A PPO deep RL agent learns to optimize cell offsets in a Python-simulated 5G environment, improving throughput, fairness, latency, jitter, packet loss, and handover counts over rule-based and other learning baselines under mobility and uncertainty.
Action Candidate Driven Clipped Double Q-Learning for Discrete and Continuous Action Tasks
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Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty
A PPO deep RL agent learns to optimize cell offsets in a Python-simulated 5G environment, improving throughput, fairness, latency, jitter, packet loss, and handover counts over rule-based and other learning baselines under mobility and uncertainty.