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% to 4.98%, leading to a performance
gain of more than 60%.

该研究提出了一种基于实际数据集的新颖的 5G 基站能耗建模方法，通过将基站标识符（BSID）信息纳入输入特征并使用嵌入层进行编码，以准确表示不同基站的能源指纹，并引入了掩码训练方法和注意机制来提高模型的泛化能力和准确性，在评估中实现了显著改进，将平均绝对百分比误差（MAPE）从 12.75％降低到 4.98％，性能提升超过 60％。

使用真实世界数据建模 5G 能耗：能量指纹即所需

Modelling the 5G Energy Consumption using Real-world Data: Energy  Fingerprint is All You Need

Surveys show that the mean absolute percentage error (MAPE) is the most
widely used measure of forecast accuracy in businesses and organizations. It is
however, biased: When used to select among competing prediction methods it
systematically selects those whose predictions are too low. This is not widely
discussed and so is not generally known among practitioners. We explain why
this happens. We investigate an alternative relative accuracy measure which
avoids this bias: the log of the accuracy ratio: log (prediction / actual).
Relative accuracy is particularly relevant if the scatter in the data grows as
the value of the variable grows (heteroscedasticity). We demonstrate using
simulations that for heteroscedastic data (modelled by a multiplicative error
factor) the proposed metric is far superior to MAPE for model selection.
Another use for accuracy measures is in fitting parameters to prediction
models. Minimum MAPE models do not predict a simple statistic and so
theoretical analysis is limited. We prove that when the proposed metric is used
instead, the resulting least squares regression model predicts the geometric
mean. This important property allows its theoretical properties to be
understood.

该论文探讨了预测准确性测量中最广泛使用的均方误差绝对百分比（MAPE）存在的偏差，并提出了使用相对精度度量时的优越性，特别是在存在异方差性的数据中。该方法可避免 MAPE 的偏见，并可用于模型选择和参数拟合。