Evaluation of EMF Exposure to Throughput Ratio for Sustainable 5G Networks

TL;DR

This study develops stochastic geometry models to analyze electromagnetic field exposure and efficiency in 5G networks, introducing a new metric REBT-DL, and validates the models with real data, emphasizing sustainable deployment strategies.

Contribution

It introduces a stochastic geometry framework using beta-Ginibre point process for realistic modeling of 5G base station deployment and proposes the REBT-DL metric for energy-aware network assessment.

Findings

Network configuration impacts EMF exposure and REBT-DL.

Beta-Ginibre point process better models real base station distributions.

Proposed framework validated with real Paris data.

Abstract

This paper builds stochastic geometry frameworks for analyzing downlink electromagnetic field (EMF) exposure and efficiency in 5G multi-connectivity networks, using 5G E-UTRAN New Radio - Dual Connectivity (EN-DC) configuration as a representative use case. The Poisson point process (PPP) and the beta-Ginibre point process (beta-GPP) are used to model the spatial distribution of base stations (BSs), where beta-GPP effectively captures the repulsion observed in real deployments. We derive tractable expressions for the distribution of EMF exposure and validate the framework through both Monte Carlo simulations and real BS data from Paris. In addition to conventional metrics, we introduce the Radiated Energy per Bit Transmitted in the Downlink (REBT-DL), which accounts for throughput and received power. Results show that network configuration significantly affect exposure and REBTDL, highlighting the relevance of energy-aware deployment strategies and confirming the proposed approach as a comprehensive tool for sustainable network evaluation. The results also confirm that \b{eta}-GPP provides a more accurate fit to practical deployments than PPP.