Federated Meta-Learning for Traffic Steering in O-RAN

• URL: http://arxiv.org/abs/2209.05874v1
• Date: Tue, 13 Sep 2022 10:39:41 GMT
• Title: Federated Meta-Learning for Traffic Steering in O-RAN
• Authors: Hakan Erdol, Xiaoyang Wang, Peizheng Li, Jonathan D. Thomas, Robert Piechocki, George Oikonomou, Rui Inacio, Abdelrahim Ahmad, Keith Briggs, Shipra Kapoor
• Abstract summary: We propose an algorithm for RAT allocation based on federated meta-learning (FML) We have designed a simulation environment which contains LTE and 5G NR service technologies.
• Score: 1.400970992993106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect quality of service (QoS) for users, compared to LTE networks. In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi. Each radio access technology (RAT) provides different types of access, and these should be allocated and managed optimally among the users. Besides resource management, 5G systems will also support a dual connectivity service. The orchestration of the network therefore becomes a more difficult problem for system managers with respect to legacy access technologies. In this paper, we propose an algorithm for RAT allocation based on federated meta-learning (FML), which enables RAN intelligent controllers (RICs) to adapt more quickly to dynamically changing environments. We have designed a simulation environment which contains LTE and 5G NR service technologies. In the simulation, our objective is to fulfil UE demands within the deadline of transmission to provide higher QoS values. We compared our proposed algorithm with a single RL agent, the Reptile algorithm and a rule-based heuristic method. Simulation results show that the proposed FML method achieves higher caching rates at first deployment round 21% and 12% respectively. Moreover, proposed approach adapts to new tasks and environments most quickly amongst the compared methods.

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