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Machine-Type Communication with Random Access and Data Aggregation: A Stochastic Geometry Approach

Enabling machine-type communication (MTC) over large scale cellular networks is a promising solution to handling the emerging MTC traffic. To enable a massive number of machines to connect to the base station, random access mechanisms and data aggregation have been largely studied separately in the literature. In this paper, we use stochastic geometry to investigate MTC over cellular with access class barring enhanced random access and data aggregation. We present an approximate yet accurate and tractable analytical framework for characterizing the MTC performance in terms of the machine type device (MTD) success probability, average number of successful MTDs and probability of successful preamble utilization. We validate the proposed model by comparison with simulations. Our results show that while the provision of more resources for the relaying phase benefits MTC, the provision of more preambles in the random access is not always beneficial to MTC. Thus, system parameters need to be chosen carefully to benefit the MTC traffic.