Throughput Optimizing Localized Link Scheduling for Multihop Wireless Networks Under Physical Interference Model

TL;DR

This paper introduces new localized link scheduling algorithms for multihop wireless networks that operate under the more realistic physical interference model, achieving provable throughput guarantees and improved approximation ratios.

Contribution

It presents the first localized algorithms with constant and logarithmic approximation ratios under the physical interference model, addressing a key challenge in wireless network scheduling.

Findings

First localized algorithm with constant throughput guarantee under physical interference.

First localized algorithm with logarithmic approximation ratio for the physical interference model.

Simulation results confirm the algorithms' correctness and efficiency.

Abstract

We study throughput-optimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on link scheduling under the physical interference model, and even fewer on more practical distributed or localized scheduling. In this paper, we tackle the challenges of localized link scheduling posed by the complex physical interference constraints. By cooperating the partition and shifting strategies into the pick-and-compare scheme, we present a class of localized scheduling algorithms with provable throughput guarantee subject to physical interference constraints. The algorithm in the linear power setting is the first localized algorithm that achieves at least a constant fraction of the optimal capacity region subject to physical interference constraints. The algorithm in the uniform power setting is the first localized algorithm with a logarithmic approximation ratio to the optimal solution. Our extensive simulation results demonstrate correctness and performance efficiency of our algorithms.