Aerospace and Electronic Systems Magazine May 2018 - 47

Cooklev et al.

Figure 1.

Cloud-based platform for RF data analytics.

access is granted to different users and applications. The platform
is collaborative, allowing RF data owners to store, share, and analyze data and data scientists to test the performance of different algorithms. Virtualization ensures that the users of the platform share
computing resources, but use the platform in isolation from one
another. Furthermore, the platform allows a video of the RF data to
be recorded, making an interesting connection between the fields
of video monitoring and RF spectrum monitoring.
In addition, this platform allows more complex services and
applications to be enhanced and offered, such as RF data analytics:
analysis to uncover what is buried in the data by mining massive
data sets of RF data at different resolution levels. Note that the
process of mining RF data that we consider generally excludes demodulation. Note also that RF time-domain or spectrum data, defined as data paired with any type of metadata, are semistructured
and well suited to be analyzed by big data techniques. There are
two main paradigms for this analysis. One paradigm, provided by
MapReduce [16] is batch processing, in which data are first stored
and then analyzed. Streaming is another paradigm that treats the
data as a stream of unknown size, and for practical reasons only,
a limited portion of this stream is stored. Although, in principle,
streaming is faster, most applications use the batch-processing paradigm even for real-time data, and Hadoop and MapReduce have
become the de facto standards for big data platforms. Note that
in most cases, Hadoop and MapReduce are run on top of public
clouds and is much more cost-effective than using a private cloud.
We consider the general data level classification [2] to be applied also to RF data analytics. Descriptive analytics, such as the
number of operating sessions, activity and occupancy, and duration, are the first example. Beyond descriptive analytics, we envision predictive and prescriptive analytics. The purpose of predictive analytics is to predict near-term or long-term spectrum
MAY - JUNE 2018

usage or variations or both in the power or frequency of wanted
and unwanted signals. Finally, prescriptive RF data analytics allow
optimization of spectrum usage and interference avoidance. Ultimately, these RF data analytics services help devices, networks,
and organizations obtain insight into the RF spectrum and its use
to make decisions and, ultimately, to gain a competitive advantage
and also to assist decision making by devices, such as cognitive
radios, and by entities, such as spectrum regulators and spectrum
licensees.

LONG-TERM SPECTRUM MONITORING AS A RF DATA
ANALYTICS SERVICE
Long-term spectrum monitoring is a service that is a very good
candidate to be offered by RF clouds not only to take advantage
of benefits, such as elasticity and ability to scale, but also to enable RF data analytics services and applications that are simply
otherwise not possible. We have used the platform shown in Figure 1 to offer descriptive, predictive, and prescriptive RF data
analytics.

DESCRIPTIVE DATA ANALYTICS APPLICATION EXAMPLES
An example of a descriptive RF data analytics service and application is spectrum monitoring for operation estimation. In this
case, the purpose of the monitoring is to start with information
about the systems known to be operational and provide feedback
to spectrum management, i.e., close the loop, by confirming that
devices operate as authorized or provide evidence that they do not.
The platform architecture discussed here can provide this monitoring service in a more efficient way than currently deployed
systems. For a long time, spectrum usage had been constant along

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