TY - JOUR
T1 - Risk-averse access point selection in wireless communication networks
AU - Ma, Wann Jiun
AU - Oh, Chanwook
AU - Liu, Yang
AU - Dentcheva, Darinka
AU - Zavlanos, Michael M.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Distributed optimization
KW - optimal wireless networking
KW - risk-averse optimization
UR - http://www.scopus.com/inward/record.url?scp=85041192843&partnerID=8YFLogxK
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U2 - 10.1109/TCNS.2018.2792309
DO - 10.1109/TCNS.2018.2792309
M3 - Article
AN - SCOPUS:85041192843
VL - 6
SP - 24
EP - 36
JO - IEEE Transactions on Control of Network Systems
JF - IEEE Transactions on Control of Network Systems
IS - 1
M1 - 8253894
ER -