A Network Latency Prediction Method Based on Sequence Decomposition and GNN

Ultra reliable low latency communication (URLLC) has emerged as a crucial element in various communication services due to its ability to provide highly dependable and real-time connections. However, existing time series models utilized in URLLC scenarios often overlook the interconnections among delay cycles, trends, and bursts. Therefore, they fail to effectively capture the temporal periodic statistical characteristics exhibited by real data. To address this limitation, we propose a network delay prediction method that utilizes graph neural networks to aggregate different characteristic trends. Specifically, we employ channels with independently learnable weights of varying magnitudes to capture the inherent periodic patterns of cycles, trends, and short fluctuations. To obtain a generalized feature representation from the combined features, we map them into a non-Euclidean space and leverage mature graph convolution techniques to aggregate structural features. Results on real world URLLC datasets show that our approach can reduce the MSE error by up to 50.1% compared to current methods.

I. Introduction

With the rapid advancement of urbanization, emerging services and applications demand low latency and high reliability. Consequently, Ultra-Reliable and Low Latency Communications (URLLC) have garnered increasing significance, particularly in the context of 5G networks [1]. URLLC spans various domains in daily life and production, profoundly impacting people’s routines, including applications such as the Internet of Things (IoT) and Internet of Vehicles (IoV) [2]. These applications share a common trait: a critical sensitivity to network latency.