Abstract
This paper proposes a deep reinforcement learning approach to Physical Resource Block (PRB) scheduling in 5G New Radio networks. A Gymnasium-compatible simulation environment is developed on the 3GPP TR 38.901 UMa NLOS channel model with Rayleigh fading, serving as a reproducible experimental platform. A Proximal Policy Optimization (PPO) agent is trained with a composite reward function that jointly optimizes aggregate throughput and Jain's Fairness Index. The agent is evaluated over 100 episodes with five independent random seeds against three classical baselines — Round Robin, Proportional Fair, and Maximum CQI. The PPO agent achieves a mean throughput of 57.09 ± 1.04 Mbps with a Jain's Fairness Index of 0.358, surpassing Round Robin (32.37 Mbps, JFI 0.630) and Proportional Fair (35.31 Mbps, JFI 0.597) in throughput while exceeding Maximum CQI in fairness (JFI 0.100). A throughput-fairness tradeoff analysis confirms that the proposed agent occupies an operating point inaccessible to any single classical method. The composite reward function, which weights both objectives equally, enables learned adaptive behavior that balances spectral efficiency and user equity. The code and evaluation pipeline are fully open-source and reproducible using the Stable-Baselines3 library.