Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo
Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licen...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/15452 |
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| Resumo: | Modern wireless systems employ adaptive techniques to provide high throughput
while observing desired coverage, Quality of Service (QoS) and capacity. An alternative
to further enhance data rate is to apply cognitive radio concepts, where a system is
able to exploit unused spectrum on existing licensed bands by sensing the spectrum and
opportunistically access unused portions. Techniques like Automatic Modulation Classification
(AMC) could help or be vital for such scenarios. Usually, AMC implementations
rely on some form of signal pre-processing, which may introduce a high computational
cost or make assumptions about the received signal which may not hold (e.g. Gaussianity
of noise). This work proposes a new method to perform AMC which uses a similarity measure
from the Information Theoretic Learning (ITL) framework, known as correntropy
coefficient. It is capable of extracting similarity measurements over a pair of random processes
using higher order statistics, yielding in better similarity estimations than by using
e.g. correlation coefficient. Experiments carried out by means of computer simulation
show that the technique proposed in this paper presents a high rate success in classification
of digital modulation, even in the presence of additive white gaussian noise (AWGN) |
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