Throughput-Optimal Random Access with Order-Optimal Delay

TL;DR

This paper introduces U-CSMA, a distributed scheduling policy that achieves high throughput and order-optimal low delay in large multihop wireless networks, validated through analytical and simulation results.

Contribution

The paper proposes U-CSMA, a novel distributed policy that ensures both throughput optimality and order-optimal delay in large wireless networks.

Findings

U-CSMA achieves order-optimal delay of O(1) in one-hop traffic.

Delay under U-CSMA scales as O(1/(1-ρ)^3) for grid-like topologies.

Simulations confirm delay and utility performance in random geometric networks.

Abstract

In this paper, we consider CSMA policies for scheduling of multihop wireless networks with one-hop traffic. The main contribution of this paper is to propose Unlocking CSMA (U-CSMA) policy that enables to obtain high throughput with low (average) packet delay for large wireless networks. In particular, the delay under U-CSMA policy becomes order-optimal. For one-hop traffic, delay is defined to be order-optimal if it is O(1), i.e., it stays bounded, as the network-size increases to infinity. Using mean field theory techniques, we analytically show that for torus (grid-like) interference topologies with one-hop traffic, to achieve a network load of $\rho$, the delay under U-CSMA policy becomes $O(1/(1-\rho)^{3})$ as the network-size increases, and hence, delay becomes order optimal. We conduct simulations for general random geometric interference topologies under U-CSMA policy combined with congestion control to maximize a network-wide utility. These simulations confirm that order optimality holds, and that we can use U-CSMA policy jointly with congestion control to operate close to the optimal utility with a low packet delay in arbitrarily large random geometric topologies. To the best of our knowledge, it is for the first time that a simple distributed scheduling policy is proposed that in addition to throughput/utility-optimality exhibits delay order-optimality.