Seasonal variation of inter-hemispheric field-aligned currents deduced from time-series analysis of the equatorial geomagnetic field data during solar cycle 23–24

AbstractThe east–west component of magnetic field variation (∆D-component) at Davao station (Philippines, geomagnetic latitude: – 2.22˚N) are used to investigate the characteristics of the long-term Inter-Hemispheric Field-Aligned Currents (IHFACs) based on the time-series analysis from August 1998 to July 2020. Recent in situ satellite and ground-based observations have reported that dusk-side current polarity of IHFAC is often opposite to that of the noon IHFAC, being inconsistent with Fukushima's IHFACs model. We investigated the consistency of the dusk-side IHFAC polarity derived from the observations with the polarity expected from Fukushima’s IHFACs model and examined the solar cycle dependence of IHFACs. It was confirmed that the dusk-side IHFACs during June and December solstices flow in the same direction of the noontime IHFACs, which was consistent with the IHFAC polarities suggested by the Fukushima model. The dusk-side IHFACs around March and September–November months disagreed with the Fukushima model. The ∆D variations clearly showed seasonal asymmetry in the dawn and noon sectors, whereas the ∆D variations in the dusk sector demonstrated seasonal symmetry. Solar cycle dependence of IHFACs was exhibited in the dusk sector. For the dawn and noon sectors, the yearly peak-to-peak ∆D amplitude in the later solar cycle SC24 decreased by about 35% in comparison with the earlier solar cycle SC23. In contrast, the dusk-side yearly peak-to-peak ∆D amplitude increased by about 200%. The dusk-side IHFAC yearly amplitude tended to be in inverse proportion to solar activity.

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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