Improving Medium Access in Single-Destination Networks with Heterogeneous Propagation Channels

This paper studies coverage probabilities of randomly deployed radio nodes in single-destination networks. Such networks are typically operated for data aggregation in machine-to-machine (M2M) communication scenarios. In large-scale deployments, diverse environments usually result in heterogeneous propagation channels. We address the question to which extent an increased medium access can compensate for impaired propagation conditions. Based on superimposed Poisson Point Processes and a strongest interferer approximation, we propose a closed-form solution for the probability of a successful packet transmission under heterogeneous channel conditions. The applicability of the framework is demonstrated for an exemplary network configuration with a given latency constraint. Jointly optimising the medium access probabilities can either increase the network coverage area (e.g. by factor two) or reduce the overall networks power consumption by up to more than 30% in sufficiently dense deployments.