18 research outputs found
Bayesian approach for the spectrum sensing mimo-cognitive radio network with presence of the uncertainty
A cognitive radio technique has the ability to learn. This system not only can observe the surrounding environment, adapt to environmental conditions, but also efficiently use the radio spectrum. This technique allows the secondary users (SUs) to employ the primary users (PUs) spectrum during the band is not being utilized by the user. Cognitive radio has three main steps: sensing of the spectrum, deciding and acting. In the spectrum sensing technique, the channel occupancy is determined with a spectrum sensing approach to detect unused spectrum. In the decision process, sensing results are evaluated and the decision process is then obtained based on these results. In the final process which is called the acting process, the scholar determines how to adjust the parameters of transmission to achieve great performance for the cognitive radio network
Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework
Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal\u27s most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform\u27s samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal\u27s modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver\u27s equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier.
Advisor: Hamid R. Sharif-Kashan
Wideband Spectrum Sensing for Dynamic Spectrum Sharing
The proliferation of wireless devices grows exponentially, demanding more and more data
communication capacity over wireless links. Radio spectrum is a scarce resource, and traditional
wireless networks deployed by Mobile Network Operators (MNO) are based on an exclusive
spectrum band allocation. However, underutilization of some licensed bands in time and geographic
domains has been reported, especially in rural areas or areas away from high population density
zones. This coexistence of increasingly high data communication needs and spectrum
underutilization is an incomprehensible scenario. A more rational and efficient use of the spectrum
is the possibility of Licensed Users (known as Primary Users – PU) to lease the spectrum, when
not in use, to Unlicensed Users (known as Secondary Users – SU), or allowing the SU to
opportunistically use the spectrum after sensing and verifying that the PU is idle. In this latter
case, the SU must stop transmitting when the PU becomes active.
This thesis addresses the spectrum sensing task, which is essential to provide dynamic spectrum
sharing between PUs and SUs. We show that the Spectral Correlation Function (SCF) and the
Spectral Coherence Function (SCoF) can provide a robust signal detection algorithm by exploiting
the cyclostationary characteristics of the data communication signal. We enhance the most used
algorithm to compute de SCF - the FAM (FFT Accumulation Method) algorithm – to efficiently
compute the SCF in a local/zoomed region of the support ( ; ) plane (frequency/cycle frequency
plane). This will provide the quick identification of spectral bands in use by PUs or free, in a
wideband sampling scenario.
Further, the characterization of the probability density of the estimates of the SCF and SCoF
when only noise is present, using the FAM algorithm, will allow the definition of an adaptive
threshold to develop a blind (with respect to the noise statistics) Constant False Alarm Rate
(CFAR) detector (using the SCoF) and also a CFAR and a Constant Detection Rate (CDR)
detector when that characterization is used to obtain an estimate of the background noise variance
(using the SCF).A proliferação de dispositivos sem fios cresce de forma exponencial, exigindo cada vez mais
capacidade de comunicação de dados através de ligações sem fios. O espectro radioelétrico é um
recurso escasso, e as redes sem fios tradicionais implantadas pelos Operadores de Redes Móveis
baseiam-se numa atribuição exclusiva de bandas do espectro. No entanto, tem sido relatada a
subutilização de algumas bandas licenciadas quer ao longo do tempo, quer na sua localização
geográfica, especialmente em áreas rurais, e em áreas longe de zonas de elevada densidade
populacional. A coexistência da necessidade cada vez maior de comunicação de dados, e a
subutilização do espectro é um cenário incompreensível. Uma utilização mais racional e eficiente
do espectro pressupõe a possibilidade dos Utilizadores Licenciados (conhecidos como Utilizadores
Primários – Primary Users - PU) alugarem o espectro, quando este não está a ser utilizado, a
Utilizadores Não Licenciados (conhecidos como Utilizadores Secundários – Secondary Users - SU),
ou permitir ao SU utilizar oportunisticamente o espectro após a deteção e verificação de que o PU
está inativo. Neste último caso, o SU deverá parar de transmitir quando o PU ficar ativo.
Nesta tese é abordada a tarefa de deteção espectral, que é essencial para proporcionar a partilha
dinâmica do espectro entre PUs e SUs. Mostra-se que a Função de Correlação Espectral (Spectral
Correlation Function - SCF) e a Função de Coerência Espectral (Spectral Coherence Function -
SCoF) permitem o desenvolvimento de um algoritmo robusto de deteção de sinal, explorando as
características ciclo-estacionárias dos sinais de comunicação de dados. Propõe-se uma melhoria ao
algoritmo mais utilizado para cálculo da SCF – o método FAM (FFT Accumulation Method) -
para permitir o cálculo mais eficiente da SCF numa região local/ampliada do plano de suporte
/ (plano de frequência/frequência de ciclo). Esta melhoria permite a identificação rápida de
bandas espectrais em uso por PUs ou livres, num cenário de amostragem de banda larga.
Adicionalmente, é feita a caracterização da densidade de probabilidade das estimativas da SCF e
SCoF quando apenas o ruído está presente, o que permite a definição de um limiar adaptativo,
para desenvolver um detetor de Taxa de Falso Alarme Constante (Constant False Alarm Rate –
CFAR) sem conhecimento do ruído de fundo (usando a SCoF) e também um detetor CFAR e Taxa
de Deteção Constante (Constant Detection Rate – CDR), quando se utiliza aquela caracterização
para obter uma estimativa da variância do ruído de fundo (usando a SCF)
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports