Risk-Averse Access Point Selection in Wireless Communication Networks
This paper considers the problem of selecting the optimal set of access points and routing decisions in wireless communication networks. We consider networks that are subject to uncertainty in the wireless channel, for example, due to multipath fading effects, and formulate the problem as a risk-ave...
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Published in | IEEE transactions on control of network systems Vol. 6; no. 1; pp. 24 - 36 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Online Access | Get full text |
ISSN | 2325-5870 2372-2533 |
DOI | 10.1109/TCNS.2018.2792309 |
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Abstract | This paper considers the problem of selecting the optimal set of access points and routing decisions in wireless communication networks. We consider networks that are subject to uncertainty in the wireless channel, for example, due to multipath fading effects, and formulate the problem as a risk-averse network flow problem with binary variables corresponding to the status of the sinks, namely, selected or not. Risk measures capture low-probability but high-cost events and, when used for stochastic optimization, they produce solutions that are more reliable compared to mean-value formulations and less conservative than worst-case approaches. By relaxing the integer constraints, we reformulate the problem as a linear optimization problem, which we solve in a distributed way using the accelerated distributed augmented Lagrangian method that was recently developed by the authors to solve optimization problems with convex separable objectives and linear coupling constraints. We present numerical simulations and experimental results using low-power wireless radios that demonstrate the ability of the proposed method to effectively deal with large variations in the quality of the wireless channel. |
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AbstractList | This paper considers the problem of selecting the optimal set of access points and routing decisions in wireless communication networks. We consider networks that are subject to uncertainty in the wireless channel, for example, due to multipath fading effects, and formulate the problem as a risk-averse network flow problem with binary variables corresponding to the status of the sinks, namely, selected or not. Risk measures capture low-probability but high-cost events and, when used for stochastic optimization, they produce solutions that are more reliable compared to mean-value formulations and less conservative than worst-case approaches. By relaxing the integer constraints, we reformulate the problem as a linear optimization problem, which we solve in a distributed way using the accelerated distributed augmented Lagrangian method that was recently developed by the authors to solve optimization problems with convex separable objectives and linear coupling constraints. We present numerical simulations and experimental results using low-power wireless radios that demonstrate the ability of the proposed method to effectively deal with large variations in the quality of the wireless channel. |
Author | Dentcheva, Darinka Oh, Chanwook Ma, Wann-Jiun Liu, Yang Zavlanos, Michael M. |
Author_xml | – sequence: 1 givenname: Wann-Jiun orcidid: 0000-0002-6427-7776 surname: Ma fullname: Ma, Wann-Jiun email: wann.jiun.ma@duke.edu organization: Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA – sequence: 2 givenname: Chanwook surname: Oh fullname: Oh, Chanwook email: chanwook.oh@duke.edu organization: Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA – sequence: 3 givenname: Yang orcidid: 0000-0001-6755-505X surname: Liu fullname: Liu, Yang email: yang.liu.261@duke.edu organization: Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA – sequence: 4 givenname: Darinka orcidid: 0000-0002-5668-4996 surname: Dentcheva fullname: Dentcheva, Darinka email: darinka.dentcheva@stevens.edu organization: Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, NJ, USA – sequence: 5 givenname: Michael M. orcidid: 0000-0003-1748-8228 surname: Zavlanos fullname: Zavlanos, Michael M. email: michael.zavlanos@duke.edu organization: Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA |
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SubjectTerms | Communication networks Communications networks Computer simulation Distributed optimization Formulations optimal wireless networking Optimization Random variables Reliability Risk risk-averse optimization Routing Uncertainty Wireless communication Wireless communications Wireless sensor networks |
Title | Risk-Averse Access Point Selection in Wireless Communication Networks |
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