Abstract
Due to the rise of new communication services, more portions of the electromagnetic spectrum must be relocated and their distribution optimized. With the digitization of the open TV service, it was observed that the distribution of channels in he frequency band destined for this service generated an inefficient use of the radio spectrum. These unused frequency bands are the so-called void spaces. To establish efficient spectrum use, it is important to identify these spectrum gap opportunities and use according to certain criteria. In this article, machine learning algorithms are proposed to identify new spectrum opportunities, through the signal levels received in the UHF frequency range of the Digital TV system. These spectrum opportunities are generated from natural or artificial obstacles present in the propagation environment. Two measurement campaigns were carried out in a suburban area to obtain the level of the signal received in an area of approximately 240,000 square meters. From the received power values, machine learning algorithms were used to make prediction of the received signal levels. By using a reception threshold, it is possible to identify the shadow regions and possible spectrum opportunities.
Highlights
D UE to the evolution of communication technology and the search for new communication services, the demand for wider frequency bands is progressively increasing
The power received on a 6 MHz channel of the digital television system, collected from 551 points, was used to train the machine learning algorithms mentioned in the previous section
A measurement campaign was carried out in a suburban area, where 551 measurements were taken to obtain power values received on the digital TV channels in the UHF band
Summary
D UE to the evolution of communication technology and the search for new communication services, the demand for wider frequency bands is progressively increasing. The frequency spectrum is a finite and scarce resource, which disables the rapid inclusion of new wireless communication services or the expansion of an existing service due to current spectrum allocation policies. The spectrum policy is statically defined for each type of service, i.e., a portion of the spectrum band is intended solely and. The authors thank CAPES Code 0001 for the financial support of the project. Several authors [1], [2] have carried out research on the use of the spectrum in the current model which is not efficient. The current scenario of spectrum usage needs a new efficient spectrum allocation paradigm
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