Low P j might seem appealing, but the lack of access opportunities leads to higher transmission delay .…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

“…The service probability, derived in , is the probability that at least a channel is idle (which is given by

((), 1 - \frac{1}{{(1 + v / u)}^{N}})

) multiplied by the probability that the channel is idle for at least a time frame (which is, considering our channel model, given by

e^{- u T_{s}}

). In other words, the service probability is expressed as

μ = ((), 1 - \frac{1}{{(1 + v / u)}^{N}}) e^{- u T_{s}}

P_{sw} = β \sum_{i = 1}^{N - 1} ((), \binom{N - 1}{i}) \frac{1}{{(v / u + 1)}^{N - i}} \frac{1}{{(u / v + 1)}^{i}}

…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

“…Considering the power spectral density of the channel, the average power of intercarrier interference at the l t h subchannel I l is expressed as

I_{l} = \frac{4 E_{s} T_{s}^{2}}{π^{2} f_{m 1} \sqrt{a}} \sum_{\begin{matrix} i = 0 \\ i \neq l \end{matrix}}^{N_{sc} - 1} \frac{1}{{(l - i)}^{2}} ([], {falsefalse}_{\int}^{0} f^{2} \frac{1}{x} K ((), \frac{1}{x}) df + {falsefalse}_{\int}^{(1 - a) f_{m 1}} f^{2} K (x) df)

P_{e | α_{l}} = \sum_{i = 1}^{N} ((), \binom{N}{i}) \frac{1}{{(v / u + 1)}^{N - i}} \frac{1}{{(u / v + 1)}^{i}} ([], \frac{ρ}{i} Q ((), \sqrt{\frac{2 E_{s} | α_{l} |^{2}}{ρ J + I_{l}}}) + ((), 1 - \frac{ρ}{i}) Q ((), \sqrt{2<...>}))

…”

Section: Proposed Schemes and Their Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mitigating jamming attacks in mobile cognitive networks through time hopping

Adem

Hamdaoui

Yavuz

2016

Wirel. Commun. Mob. Comput.

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“…For a given PUs' activity level, SUs might not be able to meet certain performance criteria. The required transparency of the SUs' activity could result in excessive packet drops or queue instability in buffered networks . Hence, modifications need to be introduced to network settings to make sure it achieves the desired quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

The impact of stochastic resource availability on cognitive network performance: modeling and analysis

Adem

Hamdaoui

2015

Wireless Communications

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Cognitive radio networks emerge as a promising solution for overcoming shortage and inefficient use of bandwidth resources by allowing secondary users (SUs) to access the primary users' (PUs) channel so long as they do not interfere with them. The dynamical spectrum availability makes SU's packet average delay one of the most important performance measures of a cognitive network. It is important to understand the nature of delay, as well as its dependence on PU behaviors. In this paper, we analytically model and analyze the dynamics of the spectrum availability and their impact on the SU's packet delay. The cognitive network is modeled as a discrete‐time queueing system. PU channel occupancy is modeled as a two‐state Markov chain. Our contribution in this paper is defining and characterizing the properties of the random process that describes the availability of the opportunistic resources. In addition, we apply the mean residual service time concept to achieve an analytical solution for the queueing delay. Moreover, inspired by the slotted Aloha system, we model the packet service mechanism and determine the manner in which it depends on the resource availability. The delay becomes unbounded if the spectrum availability dynamics are not carefully considered in network design. Copyright © 2015 John Wiley & Sons, Ltd.

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An Efficient MAC with Spectrum Handoff and Frame Fragmentation Strategies for Cognitive Radio Networks

AlQahtani

2021

Arab J Sci Eng

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Delay performance modeling and analysis in clustered cognitive radio networks

Cited by 14 publications

References 9 publications

Mitigating jamming attacks in mobile cognitive networks through time hopping

Mitigating jamming attacks in mobile cognitive networks through time hopping

The impact of stochastic resource availability on cognitive network performance: modeling and analysis

An Efficient MAC with Spectrum Handoff and Frame Fragmentation Strategies for Cognitive Radio Networks

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