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.

翻译：本文构建了随机几何框架，用于分析5G多连接网络中的下行链路电磁场（EMF）暴露与效率，并以5G E-UTRAN新空口双连接（EN-DC）配置作为代表性用例进行研究。采用泊松点过程（PPP）和β-Ginibre点过程（β-GPP）对基站（BS）的空间分布进行建模，其中β-GPP能有效捕捉实际部署中观测到的排斥效应。我们推导了电磁场暴露分布的易处理表达式，并通过蒙特卡洛模拟和巴黎实际基站数据验证了该框架。除传统指标外，本文引入了下行链路每比特传输辐射能量（REBT-DL），该指标综合考虑了吞吐量与接收功率。结果表明，网络配置对电磁场暴露和REBT-DL具有显著影响，凸显了能量感知部署策略的重要性，并证实所提方法可作为可持续网络评估的综合工具。结果同时验证了β-GPP比PPP能更精确地拟合实际部署场景。