Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN

• URL: http://arxiv.org/abs/2410.03737v1
• Date: Mon, 30 Sep 2024 23:04:30 GMT
• Title: Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
• Authors: Fatemeh Lotfi, Fatemeh Afghah,
• Abstract summary: Open Radio Access Network (O-RAN) addresses the variable demands of modern networks with unprecedented efficiency and adaptability. 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.
• Score: 6.326120268549892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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