TARMM uses a temporal graph to model RAN dynamics and MARL with action masking for proactive mobility management in 5G O-RAN, reducing tail latency by up to 44% and packet loss by up to 56% on a multi-cell testbed for VR workloads.
Qos-aware content deliv- ery in 5g-enabled edge computing: Learning-based ap- proaches.IEEE Transactions on Mobile Computing, 23(10):9324–9336
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TARMM: Scaling Delay-Critical Edge AI Offloading in 5G O-RAN via Temporal Graph Mobility Management
TARMM uses a temporal graph to model RAN dynamics and MARL with action masking for proactive mobility management in 5G O-RAN, reducing tail latency by up to 44% and packet loss by up to 56% on a multi-cell testbed for VR workloads.