5G SimulationExpert
Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning
Isaac-Net is a preprint describing a GPU-batched 5G New Radio network simulator that runs in lockstep with Isaac Lab physics, enabling thousands of parallel robot-learning environments to share realistic uplink contention. It reproduces ns-3 5G-LENA median delay within roughly 5 to 10 percent, low on an unseen carrier and high at 32 robots per environment in closed loop, and captures Age of Information better than an independent per-message delay. This matters for training multi-robot policies that need to learn network-aware coordination on private 5G fleets.