Generative QoE Modeling: A Lightweight Approach for Telecom Networks

TL;DR

A lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy in real-time and resource-constrained environments, particularly in scenarios with limited computational resources or where latency constraints are critical.

Abstract

Quality of Experience (QoE) prediction plays a crucial role in optimizing resource management and enhancing user satisfaction across both telecommunication and OTT services. While recent advances predominantly rely on deep learning models, this study introduces a lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy. By validating the use of Vector Quantization (VQ) as a preprocessing technique, continuous network features are effectively transformed into discrete categorical symbols, enabling integration with a Hidden Markov Model (HMM) for temporal sequence modeling. This VQ-HMM pipeline enhances the model's capacity to capture dynamic QoE patterns while supporting probabilistic inference on new and unseen data. Experimental results on publicly available time-series datasets incorporating both objective indicators and subjective QoE scores demonstrate the viability of this approach in real-time and resource-constrained environments, where inference latency is also critical. The framework offers a scalable alternative to complex deep learning methods, particularly in scenarios with limited computational resources or where latency constraints are critical.

Figures (5)

• Figure 1: QoE stack
• Figure 2: Transformation and Modeling pipeline
• Figure 3: Comparative analysis of models in terms of accuracy and latency
• Figure 4: Precision-Recall Analysis
• Figure 5: Experimental Results in time domain