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NeuralEmu uses ML to create high-fidelity 5G network emulation

Researchers have developed NeuralEmu, a novel emulation framework that uses machine learning to accurately simulate 5G network behavior. This system learns complex scheduler allocations directly from high-resolution network telemetry data. NeuralEmu addresses limitations of existing tools by enabling feedback interaction and capturing realistic cross-user contention, significantly reducing emulation error for various network applications. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Enables more accurate testing of network protocols and applications by simulating complex 5G scheduler behaviors.

RANK_REASON Academic paper detailing a new ML-based emulation framework for 5G networks.

Read on arXiv cs.LG →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 · Haoran Wan, Yaxiong Xie, Kyle Jamieson ·

    NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation

    arXiv:2604.26080v1 Announce Type: cross Abstract: Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workl…