A Robust Cooperative Modulation Classification Scheme with Intra sensor Fusion for the Time correlated Flat Fading Channels

Networks with distributed sensors, e.g. cognitive radio networks or wireless sensor networks enable large-scale deployments of cooperative automatic modulation classification (AMC). Existing cooperative AMC schemes with centralised fusion offer considerable performance increase in comparison to single sensor reception. Previous studies were generally focused on AMC scenarios in which multipath channel is assumed to be static during a signal reception. However, in practical mobile environments, time-correlated multipath channels occur, which induce large negative influence on the existing cooperative AMC solutions. In this paper, we propose two novel cooperative AMC schemes with the additional intra-sensor fusion, and show that these offer significant performance improvements over the existing ones under given conditions

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

Multi-stage Wireless Signal Identification for Blind Interception Receiver Design

Protection of critical wireless infrastructure from malicious attacks has become increasingly important in recent years, with the widespread deployment of various wireless technologies and dramatic growth in user populations. This brings substantial technical challenges to the interception receiver design to sense and identify various wireless signals using different transmission technologies. The key requirements for the receiver design include estimation of the signal parameters/features and classification of the modulation scheme. With the proper identification results, corresponding signal interception techniques can be developed, which can be further employed to enhance the network behaviour analysis and intrusion detection. In detail, the initial stage of the blind interception receiver design is to identify the signal parameters. In the thesis, two low-complexity approaches are provided to realize the parameter estimation, which are based on iterative cyclostationary analysis and envelope spectrum estimation, respectively. With the estimated signal parameters, automatic modulation classification (AMC) is performed to automatically identify the modulation schemes of the transmitted signals. A novel approach is presented based on Gaussian Mixture Models (GMM) in Chapter 4. The approach is capable of mitigating the negative effect from multipath fading channel. To validate the proposed design, the performance is evaluated under an experimental propagation environment. The results show that the proposed design is capable of adapting blind parameter estimation, realize timing and frequency synchronization and classifying the modulation schemes with improved performances

Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The Gibbs sampling method converges to the optimal Bayesian solution and, using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assume that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis and on prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog

Artificial Intelligence Aided Receiver Design for Wireless Communication Systems

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR