Generative QoE Modeling: A Lightweight Approach for Telecom Networks

• URL: http://arxiv.org/abs/2504.21353v1
• Date: Wed, 30 Apr 2025 06:19:37 GMT
• Title: Generative QoE Modeling: A Lightweight Approach for Telecom Networks
• Authors: Vinti Nayar, Kanica Sachdev, Brejesh Lall,
• Abstract summary: This study introduces a lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy.<n>By validating the use of Vector Quantization (VQ) as a preprocessing technique, continuous network features are effectively transformed into discrete categorical symbols.<n>This VQ-HMM pipeline enhances the model's capacity to capture dynamic QoE patterns while supporting probabilistic inference on new and unseen data.
• Score: 6.473372512447993
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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