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Surrogate modeling based cognitive decision engine for optimization of WLAN performance

(2017) WIRELESS NETWORKS. 23(8). p.2347-2359
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Abstract
Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed.
Keywords
RADIO NETWORKS, SPECTRUM ACCESS, FRAMEWORK, Cognitive decision engine, Surrogate modeling, Interference management, Dynamic spectrum access, WiFi, WLAN

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Citation

Please use this url to cite or link to this publication:

Chicago
Plets, David, Krishnan Chemmangat, Dirk Deschrijver, Michael Mehari, Selvakumar Ulaganathan, Mostafa Pakparvar, Tom Dhaene, Jeroen Hoebeke, Ingrid Moerman, and Emmeric Tanghe. 2017. “Surrogate Modeling Based Cognitive Decision Engine for Optimization of WLAN Performance.” Wireless Networks 23 (8): 2347–2359.
APA
Plets, D., Chemmangat, K., Deschrijver, D., Mehari, M., Ulaganathan, S., Pakparvar, M., Dhaene, T., et al. (2017). Surrogate modeling based cognitive decision engine for optimization of WLAN performance. WIRELESS NETWORKS, 23(8), 2347–2359.
Vancouver
1.
Plets D, Chemmangat K, Deschrijver D, Mehari M, Ulaganathan S, Pakparvar M, et al. Surrogate modeling based cognitive decision engine for optimization of WLAN performance. WIRELESS NETWORKS. Dordrecht: Springer; 2017;23(8):2347–59.
MLA
Plets, David et al. “Surrogate Modeling Based Cognitive Decision Engine for Optimization of WLAN Performance.” WIRELESS NETWORKS 23.8 (2017): 2347–2359. Print.
@article{8545110,
  abstract     = {Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed.},
  author       = {Plets, David and Chemmangat, Krishnan and Deschrijver, Dirk and Mehari, Michael and Ulaganathan, Selvakumar and Pakparvar, Mostafa and Dhaene, Tom and Hoebeke, Jeroen and Moerman, Ingrid and Tanghe, Emmeric},
  issn         = {1022-0038},
  journal      = {WIRELESS NETWORKS},
  keywords     = {RADIO NETWORKS,SPECTRUM ACCESS,FRAMEWORK,Cognitive decision engine,Surrogate modeling,Interference management,Dynamic spectrum access,WiFi,WLAN},
  language     = {eng},
  number       = {8},
  pages        = {2347--2359},
  publisher    = {Springer},
  title        = {Surrogate modeling based cognitive decision engine for optimization of WLAN performance},
  url          = {http://dx.doi.org/10.1007/s11276-016-1293-0},
  volume       = {23},
  year         = {2017},
}

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