DRAFT: Paris Telecom Team Introduces REBT-DL Metric to Balance 5G Throughput and EMF Exposure — EMF Signal
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IEEE ICC 2026 / arXiv
Research

🇫🇷 Paris Telecom Team Introduces REBT-DL Metric to Balance 5G Throughput and EMF Exposure

July 18, 2026 (EMFS)
Jun 8, 2026 (original)
Source: IEEE ICC 2026 / arXiv
Source category: Research
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A theoretical study from Telecom Paris (Trinh, Wang, Wiart) builds stochastic-geometry models for downlink EMF exposure and energy efficiency in 5G multi-connectivity networks, using EN-DC as the reference use case. They introduce a new metric — Radiated Energy per Bit Transmitted in the Downlink (REBT-DL) — combining throughput and received power. Validated against real base-station data from Paris, the model shows that network configuration significantly affects both exposure and REBT-DL, and that a repulsion-aware point process (β-GPP) matches real deployments better than the standard Poisson model.

"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."

— Trinh et al.

"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."

— Trinh et al.

"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."

— Trinh et al.

Source

Evaluation of EMF Exposure to Throughput Ratio for Sustainable 5G Networks www.emf-portal.org

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

Trinh et al. (2026) IEEE International Conference on Communications Journal Level 1ⓘ

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🇫🇷 Paris Telecom team introduces REBT-DL — a new metric that weighs 5G throughput against EMF exposure.

Validated against Paris base-station data; β-GPP beats Poisson for real deployments.

Source: IEEE ICC 2026 / arXiv

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