Algorithms for wireless communication systems using SDR platform

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.This thesis presents a detailed study on software based channel emulators and a set of algorithms pertaining to the soft emulator. With the fact that several wireless communications technologies were released in the last decades, there are a lot of challenging issues emerging due to the need for faster and more reliable technologies. From these challenging issues, we have chosen to focus our research on two outstanding challenges: real-time software channel emulator and automatic modulation classification. Recently, there has been an increase in the demand for a reliable and low-cost channel emulator to study the effects of real wireless channels. Hence, in the first part of the thesis, wediscussanimplementationofareal-timesoftwarechannelemulator. Thereal-time fading channel emulator was implemented by using a software defined radio platform. In order to verify the model, the frequency spectrum specifications of the channel generated was checked with a double tone transmitter. Then as a second step of verification, bit error rate (BER) of a real-time Orthogonal Frequency Division Multiplexing system using the Universal Software Radio Peripheral (USRP) and LABVIEW software was compared with the BER floor calculated from the theoretical equations. It has been shown that the developed channel emulator can indeed emulate a fading wireless channel. In the second part of the thesis we focused on covering an issue related to blind estimation or classification of a parameter in wireless communications at the receiver. This problem appears in cognitive radios and some defense applications where the receivers needs to know the type of the modulation of an incoming signal. The efficient automatic modulation classification scheme proposed in this study can be utilized for a group of digitally modulated signals such as QPSK, 16-PSK, 64-PSK, 4-QAM, 16-QAM, and 64QAM. We performed the classification in two stages: first we classified the modulation between QAM and PSK signaling, and then we determined the M-ary order of the modulation by developing Kernel Density Estimation and analyzing the probability density distribution for the real and imaginary parts of the modulated signals. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. Thus, in this thesis first of all we were able to develop a software based channel emulator. The developed channel emulator can be a very useful tool for other researchers in testing their real-time systems on a verified Doppler channel. Moreover, the emulator can find other applications from education to wireless device developments due to its flexibility. On the other hand, with the automatic modulation classification, the unknown modulation of an incoming signal can be determined. Hence, the two issues can be combined to find applications in cognitive radio developments.Abstract iii Öz v Acknowledgments viii List of Figures xi Abbreviations xiii 1 Introduction and Literature Review 1 1.1 Channel Emulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Automatic Modulation Classification . . . . . . . . . . . . . . . . . . . . . 4 1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Real Time Fading Channel Emulator using SDR 8 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Implementation of fading channels . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Implementation of Multipath Doppler Channel . . . . . . . . . . . 13 2.2.2 Specifications of the OFDM system used in verification . . . . . . 14 2.3 Theoretical BER curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.1 First verification phase . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.2 Second verification phase . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.3 Multipath channel simulation results . . . . . . . . . . . . . . . . . 21 2.4.4 Sources of error and mismatch . . . . . . . . . . . . . . . . . . . . 22 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3 Automatic Modulation Classification based on Kernel Density Estimation 25 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Signal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.3 KDE for the Modulation estimation . . . . . . . . . . . . . . . . . 28 3.2.4 Filtering to improve modulation estimation . . . . . . . . . . . . . 29 3.2.5 AMC proposed flow diagram . . . . . . . . . . . . . . . . . . . . . 31 3.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Choosing parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Conclusion and Future Work 40 4.1 Channel emulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 Automatic Modulation Classification . . . . . . . . . . . . . . . . . . . . . 41 4.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 A Proof for equation 2.4 used to calculate the BER for a given fading channel with certain fD 43 B LABVIEW diagram used to generate the curves in Figure 2.14 46 Bibliography 4

Classificação automática de modulações digitais usando histogramas e máquinas de vetores de suporte

