Node-Based Optimal Power Control, Routing, and Congestion Control in Wireless Networks

TL;DR

This paper introduces a unified framework for optimizing power control, routing, and congestion control in wireless networks, ensuring convergence to optimal solutions through distributed algorithms that adapt to various physical-layer models.

Contribution

It develops a comprehensive, convergent distributed algorithm framework for joint power control, routing, and congestion control in wireless networks with convex and quasiconvex link cost models.

Findings

Algorithms converge to the global optimum from any initial point.

Framework accommodates arbitrary convex rate regions and quasiconvex link costs.

Seamless integration of congestion control into the optimization framework.

Abstract

We present a unified analytical framework within which power control, rate allocation, routing, and congestion control for wireless networks can be optimized in a coherent and integrated manner. We consider a multi-commodity flow model with an interference-limited physical-layer scheme in which power control and routing variables are chosen to minimize the sum of convex link costs reflecting, for instance, queuing delay. Distributed network algorithms where joint power control and routing are performed on a node-by-node basis are presented. We show that with appropriately chosen parameters, these algorithms iteratively converge to the global optimum from any initial point with finite cost. Next, we study refinements of the algorithms for more accurate link capacity models, and extend the results to wireless networks where the physical-layer achievable rate region is given by an arbitrary convex set, and the link costs are strictly quasiconvex. Finally, we demonstrate that congestion control can be seamlessly incorporated into our framework, so that algorithms developed for power control and routing can naturally be extended to optimize user input rates.