NeuralEmu uses machine learning trained on real 5G telemetry to predict resource blocks and modulation for multiple users, cutting emulation error by 51-57% versus prior tools for web, video, and gaming metrics.
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StreamGuard implements a closed-loop 5G architecture that prioritizes subflows in video conferencing via RAN monitoring and packet marking, delivering up to 70% better QoE or 2x higher background throughput in testbed evaluations.
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NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation
NeuralEmu uses machine learning trained on real 5G telemetry to predict resource blocks and modulation for multiple users, cutting emulation error by 51-57% versus prior tools for web, video, and gaming metrics.
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StreamGuard: Exploring a 5G Architecture for Efficient, Quality of Experience-Aware Video Conferencing
StreamGuard implements a closed-loop 5G architecture that prioritizes subflows in video conferencing via RAN monitoring and packet marking, delivering up to 70% better QoE or 2x higher background throughput in testbed evaluations.