Distributed stochastic power control in ad hoc networks: a nonconvex optimization case

Lei Yang,Junshan Zhang,Yalin E Sagduyu ,Jason Hongjun Li

doi:10.1186/1687-1499-2012-231

Abstract

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.

Highlights

The broadcast nature of wireless transmissions makes wireless networks susceptible to interference, which deteriorates quality of service (QoS) provisioning
To improve the convergence rate, we propose an enhanced DSPC (EDSPC) algorithm by empirically choosing the initial penalty values α0 and β0 and employing a geometric cooling schedule [18], which reduces the temperature T in simulated annealing (SA) by T = ξ T, 0 < ξ < 1, at each time epoch
Different from the techniques used in Section “Power control for unicast communications”, we relax rl min m∈Ml rlm in as rl ≤ log(1 + γlm(p)), ∀ l ∈ L, m ∈ Ml, in order to Distributed global optimization algorithms We develop distributed algorithms that can find the globally optimal solutions to (13) based on extended duality theory (EDT) and SA

Summary

Introduction

The broadcast nature of wireless transmissions makes wireless networks susceptible to interference, which deteriorates quality of service (QoS) provisioning. To improve the convergence rate, we propose an EDSPC algorithm by empirically choosing the initial penalty values α0 and β0 and employing a geometric cooling schedule [18], which reduces the temperature T in SA by T = ξ T, 0 < ξ < 1, at each time epoch.

Results

Conclusion