TY - GEN
T1 - Distributed power control for ad-hoc communications via stochastic nonconvex utility optimization
AU - Yang, Lei
AU - Sagduyu, Yalin E.
AU - Zhang, Junshan
AU - Li, Jason H.
PY - 2011/9/2
Y1 - 2011/9/2
N2 - It is known that distributed power control in wireless ad-hoc networks is challenging, due to the inherent global coupling between concurrent transmissions interfering with each other. Observing that the globally optimal point lies on the boundary of the feasible region, we transform the utility maximization problem into a more structured problem in the form of maximizing the minimum weighted utility. Then, we develop a centralized algorithm for the minimum weighted utility maximization problem as a benchmark. Next, by using extended duality theory, we introduce penalty multipliers and decompose the minimum weighted utility maximization problem into subproblems for individual users. Appealing to the simulated annealing method, we propose a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the overall system utility. Although the underlying optimization problem is nonconvex, our algorithm can guarantee global optimality although the convergence rate may be slow due to the usage of simulated annealing. We improve the convergence rate further by devising an enhanced algorithm based on the geometric cooling schedule.
AB - It is known that distributed power control in wireless ad-hoc networks is challenging, due to the inherent global coupling between concurrent transmissions interfering with each other. Observing that the globally optimal point lies on the boundary of the feasible region, we transform the utility maximization problem into a more structured problem in the form of maximizing the minimum weighted utility. Then, we develop a centralized algorithm for the minimum weighted utility maximization problem as a benchmark. Next, by using extended duality theory, we introduce penalty multipliers and decompose the minimum weighted utility maximization problem into subproblems for individual users. Appealing to the simulated annealing method, we propose a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the overall system utility. Although the underlying optimization problem is nonconvex, our algorithm can guarantee global optimality although the convergence rate may be slow due to the usage of simulated annealing. We improve the convergence rate further by devising an enhanced algorithm based on the geometric cooling schedule.
KW - Distributed Power Control
KW - Extended Duality Theory
KW - Nonconvex Optimization
KW - Simulated Annealing
UR - http://www.scopus.com/inward/record.url?scp=80052181507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052181507&partnerID=8YFLogxK
U2 - 10.1109/icc.2011.5962986
DO - 10.1109/icc.2011.5962986
M3 - Conference contribution
AN - SCOPUS:80052181507
SN - 9781612842332
T3 - IEEE International Conference on Communications
BT - 2011 IEEE International Conference on Communications, ICC 2011
T2 - 2011 IEEE International Conference on Communications, ICC 2011
Y2 - 5 June 2011 through 9 June 2011
ER -