Logistic regression and a shallow MLP compiled as deterministic C modules inside a FlexRIC xApp achieve 1–25 µs inference and sub-4 ms end-to-end service latency on an OAI testbed, meeting the 10 ms Near-RT budget for >95% of projected loops.
To- ward 6G networks: Use cases and technologies
2 Pith papers cite this work. Polarity classification is still indexing.
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Digital-twin belief-state reinforcement learning keeps ISAC throughput and sensing accuracy high even when telemetry arrives up to 100 ms late in 6G simulations.
citing papers explorer
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Enabling Real-Time AI in O-RAN: Deploying and Measuring AI Inside a Near-RT RIC xApp
Logistic regression and a shallow MLP compiled as deterministic C modules inside a FlexRIC xApp achieve 1–25 µs inference and sub-4 ms end-to-end service latency on an OAI testbed, meeting the 10 ms Near-RT budget for >95% of projected loops.
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Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks
Digital-twin belief-state reinforcement learning keeps ISAC throughput and sensing accuracy high even when telemetry arrives up to 100 ms late in 6G simulations.