Approximations of the Aggregated Interference Statistics for Outage Analysis in Massive MTC

TL;DR

This paper develops analytical approximations for aggregated interference in massive MTC, enabling efficient outage probability evaluation and resource allocation strategies in large-scale sensor networks.

Contribution

It introduces a novel continuous approximation method for interference statistics in massive MTC, facilitating closed-form outage probability expressions and optimized resource allocation.

Findings

Approximations closely match numerical simulations.

Resource allocation based on statistics reduces outage probability.

Proposed methods improve performance analysis in massive MTC systems.

Abstract

This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram--Charlier series expansion of a truncated Gaussian kernel is proposed. We use this approximation to derive an analytic closed-form expression for the outage probability, corresponding to the event of the signal-to-interference-and-noise ratio being below a detection threshold. This metric is useful since it can be used for evaluating the performance of mMTC systems. We analyze, as an illustrative application of the previous approximation, a scenario with several multi-antenna collector nodes, each equipped with a set of predefined spatial beams. We consider two setups, namely single- and multiple-resource, in reference to the number of resources that are allocated to each beam. A graph-based approach that minimizes the average outage probability, and that is based on the statistics approximation, is used as allocation strategy. Finally, we describe an access protocol where the resource identifiers are broadcast (distributed) through the beams. Numerical simulations prove the accuracy of the approximations and the benefits of the allocation strategy.