Temporal starvation in multi-channel CSMA networks: an analytical framework

TL;DR

This paper develops an analytical framework to understand how increasing the number of channels in multi-channel CSMA networks affects throughput and temporal starvation, revealing counterintuitive trade-offs and limitations of existing metrics.

Contribution

It introduces a novel analytical approach using first hitting times to study transient dynamics and shows that more channels do not necessarily improve throughput or reduce starvation.

Findings

Aggregate throughput decreases as channels increase.

Temporal starvation persists despite more channels.

Mixing time may overestimate starvation issues.

Abstract

In this paper we consider a stochastic model for a frequency-agile CSMA protocol for wireless networks where multiple orthogonal frequency channels are available. Even when the possible interference on the different channels is described by different conflict graphs, we show that the network dynamics can be equivalently described as that of a single-channel CSMA algorithm on an appropriate virtual network. Our focus is on the asymptotic regime in which the network nodes try to activate aggressively in order to achieve maximum throughput. Of particular interest is the scenario where the number of available channels is not sufficient for all nodes of the network to be simultaneously active and the well-studied temporal starvation issues of the single-channel CSMA dynamics persist. For most networks we expect that a larger number of available channels should alleviate these temporal starvation issues. However, we prove that the aggregate throughput is a non-increasing function of the number of available channels. To investigate this trade-off that emerges between aggregate throughput and temporal starvation phenomena, we propose an analytical framework to study the transient dynamics of multi-channel CSMA networks by means of first hitting times. Our analysis further reveals that the mixing time of the activity process does not always correctly characterize the temporal starvation in the multi-channel scenario and often leads to pessimistic performance estimates.