Compressing network data with deep learning
This thesis delves into the problem of compressing data generated by mobile networks, which are formed by many antennas and IoT devices, that emit huge quantities of measurements. Storing this data is growing more difficult and expensive, a trend driven by continuously escalating data transmission s...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/406468 |
| Acceso en línea: | https://hdl.handle.net/2117/406468 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Data compression (Computer science) compressió amb aprenentatge profund codificació entròpica transformadors dades de xarxes mòbils deep learning compression entropy coding transformers mobile network data Aprenentatge profund Dades -- Compressió (Informàtica) Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | This thesis delves into the problem of compressing data generated by mobile networks, which are formed by many antennas and IoT devices, that emit huge quantities of measurements. Storing this data is growing more difficult and expensive, a trend driven by continuously escalating data transmission speeds, the sheer volume of traffic, and the increasing number of devices. To solve this problem, we surveyed some compression algorithms, both classical and learning-based, with a particular focus on deep-learning predictors applied in conjunction with entropy encoders. The core of our research is centered around compression algorithms based on Transformers and Arithmetic encoders. We propose several improvements like pretraining and using different models for each column, which outperform current approaches for our particular data domain. The focus of this research will be trying to improve compression on a large-scale dataset provided by Telefonica, which contains heterogeneous measurements from a huge network of antennas. Additionally, we provide a comprehensive analysis of the outcomes obtained, giving an intuition about why deep learning approaches work better than the current baselines, albeit at a higher computational cost, and explaining why our domain-specific solution slightly outperforms the rest. |
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