Throughput Optimal Distributed Control of Stochastic Wireless Networks

TL;DR

This paper develops distributed power control algorithms for multi-hop wireless networks that optimize throughput using the MDB policy, ensuring throughput optimality despite iterative convergence delays.

Contribution

It introduces low-overhead, distributed gradient projection algorithms for MDB optimization in CDMA networks, demonstrating throughput optimality with convergence time.

Findings

Algorithms achieve throughput optimality in simulations

Convergence time does not compromise throughput performance

Distributed implementation reduces communication overhead

Abstract

The Maximum Differential Backlog (MDB) control policy of Tassiulas and Ephremides has been shown to adaptively maximize the stable throughput of multi-hop wireless networks with random traffic arrivals and queueing. The practical implementation of the MDB policy in wireless networks with mutually interfering links, however, requires the development of distributed optimization algorithms. Within the context of CDMA-based multi-hop wireless networks, we develop a set of node-based scaled gradient projection power control algorithms which solves the MDB optimization problem in a distributed manner using low communication overhead. As these algorithms require time to converge to a neighborhood of the optimum, the optimal rates determined by the MDB policy can only be found iteratively over time. For this, we show that the iterative MDB policy with convergence time remains throughput optimal.