Deep Reinforcement Learning-Based Resource Management for 5G Networks: Optimizing eMBB Throughput and URLLC Latency

The rapid progression of 5G networks has ushered in a new era of communication, marked by challenges in optimizing enhanced Mobile Broadband (eMBB) throughput and Ultra-Reliable Low Latency Communications (URLLC) latency. This research delves into Deep Reinforcement Learning (DRL) to address these challenges, with a particular emphasis on the Proximal Policy Optimization (PPO) algorithm. By leveraging a judiciously crafted environment and reward structure, our DRL agents were trained to optimize eMBB throughput and URLLC latency concurrently. Using the Colosseum O-RAN COMMAG Dataset, our agents achieved an average eMBB throughput of 0.4451 Gbps and an average URLLC latency of 0.4719 ms. These outcomes highlight the potency of DRL as a tool for 5G optimization, presenting a promising avenue for future advancements in intelligent 5G network management.