A two-stage stochastic optimization approach using Sample Average Approximation for adaptive reinstantiation of RAN functions in disaggregated mobile networks to maintain service under cascading failures and uncertainty.
Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
DQN-based agent jointly detects three-class cell outages and compensates via power and tilt adjustments, reporting 99.1% coverage and 54% full recovery in simulations.
citing papers explorer
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Resilience under Uncertainty: Securing 6G through Stochastic Reinstantiation of RAN Functions
A two-stage stochastic optimization approach using Sample Average Approximation for adaptive reinstantiation of RAN functions in disaggregated mobile networks to maintain service under cascading failures and uncertainty.
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Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning
DQN-based agent jointly detects three-class cell outages and compensates via power and tilt adjustments, reporting 99.1% coverage and 54% full recovery in simulations.