In ns-3 simulations, MAPPO with greedy allocation lowers average end-to-end latency and raises latency-success probability versus round-robin for teleoperated driving.
C-V2X Use Cases V olume II: Examples and Service Level Requirements,
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Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving
In ns-3 simulations, MAPPO with greedy allocation lowers average end-to-end latency and raises latency-success probability versus round-robin for teleoperated driving.