Federated Meta-Learning for Traffic Steering in O-RAN

TL;DR

This paper introduces a federated meta-learning algorithm for dynamic radio access technology allocation in 5G networks, improving adaptability and QoS in complex, changing environments.

Contribution

The paper presents a novel federated meta-learning approach for RAT allocation in 5G, enabling faster adaptation and better resource management compared to existing methods.

Findings

FML achieves 21% higher caching rate at first deployment.

FML adapts more quickly to new tasks and environments.

Proposed method outperforms single RL, Reptile, and heuristic approaches.

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.