کنفرانس مهندسی برق ایران

صفحه اصلی / سی و دومین کنفرانس بین المللی مهندسی برق

Distributed Deep Reinforcement Learning for Radio Resource Management in O-RAN

نویسندگان :

Ahmad Ahmadi Siahpoush¹ Vahid Shah-Mansouri²

1- دانشگاه تهران 2- دانشگاه تهران

کلمات کلیدی :

Open Radio Access Network (O-RAN)،Radio Resource Management،Deep Reinforcement Learning (DRL)،Deep Q-Network (DQN)

چکیده :

The open radio access network (O-RAN) has been developed with the purpose of enabling intelligence and openness in next generation cellular networks. It is based on disaggregated, virtualized, and software-based components which provides an open environment for network vendors and operators. O-RAN offers standardized interfaces and the ability to host network applications from third-party vendors through x-applications (xApps), which enables higher flexibility for network management utilizing artificial intelligence (AI) and machine learning (ML) techniques. The resource management in O-RAN is performed by xApps implemented in the near-real-time RAN intelligent controller (Near-RT RIC). In this Paper, xApps are modeled as deep reinforcement learning (DRL) agents that perform optimal radio resource management through interaction with each other and with the O-RAN environment. In particular, each xApp is considered to be a deep Q-network (DQN) agent that is responsible for the joint optimization of power and radio resource allocation. We also propose a distributed radio resource management algorithm where multiple xApps collaborate in a distributed manner, aiming to minimize interference between network users and enabling them to reach their maximum data transmission capacity. Our experimental results show the superior performance of the proposed algorithm for the distributed management of radio resources compared to a decentralized approach, while also achieving comparable performance with a centralized approach.