Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
<|reference_start|>Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN: As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Utilizing artificial intelligence (AI) and machine learning (ML), O-RAN addresses the variable demands of modern networks with unprecedented efficiency and adaptability. Despite progress in using ML-based strategies for network optimization, challenges remain, particularly in the dynamic allocation of resources in unpredictable environments. This paper proposes a novel Meta Deep Reinforcement Learning (Meta-DRL) strategy, inspired by Model-Agnostic Meta-Learning (MAML), to advance resource block and downlink power allocation in O-RAN. Our approach leverages O-RAN's disaggregated architecture with virtual distributed units (DUs) and meta-DRL strategies, enabling adaptive and localized decision-making that significantly enhances network efficiency. By integrating meta-learning, our system quickly adapts to new network conditions, optimizing resource allocation in real-time. This results in a 19.8% improvement in network management performance over traditional methods, advancing the capabilities of next-generation wireless networks.<|reference_end|>
@article{lotfi2024meta,
    title={Meta Reinforcement Learning Approach for Adaptive Resource Optimization
  in O-RAN},
    author={Fatemeh Lotfi, Fatemeh Afghah},
    journal={arXiv preprint arXiv:2410.03737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03737},
    primaryClass={cs.NI cs.AI cs.LG cs.RO cs.SY eess.SY stat.ML}
}