Enhancing Energy Efficiency in BBUs: Predictive Analysis with Long-Term and Granular Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU152695" target="_blank" >RIV/00216305:26220/24:PU152695 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Energy Efficiency in BBUs: Predictive Analysis with Long-Term and Granular Data

Popis výsledku v původním jazyce

This paper investigates optimizing Baseband Unit (BBU) energy consumption through predictive modeling using both long-term and granular datasets. We evaluate various machine learning models, including a hyperparameter-optimized Convolutional Neural Networks- Long Short-Term Memory (CNN-LSTM) architecture, to predict traffic volume, user equipment (UE) downlink latency, and BBU power consumption. Our results show that while granular data offered detailed insights, the long-term dataset consistently provided better results for traffic volume prediction. Despite this, we focused on the granular dataset for its ability to capture short-term fluctuations, which is crucial for real-time network management. The CNN-LSTM with hyperparameter optimization demonstrated superior accuracy compared to baseline models such as LSTM and GRU, especially for predicting downlink latency and power consumption. The combined use of long-term and granular datasets added complexity without significantly improving predictive a

Název v anglickém jazyce

Enhancing Energy Efficiency in BBUs: Predictive Analysis with Long-Term and Granular Data

Popis výsledku anglicky

This paper investigates optimizing Baseband Unit (BBU) energy consumption through predictive modeling using both long-term and granular datasets. We evaluate various machine learning models, including a hyperparameter-optimized Convolutional Neural Networks- Long Short-Term Memory (CNN-LSTM) architecture, to predict traffic volume, user equipment (UE) downlink latency, and BBU power consumption. Our results show that while granular data offered detailed insights, the long-term dataset consistently provided better results for traffic volume prediction. Despite this, we focused on the granular dataset for its ability to capture short-term fluctuations, which is crucial for real-time network management. The CNN-LSTM with hyperparameter optimization demonstrated superior accuracy compared to baseline models such as LSTM and GRU, especially for predicting downlink latency and power consumption. The combined use of long-term and granular datasets added complexity without significantly improving predictive a

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/FW10010014" target="_blank" >FW10010014: Nová automatizace procesů řízená umělou inteligencí pro zjednodušení a zlepšení telekomunikačních procesů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů