Meta Learner-Based Transfer Learning: Bridging Simulation and Actual Router Metrics

Future carrier networks for 6G are expected to guarantee the performance across heterogeneous networks that integrate multiple technologies. This integration requires the effective verification of network performance under unpredictable conditions. To address this, the network digital replica (NDR) has been proposed to evaluate the performance of actual network equipment digitally. However, a major challenge of the NDR is the difficulty and expense of acquiring extensive real-world datasets for various traffic patterns, which is necessary for training actual network node models. This paper proposes a meta learner-based transfer learning to infer actual router metrics based on the model using network simulation data. The proposed method aims to build a model of actual router metrics based on limited real-world datasets by supplementing it with different ranges and types of simulation data. To address the differences in these datasets, the proposed method utilizes Neural Processes as a meta-learner combined with Partial Least Squares analysis to capture and bridge the representation for packet multiplexing tasks between simulation and real-world datasets for transfer learning. The results show that the proposed method improves the inference accuracy of actual router metrics; throughput, packet loss rate, and packet delay.