NetMOS: Topology-aware VoIP MOS prediction via attention-recurrent GNNs
Author Identifier (ORCID)
Abstract
The Mean Opinion Score (MOS) is a standard metric for assessing the Quality of Experience (QoE) in Voice over IP (VoIP) applications. Accurate prediction of how network conditions influence MOS is critical for network planning, operation, and optimization. This requires modeling traffic flows with application-level granularity, which significantly increases both the dimensionality and structural complexity of the learning task. The ability to achieve efficient, robust, and generalizable data-driven learning in the presence of such complexity depends critically on the careful design of model architectures. This paper presents NetMOS, a Graph Neural Network (GNN) architecture specifically crafted to model IP networks and predict VoIP MOS scores. NetMOS models IP networks as heterogeneous graphs and designs a two-stage Message Passing Neural Network (MPNN) to capture both permutation invariant and sequential dependencies in traffic flow and network interactions. It uses a Gated Recurrent Unit (GRU) layer to model the ordered influence of links along a traffic path and introduces a customized attention layer with Sigmoid activations to model the cumulative effects of multiple flows on the links. Simulations demonstrate that NetMOS consistently outperforms conventional GNN-based baselines across diverse network topologies in Mean Absolute Error (MAE), R2 score, Pearson correlation, and Spearman correlation. NetMOS generalizes effectively beyond the training topology, maintaining high prediction accuracy on unseen network topologies and varying network activity durations without retraining. NetMOS also provides MOS predictions 44× – 170× faster than packet-level simulations.
Keywords
graph neural networks, mean opinion score, network modeling, quality of experience, VoIP
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
23
Publication Title
IEEE Transactions on Network and Service Management
Publisher
IEEE
School
School of Engineering
Copyright
subscription content
Content Type
Metadata only
First Page
5280
Last Page
5292
Recommended Citation
Ranaweera, S., He, Y., Jayawickrama, B., Wang, X., Liu, R. P., & Ni, W. (2026). NetMOS: Topology-aware VoIP MOS prediction via attention-recurrent GNNs. IEEE Transactions on Network and Service Management, 23, 5280–5292. https://doi.org/10.1109/TNSM.2026.3701762