Queueing Systems

, Volume 91, Issue 3–4, pp 241–263 | Cite as

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

  • Alessandro ZoccaEmail author


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 analytic 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.


Random-access networks Performance evaluation Throughput analysis Markov process Hitting times Mixing times Partial q-coloring 

Mathematics Subject Classification

60J27 60K35 68M10 68M20 90B15 90B18 



The author acknowledges the NWO Rubicon Grant 680.50.1529 and the Resnick Sustainability Institute at Caltech for the support and is grateful to Sem Borst for the discussions at the early stages of this work.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.California Institute of TechnologyPasadenaUSA

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