Spectrum mobility in cognitive radio networks

Christian, Ivan; Moh, Sangman; Chung, Ilyong; Lee, Jinyi

doi:10.1109/mcom.2012.6211495

Cited by 179 publications

(109 citation statements)

References 11 publications

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“…Hattab et al, Jang and Umar et al [7,8,9] studied extensively the performance of timing based SHO. This type of SHO is further classified into reactive, proactive and hybrid spectrum handoff [10]. In reactive method the free channel is sensed first then the handoff action is performed and finally the channel switching is done after identifying the free channel.…”

Section: Handoff Triggering Timing Based Shomentioning

confidence: 99%

“…NSH is defined as the number of handoffs that occurs in CR networks during one session of CR data transmission.CR networks are considered to be efficient if and only if the number of handoff occurring are minimal [10].This indicator is investigated in the interference-limited resource optimization in cognitive femto cells with fairness and imperfect spectrum sensing [17]. It is also proposed and investigated that this value can be effectively reduced by a delay requirement [19] and spectrum usage [18].…”

Section: Number Of Spectrum Handoff (Nsh)mentioning

confidence: 99%

“…Link maintenance probability indicates that the probability that the communication link is successfully maintained for successful data transmission within the allowable spectrum handoff in CR networks [10].This probability parameter was investigated for effective SHO in case of adhoc networks in CR for seamless spectrum sensing and effective channel usage [19]. Moreover, LMP parameter was observed and investigated under spectrum sharing using femto cells to enable packet transfer without loss [21].…”

Section: Link Maintenance Probability (Lmp)mentioning

confidence: 99%

See 2 more Smart Citations

A Study on Quantitative Parameters of Spectrum Handoff in Cognitive Radio Networks

J¹,

Murugesh²

2017

IJWMN

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The innovation of wireless technologies requires dynamic allocation of spectrum band in an efficient manner. This has been achieved by Cognitive Radio (CR) networks which allow unlicensed users to make use of free licensed spectrum, when the licensed users are kept away from that spectrum. The cognitive radio makes decision, switching from primary user to secondary user and vice-versa, based on its built-in interference engine. It allows secondary users to makes use of a channel based on its availability i.e. on the absence of the primary user and they should vacate the channel once the primary user re-enters and continue their communication on another available channel and this process in the cognitive radio isknown as spectrum mobility. The main objective of spectrum mobility is that, there is no interruption caused due to the channel occupied by secondary users and maintains a good quality of service. In order to achieve better spectrum mobility, it is mandatory to choose an effective spectrum handoff strategy with the capability of predicting spectrum mobility. The handoff strategy with its parameters and its impact is an important concept in spectrum mobility but fairly explored. In this paper an empirical study on quantitative parameters involved in spectrum mobility prediction are discussed in detail. These parameters are studied extensively because they play a vital role in the spectrum handoff process moreover the impact of these parameters in various handoff methods can be used to predict the effectiveness of the system.

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Section: Handoff Triggering Timing Based Shomentioning

confidence: 99%

Section: Number Of Spectrum Handoff (Nsh)mentioning

confidence: 99%

Section: Link Maintenance Probability (Lmp)mentioning

confidence: 99%

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A Study on Quantitative Parameters of Spectrum Handoff in Cognitive Radio Networks

J¹,

Murugesh²

2017

IJWMN

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“…Transmissions may be paused when the selection of a channel is defective because it may change for various reasons, for instance, when the selected channel is about to be occupied by a PU, it is already occupied, or it presents low quality for transmissions. Hence, finding a channel suitable for communication is essential for spectral mobility [10]. Also, channel selection depends on several factors, for example, channel capacity, channel availability during the spectral handoff, and the probability of channel availability.…”

Section: Introductionmentioning

confidence: 99%

“…Poor channel selection can cause multiple spectral handoffs, which may degrade performance of the whole system. The most common approach for channel selection is to employ a list of BKCs, e.g., in [10,11]. The most common channel selection approach is to use a list of BKCs, i.e., as presented in [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multivariable algorithm for dynamic channel selection in cognitive radio networks

Hernández

Salgado

Garcı́a

et al. 2015

J Wireless Com Network

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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work presents a multivariable algorithm for dynamic channel selection used in cognitive wireless networks. The channel selection is based on the fuzzy analytical hierarchical process (FAHP) method. The selected criteria for choosing the best backup channel are probability of channel availability, estimated channel time availability, signal to noise plus interference ratio, and bandwidth. These criteria are determined by means of a customized Delphi Method and using the FAHP technique; the corresponding weight and significance is calculated for two applications classified as best effort (BE) and real time (RT). The insertion of the fuzzy logic in the AHP algorithm allows better handling of inaccurate information because, as shown the results, consider more options to evaluate in contrast to a conventional AHP. As a difference with related work, the performance of our proposed FAHP method was validated with captured data in experiments realized at the GSM frequency band (824-849 MHz). This is due to the challenge of finding white spaces to communicate in this frequency band. This band represents more disputes in accessing spectral opportunities than other radio frequency (RF) bands because of the high demand for mobile phone communications. The proposed FAHP algorithm has a practical computational complexity and provides an effective frequency-channel selection. This proposed FAHP algorithm presents a new methodology to select and classify the variables based on a modified version of the Delphi method. The results of the proposed method were contrasted numerically with other three methods.

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