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.


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    Title :

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




    Publication date :

    2023-10-10


    Size :

    1442562 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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