Automatic classification of digital communication signal modulations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAutomatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.School of Engineering and Design Brunel University London, the Faculty of Engineering University of Liverpool, and the University of Liverpool Graduate Association (Hong Kong)

機械学習を用いたコグニティブ無線における変調方式識別に関する研究

The current spectrum allocation cannot satisfy the demand for future wireless communications, which prompts extensive studies in search of feasible solutions for the spectrum scarcity. The burden in terms of the spectral efficiency on the radio frequency terminal is intended to be small by cognitive radio (CR) systems that prefer low power transmission, changeable carrier frequencies, and diverse modulation schemes. However, the recent surge in the application of the CR has been accompanied by an indispensable component: the spectrum sensing, to avoid interference towards the primary user. This requirement leads to a complex strategy for sensing and transmission and an increased demand for signal processing at the secondary user. However, the performance of the spectrum sensing can be extended by a robust modulation classification (MC) scheme to distinguish between a primary user and a secondary user along with the interference identification. For instance, the underlying paradigm that enables a concurrent transmission of the primary and secondary links may need a precise measure of the interference that the secondary users cause to the primary users. An adjustment to the transmission power should be made, if there is a change in the modulation of the primary users, implying a noise oor excess at the primary user location; else, the primary user will be subject to interference and a collision may occur.Alternatively, the interweave paradigm that progresses the spectrum efficiency by reusing the allocated spectrum over a temporary space, requires a classification of the intercepted signal into primary and secondary systems. Moreover, a distinction between noise and interference can be accomplished by modulation classification, if spectrum sensing is impossible. Therefore, modulation classification has been a fruitful area of study for over three decades.In this thesis, the modulation classification algorithms using machine learning are investigated while new methods are proposed. Firstly, a supervised machine learning based modulation classification algorithm is proposed. The higher-order cumulants are selected as features, due to its robustness to noise. Stacked denoising autoencoders,which is an extended edition of the neural network, is chosen as the classifier. On one hand stacked pre-train overcomes the shortcoming of local optimization, on the other, denoising function further enhances the anti-noise performance. The performance of this method is compared with the conventional methods in terms of the classification accuracy and execution speed. Secondly, an unsupervised machine learning based modulation classification algorithm is proposed.The features from time-frequency distribution are extracted. Density-based spatial clustering of applications with noise (DBSCAN) is used as the classifier because it is impossible to decide the number of clusters in advance. The simulation reveals that this method has higher classification accuracy than the conventional methods. Moreover, the training phase is unnecessary for this method. Therefore, it has higher workability then supervised method. Finally, the advantages and dis-advantages of them are summarized.For the future work, algorithm optimization is still a challenging task, because the computation capability of hardware is limited. On one hand, for the supervised machine learning, GPU computation is a potential solution for supervised machine learning, to reduce the execution cost. Altering the modulation pool, the network structure has to be redesigned as well. On the other hand, for the unsupervised machine learning, that shifting the symbols to carrier frequency consumes extra computing resources.電気通信大学201

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Joint 1D and 2D Neural Networks for Automatic Modulation Recognition

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O\u27Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these architectures and integrated the models to perform joint detection and classification. To our knowledge, the present research is the first to study and successfully combine a lD ResNet classifier and Yolo v3 object detector to fully automate the process of AMR for parameter estimation, pulse extraction and waveform classification for non-cooperative scenarios. The overall performance of the joint detector/ classifier is 90 at 10 dB signal to noise ratio for 24 digital and analog modulations

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

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