Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
CoRR(2024)
Abstract
The introduction of fifth-generation (5G) radio technology has revolutionized
communications, bringing unprecedented automation, capacity, connectivity, and
ultra-fast, reliable communications. However, this technological leap comes
with a substantial increase in energy consumption, presenting a significant
challenge. To improve the energy efficiency of 5G networks, it is imperative to
develop sophisticated models that accurately reflect the influence of base
station (BS) attributes and operational conditions on energy usage.Importantly,
addressing the complexity and interdependencies of these diverse features is
particularly challenging, both in terms of data processing and model
architecture design.
This paper proposes a novel 5G base stations energy consumption modelling
method by learning from a real-world dataset used in the ITU 5G Base Station
Energy Consumption Modelling Challenge in which our model ranked second. Unlike
existing methods that omit the Base Station Identifier (BSID) information and
thus fail to capture the unique energy fingerprint in different base stations,
we incorporate the BSID into the input features and encoding it with an
embedding layer for precise representation. Additionally, we introduce a novel
masked training method alongside an attention mechanism to further boost the
model's generalization capabilities and accuracy. After evaluation, our method
demonstrates significant improvements over existing models, reducing Mean
Absolute Percentage Error (MAPE) from 12.75
gain of more than 60
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