Machine Learning-Aided Latency Prediction in Packet-Switched Xhaul Networks

In this work, we address the challenge of accurately predicting latency in packet-switched Xhaul networks, enabling the convergent transport of fronthaul (FH) and midhaul (MH) traffic within radio access networks (RANs). Although deterministic worst-case (WC) models provide strict latency bounds, they tend to significantly overestimate actual flow latencies, leading to inefficient resource allocation. To address this limitation, we propose a machine learning-based (ML) latency prediction framewo