Title: Aggregation and Resource Scheduling in Machine-type Communication Networks: A Stochastic Geometry Approach,
Abstract: Data aggregation is a promising approach to enable massive machine-type
communication (mMTC). This paper focuses on the aggregation phase where a
massive number of machine-type devices (MTDs) transmit to aggregators. By using
non-orthogonal multiple access (NOMA) principles, we allow several MTDs to
share the same orthogonal channel in our proposed hybrid access scheme. We
develop an analytical framework based on stochastic geometry to investigate the
system performance in terms of average success probability and average number
of simultaneously served MTDs, under imperfect successive interference
cancellation (SIC) at the aggregators, for two scheduling schemes: random
resource scheduling (RRS) and channel-aware resource scheduling (CRS). We
identify the power constraints on the MTDs sharing the same channel to attain a
fair coexistence with purely orthogonal multiple access (OMA) setups, then
power control coefficients are found so that these MTDs perform with similar
reliability. We show that under high access demand, the hybrid scheme with CRS
outperforms the OMA setup by simultaneously serving more MTDs with reduced
power consumption.