On the SIR Meta Distribution in Massive MTCNetworks with Scheduling and Data Aggregation

TL;DR

This paper analyzes the SIR meta distribution in massive machine-type communication networks with data aggregation, focusing on scheduling methods to improve reliability for numerous devices.

Contribution

It introduces the use of SIR meta distribution to evaluate per-link reliability in data aggregation scenarios with scheduling, comparing RRS and CRS methods.

Findings

CRS outperforms RRS in reliability metrics.

Meta distribution provides detailed reliability insights.

Scheduling impacts the fraction of users meeting target reliability.

Abstract

Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. We use the concept of meta distribution (MD) of the signal-to-interference ratio (SIR) to gain a complete understanding of the per-link reliability and describe the performance of two scheduling methods for data aggregation of machine type communication (MTC): random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements.