65 research outputs found
An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel
Computing the distinct features from input data, before the classification,
is a part of complexity to the methods of Automatic Modulation Classification
(AMC) which deals with modulation classification was a pattern recognition
problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude
Modulation (M-QAM) which underneath different channel scenarios was well
detailed. A search of the literature revealed indicates that few studies were
done on the classification of high order M-QAM modulation schemes like128-QAM,
256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the
powerful capability of the natural logarithmic properties and the possibility
of extracting Higher-Order Cumulant's (HOC) features from input data received
raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN)
channel with four effective parameters which were defined to distinguished the
types of modulation from the set; 4-QAM~1024-QAM. This approach makes the
recognizer more intelligent and improves the success rate of classification.
From simulation results, which was achieved under statistical models for noisy
channels, manifest that recognized algorithm executes was recognizing in M-QAM,
furthermore, most results were promising and showed that the logarithmic
classifier works well over both AWGN and different fading channels, as well as
it can achieve a reliable recognition rate even at a lower signal-to-noise
ratio (less than zero), it can be considered as an Integrated Automatic
Modulation Classification (AMC) system in order to identify high order of M-QAM
signals that applied a unique logarithmic classifier, to represents higher
versatility, hence it has a superior performance via all previous works in
automatic modulation identification systemComment: 18 page
Deep Neural Network Architectures for Modulation Classification
This thesis investigates the value of employing deep learning for the task of wireless signal modulation recognition. Recently in deep learning research on AMC, a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a CNN architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of O’shea [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of O’shea [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet) and Densely Connected Network (DenseNet) to achieve high SNR accuracies of approximately 83% and 86.6%, respectively. We also introduce a CLDNN to achieve an accuracy of approximately 88.5% at high SNR. To improve the classification accuracy of QAM, we calculate the high order cumulants of QAM16 and QAM64 as the expert feature and improve the total accuracy to approximately 90%. Finally, by preprocessing the input and send them into a LSTM model, we improve all classification success rates to 100% except the WBFM which is 46%. The average modulation classification accuracy got a improvement of roughly 22% in this thesis
A Novel Graph Neural Network-based Framework for Automatic Modulation Classification in Mobile Environments
Automatic modulation classification (AMC) refers to a signal processing procedure through which the modulation type and order of an observed signal are identified without any prior information about the communications setup. AMC has been recognized as one of the essential measures in various communications research fields such as intelligent modem design, spectrum sensing and management, and threat detection. The research literature in AMC is limited to accounting only for the noise that affects the received signal, which makes their models applicable for stationary environments. However, a more practical and real-world application of AMC can be found in mobile environments where a higher number of distorting effects is present. Hence, in this dissertation, we have developed a solution in which the distorting effects of mobile environments, e.g., multipath, Doppler shift, frequency, phase and timing offset, do not influence the process of identifying the modulation type and order classification. This solution has two major parts: recording an emulated dataset in mobile environments with real-world parameters (MIMOSigRef-SD), and developing an efficient feature-based AMC classifier. The latter itself includes two modules: feature extraction and classification. The feature extraction module runs upon a dynamic spatio-temporal graph convolutional neural network architecture, which tackles the challenges of statistical pattern recognition of received samples and assignment of constellation points. After organizing the feature space in the classification module, a support vector machine is adopted to be trained and perform classification operation. The designed robust feature extraction modules enable the developed solution to outperform other state-of-the-art AMC platforms in terms of classification accuracy and efficiency, which is an important factor for real-world implementations. We validated the performance of our developed solution in a prototyping and field-testing process in environments similar to MIMOSigRef-SD. Therefore, taking all aspects into consideration, our developed solution is deemed to be more practical and feasible for implementation in the next generations of communication systems.
Advisor: Hamid R. Sharif-Kashan
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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)
Automatic modulation classification using techniques from image classification
Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, especially in 5G and 6G technology. Machine Learning (ML) can provide novel and efficient technology for modulation classification, especially for systems working in low signal to noise ratio (SNR). In this article, two dynamic systems not reliant on received signal phase lock and frequency lock are presented, with both employing ML to classify the modulation types for different received SNR. The first model is developed from the previous existing literatures, which utilises constellation images (CI) and image classification technology. Here, modulation types can be detected in a dynamic way without phase lock and frequency lock. In the second model, a new method named Graphic Representation of Features (GRF) is proposed, which represents the statistical features as a spider graph for ML. The concepts are tested and verified using simulations and RF data using a lab software defined radio (SDR). The results from the two models are compared. With the GRF techniques an overall classification accuracy of 59% is observed for 0 dB SNR and 86% at 10 dB SNR, compared to a random guess accuracy of 25%
The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers
Carbon monoxide is a type of pollutant that is harmful to human health and the environment. On the other hand, carbon monoxide also has benefits for industrial matter. Since the benefits and disadvantages of carbon monoxide, the measurement of carbon monoxide concentration is required. The measurement of carbon monoxide level is not easy moreover with low-cost sensors. The usage of 4 sensors namely TGS2611, TGS2612, TGS2610 and TGS2602 has been used along with feature extractor. The polynomial classifier is required to interpret the feature vector into the amount of substance concentration. The common classifier methods suffer fatal limitations. The polynomial classifiers method offers lower complexity in solution and lower computational effort. Since the involvement of a huge number of data points in the modelling process leads to high degree in the polynomial model. The occurrence of Runge's phenomenon is highly possible in this condition. This phenomenon affects the accuracy level of the generated model. The degree reduction algorithm is required to prevent the occurrence of Runge’s phenomenon. The combination of MAF (Mean Average Filter) and derivative approach as degree reductor algorithm has succeeded in reducing the polynomial model degree. The greater the number degree in the model means the greater the computational load. The model degree reductor algorithm has been succeeded to reduce computational load by 96.6%.Karbon monoksida merupakan salah satu jenis polutan yang berbahaya bagi kesehatan manusia dan lingkungan. Di sisi lain, karbon monoksida juga memiliki manfaat untuk keperluan industri. Karena kelebihan dan kekurangan karbon monoksida, maka diperlukan pengukuran konsentrasi karbon monoksida. Pengukuran kadar karbon monoksida tidak mudah apalagi dengan sensor yang murah. Penggunaan 4 sensor yaitu TGS2611, TGS2612, TGS2610 dan TGS2602 telah digunakan bersama dengan feature extractor. Pengklasifikasi polinomial diperlukan untuk menginterpretasikan vektor fitur ke dalam jumlah konsentrasi zat. Metode pengklasifikasi umum mengalami keterbatasan fatal. Metode pengklasifikasi polinomial menawarkan kompleksitas yang lebih rendah dalam solusi dan upaya komputasi yang lebih rendah. Karena keterlibatan sejumlah besar titik data dalam proses pemodelan mengarah ke derajat yang tinggi dalam model polinomial. Fenomena Runge sangat mungkin terjadi pada kondisi ini. Fenomena ini mempengaruhi tingkat akurasi model yang dihasilkan. Algoritma reduksi derajat diperlukan untuk mencegah terjadinya fenomena Runge. Kombinasi MAF (Mean Average Filter) dan pendekatan turunan sebagai algoritma pereduksi derajat telah berhasil mereduksi derajat model polinomial. Semakin besar angka derajat dalam model berarti semakin besar beban komputasinya. Algoritma pereduksi derajat model telah berhasil mengurangi beban komputasi sebesar 96,6%
Automatic Modulation Classification in Mobile OFDM Systems with Adaptive Modulation
Adaptive modulation is an efficient way to combat the effects of deep fades in broadband orthogonal frequency division multiplexing (OFDM) systems with time-varying multipath channels, where modulation schemes are adapted to the current channel state. Bandwidth efficient modulation schemes are applied on subcarriers with high channel quality, while robust modulation schemes or even no modulation is preferred for subcarriers in deep fades. The resulting benefit in terms of required transmit power was demonstrated for a fixed data rate in the literature, where a gain of 5 · · · 15 dB was recorded for a BER of 0.001 over the OFDM system with a fixed modulation. In literature, several algorithms for adaptive modulation have been proposed with different emphasis on bandwidth efficiency and implemental complexity. In this thesis, the algorithm proposed by Chow is used.
A main drawback of adaptive modulation is that it requires the adapted modulation schemes to be provided at the receiver to enable demodulation. Traditionally, this information is provided in forms of explicit signalling, which reduces the bandwidth efficiency due to the signalling overhead. In the thesis, proposals are developed to reduce this undesirable overhead. These proposals exploit the correlation properties inherently existing in the transmission channel in both time and frequency domain, which leads to memory effects in the signalling source. State-dependent Huffman coding schemes are then applied to reduce the redundancy resulting from these memory effects.
This signalling overhead can be totally eliminated by automatic modulation classification (AMC). In the past, AMC was mainly of interest in military fields like threat analysis and electronic surveillance, where no prior knowledge about the used modulation scheme is available. The received signal is the single information source for classification. Under such circumstance, maximum likelihood (ML) based AMC provides the optimum solution in the sense that the classification error probability is minimized. Nowadays, AMC is drawing more and more research interest also in civilian applications like systems with adaptive modulation, where certain co-operations are organized as in the system considered in this thesis. These co-operations provide certain prior information, which can be utilized to improve the classification reliability. Consequently, the ML based approach does not deliver the minimum error probability any more. Investigations have to be conducted to verify how much the performance can be improved by incorporating this prior information into the AMC algorithm. As one focus in this thesis, a AMC algorithm is developed, which is potentially able to minimize the classification error probability again. Another focus is to reduce the implemental complexity to enable the application of AMC in systems with high time requirements like real-time systems.
In the last part of the thesis, comparisons are performed between these two approaches, namely explicit signalling and
signalling-free AMC, in terms of the end-to-end packet error probability. To ensure a fair comparison, the net data rate is always
maintained as a constant in both operation modes
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