Generative QoE Modeling: A Lightweight Approach for Telecom Networks
This work addresses QoE prediction for telecom and OTT services, offering a scalable alternative to deep learning in resource-limited scenarios, but it is incremental as it builds on existing methods like VQ and HMM.
The study tackled the problem of predicting Quality of Experience (QoE) in telecom networks by introducing a lightweight generative modeling framework, which achieved viability in real-time and resource-constrained environments by balancing computational efficiency, interpretability, and predictive accuracy.
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.