Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
Real: Reinforcement learning-enabled xapps for experimental closed-loop optimization in o-ran with osc ric and srsran
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DDQN RL with transfer learning xApp for O-RAN reduces UAV handover frequency by up to 54.6% versus greedy baselines while keeping outage probability negligible.
MORPH fuses iPerf measurements on OpenAirInterface, MCS-conditioned theoretical throughput, and 3GPP PHY simulation to train RL agents that achieve more robust slice performance and SLA compliance than single-source training for PRB-level spectrum allocation in a single gNB.
citing papers explorer
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
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xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
DDQN RL with transfer learning xApp for O-RAN reduces UAV handover frequency by up to 54.6% versus greedy baselines while keeping outage probability negligible.
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MORPH: Multi-Environment Orchestrated Reinforcement Learning for PRB Handling in O-RAN
MORPH fuses iPerf measurements on OpenAirInterface, MCS-conditioned theoretical throughput, and 3GPP PHY simulation to train RL agents that achieve more robust slice performance and SLA compliance than single-source training for PRB-level spectrum allocation in a single gNB.