Aerospace and Electronic Systems Magazine May 2018 - 49

Cooklev et al.

Figure 3.

Power spectrum variation of the power in LTE channel.

The upper part in Figure 3 represents a spectrogram of the
power in the frequency-time domain, averaged over 1 minute for
all RBs. At the bottom of the figure is shown the average power
for the measurement period. It is observed that during the night,
the load in the cell decreases. At the center of the frequency
band, the subspace of the RBs in which the synchronization signals and physical broadcasts channels are transmitted is clearly
visible. Figure 4 illustrates the standard deviation of the power
variation.
We can also determine the level of activity and the load of the
cell through data mining of the collected RF data from the monitoring using Hjorth parameters as a robust feature descriptor, following the approach from [18]. The results in Figures 5 and 6 show the
activity-related estimation parameters "mobility" and "complexity" of the RBs. In the bottom parts of the figures, the results for the
entire measurement period are illustrated. The difference between
the load of the RBs during the night and the day and the service
RBs in the center could be seen from the figures. It is evident that
RBs with numbers 0-32 have a higher load than those in the upper part of the bandwidth. In addition to the channel occupancy
analysis, examples of the estimation of the efficiency of spectrum
utilization are given in [18]. Nevertheless, the answer to the question how the application of RF data analytics approaches can lead
to an improvement of the utilization of spectrum and in this sense,
MAY - JUNE 2018

spectrum efficiency, needs further analysis and will be a topic of
future research.

PREDICTIVE AND PRESCRIPTIVE DATA ANALYTICS
APPLICATIONS
Spectrum occupancy forecasting and interference identification are
good examples of RF predictive and prescriptive analytics. Predictive analytics relies on ML tools to develop models on the basis of
past data for the goal of predicting the future. An overview of the
emerging ML approaches as fundamental components of data analytics and some of the challenges in this field are presented in [19].
An example of spectrum occupancy forecasting using ML algorithms on RF data was presented in [20]. The data is obtained
from long-term spectrum monitoring at an airport traffic control
station in Bulgaria. It is shown that by applying proper ML-based
data analytics to the data obtained from long-term spectrum monitoring, results in adequate forecasting particularly for channels in
which the activity is more than 50% of the time.
Another example for both predictive and prescriptive analytics
could be illustrated by using the measurement results from [18].
There, we illustrate an approach for interference recognition on the
basis of the analysis of large amount of data obtained from long-

IEEE A&E SYSTEMS MAGAZINE

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