  To enable massive machine type communication (mMTC), data aggregation is a
promising approach to reduce the congestion caused by a massive number of
machine type devices (MTDs). In this work, we consider a two-phase
cellular-based mMTC network where MTDs transmit to aggregators (i.e.,
aggregation phase) and the aggregated data is then relayed to base stations
(i.e., relaying phase). Due to the limited resources, the aggregators not only
aggregate data, but also schedule resources among MTDs. We consider two
scheduling schemes: random resource scheduling (RRS) and channel-aware resource
scheduling (CRS). By leveraging the stochastic geometry, we present a tractable
analytical framework to investigate the signal-to-interference ratio (SIR) for
each phase, thereby computing the MTD success probability, the average number
of successful MTDs and probability of successful channel utilization, which are
the key metrics characterizing the overall mMTC performance. Our numerical
results show that, although the CRS outperforms the RRS in terms of SIR at the
aggregation phase, the simpler RRS has almost the same performance as the CRS
for most cases with regards to the overall mMTC performance. Furthermore, the
provision of more resources at the aggregation phase is not always beneficial
to the mMTC performance.
