Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL

Abstract

With 5G technology evolving, Open Radio Access Network (O-RAN) solutions are becoming crucial, especially for handling the diverse Quality of Service (QoS) needs in vehicular networks. These networks are dynamic and have many different applications, calling for effective O-RAN strategies. This paper examines a three-tier hierarchical O-RAN slicing model, created to address the unique challenges of vehicular networks. The top-level follow 3GPP standards like ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). The middle level is organized by vehicle types, and the lowest level is designed for specific vehicle applications. This approach leads to better network resource management. Additionally, this study explores the advantages of a federated deep reinforcement learning (DRL) approach for efficient learning while maintaining privacy. It introduces a federated DRL approach incorporating federated averaging and deep deterministic policy gradient (DDPG) techniques, to enhance inter-slice operations and resource allocation in vehicular networks. Lastly, the effectiveness of this algorithm is demonstrated through a small simulation in a vehicular framework.