Performance Evaluation and Optimization of LPWA IoT Networks: A Stochastic Geometry Approach

TL;DR

This paper develops an analytical framework using stochastic geometry to model, analyze, and optimize the reliability, energy efficiency, and operation control of LPWA IoT networks, considering interference, traffic, and QoS trade-offs.

Contribution

It introduces a novel stochastic geometry-based model for LPWA IoT networks that accounts for interference, heterogeneous deployment, and asynchronous radio usage, providing insights for network planning and optimization.

Findings

Increasing access point density improves QoS.

More transmitted replicas and higher transmit power enhance reliability.

The energy-optimized control policy extends device battery life.

Abstract

Leveraging grant-free radio access for enabling lowpower wide-area (LPWA) Internet of Things (IoT) connectivity has attracted lots of attention in recent years. Regarding lack of research on LPWA IoT networks, this work is devoted to reliability modeling, battery-lifetime analysis, and operation-control of such networks. We derive the interplay amongst density of the access points, communication bandwidth, volume of traffic from heterogeneous sources, and quality of service (QoS) in communications. The presented analytical framework comprises modeling of interference from heterogeneous sources with correlated deployment locations and time-frequency asynchronous radio-resource usage patterns. The derived expressions represent the operation regions and rates in which, energy and cost resources of devices and the access network, respectively, could be traded to achieve a given level of QoS in communications. For example, our expressions indicate the expected increase in QoS by increasing number of transmitted replicas, transmit power, density of the access points, and communication bandwidth. Our results further shed light on scalability of such networks and figure out the bounds up to which, scaling resources can compensate the increase in traffic volume and QoS demand. Finally, we present an energy-optimized operation control policy for IoT devices. The simulation results confirm tightness of the derived analytical expressions, and indicate usefulness of them in planning and operation control of IoT networks.