Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

TL;DR

This paper presents a distributed stochastic power control algorithm for ad-hoc networks that achieves global optimality in nonconvex utility maximization problems, incorporating queue stabilization and multicast extensions.

Contribution

It introduces a novel boundary-based transformation and extended duality approach for globally optimal distributed power control in nonconvex settings, with convergence enhancements.

Findings

Guarantees global optimality despite nonconvexity

Integrates power control with queue stabilization for network efficiency

Extends algorithms to multicast traffic with proven optimality

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.