Approximate Aggregate Utility Maximization in Multi-Hop Wireless Networks using Distributed Greedy Scheduling

TL;DR

This paper investigates a distributed greedy heuristic for link scheduling in multihop wireless networks to maximize aggregate utility, providing bounds and simulation results showing near-optimal performance.

Contribution

It introduces a novel distributed greedy heuristic for approximate aggregate utility maximization in multihop wireless networks, addressing NP-hard maximal independent set computation.

Findings

Distributed greedy heuristic achieves utility close to optimal.

Bounds on aggregate utility provide insights into network performance.

Simulation results confirm near-optimal utility with the heuristic.

Abstract

In this paper, we study the performance of greedy scheduling in multihop wireless networks, where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. The dual can be solved optimally, only with the knowledge of the maximal independent sets in the network. But computation of maximal independent sets is known to be NP-hard. Motivated by this, we propose a distributed greedy heuristic to address the problem of link scheduling. We evaluate the effect of the distributed greedy heuristic on aggregate utility maximization in detail, for the case of an arbitrary graph. We provide some insights into the factors affecting aggregate utility maximization in a network, by providing bounds on the same. We give simulation results for the approximate aggregate utility maximization achieved under distributed implementation of the greedy heuristic and find them close to the maximum aggregate utility obtained using optimal scheduling.