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2014A rápida expansão de comunicações sem fio tornaram o espectro de rádio frequência um recurso bastante escasso. O rádio cognitivo surgiu como uma solução viável para contornar essa dificuldade. No contexto de rádio cognitivo, o monitoramento de espectro (Spectrum Sensing (SS)) desempenha um papel fundamental. Em particular, a Classificação Automática de Modulações (Automatic Modulation Classification (AMC)) é um dos blocos computacionais que mais demanda recursos dentro do SS. Atualmente, as pesquisas em AMC concentram-se no desenvolvimento de métodos rápidos e precisos que sejam capazes de atingir o throughput exigido por aplicações contemporâneas. Alguns trabalhos recentes tem proposto o uso de histogramas do sinal de entrada como parâmetros de classificação e Máquinas de Vetores de Suporte (Support Vector Machine (SVM)) como técnica de classificação. O presente trabalho apresenta uma avaliação de tal abordagem e busca projetar arquiteturas de hardware dedicadas eficientes para AMC. Na primeira parte deste trabalho, soluções de hardware para classificação de múltiplas classes usando SVM são projetadas e avaliadas. O esquema "um contra todos melhorado" proposto apresentou uma taxa de acertos até 162% maior do que o esquema "um contra um" comumente utilizado, sendo 30;2% mais eficiente em termos de energia consumida para classificação. Na segunda parte do trabalho, o uso de histogramas em conjunto com SVM é avaliado como uma técnica de classificação cega. Isso é realizado através de experimentos que assumem que o receptor não conhece a taxa de dados do transmissor, em oposição aos trabalhos correlatos. Como outra novidade, histogramas bi-dimensionais são propostos e a taxa de acertos resultante é comparada com aquela obtida para histogramas uni-dimensionais. Os experimentos mostraram que os histogramas bi-dimensionais fornecem uma taxa de acertos ligeiramente maior a um custo de hardware equivalente. Para a classificação baseada em histogramas bi-dimensionais, os seguintes parâmetros foram avaliados: tamanho do conjunto de treino, robustez ao ruído e relação entre número de amostras e taxa de acertos. Para as condições assumidas nos experimentos conduzidos, um tamanho do conjunto de treino de 256 amostras forneceram a melhor relação entre a taxa de acertos e recursos de hardware. Em relação à robustez ao ruído, os experimentos mostraram que a taxa de acertos global é melhor adaquando as SVMs são treinadas para toda a faixa de relação sinal-ruído na qual atuarão, ao invés de treinadas para um valor específico de relação sinal-ruído. Finalmente, percebeu-se que a taxa de acertos aumenta drasticamente quando o número de amostras para classificação é maior.Abstract: The fast expansion of wireless communications made the radio frequency spectrum a quite scarce resource. Cognitive radio emerges as a viable solution to circumvent this difficulty. In the context of cognitive radio, Spectrum Sensing (SS) plays a fundamental role. In particular, Automatic Modulation Classification (AMC) is one of the most computational demanding blocks within SS. Current research in AMC focuses on developing fast and accurate methods that are able to achieve the throughput required by contemporary applications. Some recent works have proposed the use of histrograms of the input signal as classification parameters, and Support Vector Machine (SVM) as a classification technique. This work presents an evaluation of such approach looking for designing efficient dedicated hardware architectures for AMC. In the first part of the work, hardware solutions for classifying multiple classes using SVM are designed and evaluated. The proposed "enhancedone against all" scheme presented up to 162% higher accuracy than the commonly used "one against one" scheme, while consuming 30:2% less energy per classification. In the second part of the work, the use of histograms along with SVM is evaluated as a blind classification technique. This was accomplished by conducting experiments assuming that the receiver does not know the transmitted baud rate, in opposition to the correlated work. As another novelty, two-dimensional histograms are proposed and the resulting accuracy is compared to that obtained from one-dimensional histograms. The experiments showed that two-dimensional histograms provide slightly higher accuracy at equivalent hardware costs. The following parameters were evaluated for two-dimensional histogram-based classification: training set size, robustness to noise and relation between number of samples and hit rate. For the conditions assumed in the conducted experiments, a training set size of 256 samples provided the best trade-off between accuracy and hardware resources. Concerning the robustness to noise, the experiments showed that the global accuracy is improved when SVMs are trained for the whole signal-to noise range that they are supposed to operate, instead of training to a specific signal-to-noise value. Finally, it has been noticed that accuracy is drastically improved when the number of samples for classification is increased