Throughput Characterization of Wireless CSMA Networks With Arbitrary Sensing and Interference Topologies
For network engineers and researchers, this provides a more accurate analytical tool for throughput estimation and parameter tuning in complex CSMA networks, addressing a long-standing open problem.
The paper develops a new analytical framework for throughput characterization in wireless CSMA networks with arbitrary sensing and interference topologies, yielding explicit throughput expressions without the zero-propagation-delay assumption. Simulation results show the model significantly outperforms existing approaches in throughput estimation for dense deployments and strongly coupled scenarios.
The performance analysis of wireless CSMA networks is notoriously difficult due to the intricate sensing and interference relationships among links. Even the fundamental problem of throughput characterization remains open when sensing and interference topologies are both arbitrary. In this paper, we develop a new analytical framework for throughput characterization in wireless CSMA networks with arbitrary sensing and interference topologies. The proposed framework yields explicit throughput expressions without relying on the commonly adopted zero-propagation-delay assumption. The key idea is to exploit the clique structure of the sensing graph to transform the original CSMA network into an equivalent multi-channel network, and then model its dynamics through a discrete-time Markov renewal process. In this way, the framework explicitly captures global coupling among links and enables analytical evaluation of how access parameters affect network performance. The proposed analysis is applied to several representative CSMA scenarios, including networks with multi-BSS IEEE 802.11 networks with universal frequency reuse, and ad-hoc topologies exhibiting hidden-terminal, exposed-terminal, and flow-in-the-middle effects. Simulation results show that, in dense deployments and in scenarios with strong coupling among link behaviors, the proposed model significantly outperforms existing analytical approaches in throughput estimation and enables more accurate determination of access parameters.