TG-DIN: Theory-Guided Demand Inference Network for Generalizable QoS Measurement and Prediction

TL;DR

TG-DIN is a novel, interpretable demand inference network that uses a theory-guided approach to improve QoS measurement and prediction across diverse network conditions.

Contribution

It introduces a theory-guided demand inference model that enhances interpretability and robustness in QoS prediction without requiring labeled demand data.

Findings

TG-DIN outperforms data-driven baselines under distribution shifts.

It generalizes well across different network capacities and traffic patterns.

Successfully applied to real packet traces with synthetic training.

Abstract

In this paper, we introduce TG-DIN, a theory-guided demand inference network that infers latent user demand from observable network quality-of-service (QoS) measurements. Rather than directly predicting QoS outcomes using black-box models, TG-DIN explicitly models latent demand as an intermediate variable and links it to observable behavior through a differentiable theory layer grounded in scheduling and queuing principles. This design yields an interpretable, mechanism-consistent representation of user demand that is directly applicable to downstream tasks such as congestion diagnosis, resource allocation, capacity planning, and policy evaluation. The theory layer further enables a principled randomized training regime that exposes the model to diverse yet physically meaningful operating conditions without requiring labeled demand data. Extensive synthetic experiments show that TG-DIN generalizes robustly across capacities, demand levels, and traffic patterns, substantially outperforming purely data-driven baselines under distribution shift. Moreover, when trained exclusively on synthetic data and applied directly to real packet traces, TG-DIN accurately recovers per-user allocation structure in shared-link scenarios. Together, these results demonstrate the effectiveness of theory-guided inductive biases for achieving transferable, deployment-ready inference in dynamic network environments.