  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.
