A Stochastic Geometry Analysis of Energy Harvesting in Large Scale Wireless Networks

TL;DR

This paper uses stochastic geometry to analyze the sustainable capacity of large-scale wireless networks with RF energy harvesting, deriving optimal base station densities for maximum throughput.

Contribution

It provides an analytical framework for evaluating energy harvesting and data delivery in large wireless networks with Poisson-distributed nodes.

Findings

Maximum throughput achieved at optimal BS density.

Analytical expressions for user count and success probability.

Validation through extensive simulations.

Abstract

In this paper, the theoretical sustainable capacity of wireless networks with radio frequency (RF) energy harvesting is analytically studied. Specifically, we consider a large scale wireless network where base stations (BSs) and low power wireless devices are deployed by homogeneous Poisson point process (PPP) with different spatial densities. Wireless devices exploit the downlink transmissions from the BSs for either information delivery or energy harvesting. Generally, a BS schedules downlink transmission to wireless devices. The scheduled device receives the data information while other devices harvest energy from the downlink signals. The data information can be successfully received by the scheduled device only if the device has sufficient energy for data processing, i.e., the harvested energy is larger than a threshold. Given the densities of BSs and users, we apply stochastic geometry to analyze the expected number of users per cell and the successful information delivery probability of a wireless device, based on which the total network throughput can be derived. It is shown that the maximum network throughput per cell can be achieved under the optimal density of BSs. Extensive simulations validate the analysis.