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

Classification of linear and nonlinear modulations using Bayesian methods

La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis. Plus précisément, nous étudions trois techniques permettant d'estimer la loi a posteriori d'une modulation au niveau du récepteur. La première technique estime les paramètres inconnus associés aux diverses imperfections affectant le récepteur à l'aide d'une approche Bayésienne couplée avec une méthode de simulation MCMC (Markov Chain Monte Carlo). Une deuxième technique utilise l'algorithme de Baum Welch qui permet d'estimer de manière récursive la loi a posteriori du signal reçu et de déterminer la modulation la plus probable parmi un catalogue donné. La dernière méthode étudiée dans cette thèse consiste à corriger les erreurs de synchronisation de phase et de fréquence avec une boucle de phase. Les algorithmes considérés dans cette thèse ont permis de reconnaître un certain nombre de modulations linéaires de types QAM (Quadrature Amplitude Modulation) et PSK (Phase Shift Keying) mais aussi des modulations non linéaires de type GMSK (Gaussian Minimum Shift Keying). ABSTRACT : This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation. The first technique estimates the unknown parameters associated with various imperfections using a Bayesian approach coupled with Markov Chain Monte Carlo (MCMC) methods. A second technique uses the Baum Welch (BW) algorithm to estimate recursively the posterior probabilities and determine the most likely modulation type from a catalogue. The last method studied in this thesis corrects synchronization errors (phase and frequency offsets) with a phase-locked loop (PLL). The classification algorithms considered in this thesis can recognize a number of linear modulations such as Quadrature Amplitude Modulation (QAM), Phase Shift Keying (PSK), and nonlinear modulations such as Gaussian Minimum Shift Keying (GMSK

Sistema de classificação automática de modulação para rádio cognitivo baseado em redes neurais artificiais e implementado em FPGA

Orientador: Prof. Dr. Luis Henrique Assumpção Lolis.Coorientador: Prof. Dr. André Augusto MarianoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 06/04/2021Inclui referências: p.73-76Resumo: Nos últimos anos, os sistemas de comunicação sem fio têm passado por um rápido crescimento, impulsionados principalmente pelos avanços de novas tecnologias como o 5G, combinadas com o progresso de dispositivos eletrônicos, que demandam cada vez um maior fluxo de dados. Embora essas tecnologias representem um avanço para os sistemas de comunicação, elas enfrentam um problema físico: a escassez de frequências do espectro eletromagnético. Devido a essa limitação, cada vez mais são necessários sistemas capazes de aperfeiçoar o uso do espectro, entregando um ambiente capaz de fornecer uma comunicação rápida, segura e eficiente onde os equipamentos sem fio possam operar. Desta forma, esta pesquisa desenvolveu um sistema de classificação automática de modulação que pode ser aplicado em ambientes de radio cognitivo, baseado em redes neurais artificiais e implementado em uma FPGA. A classificação de modulação e uma das tarefas executadas em um ambiente que aplica radio cognitivo, que tem por objetivo identificar, de maneira automática, a modulação utilizada pelo sinal que chega no receptor, aplicada principalmente em ambientes em que dois usuários desejam compartilhar a mesma banda do espectro, sem que haja interferência nas suas comunicações. Esta pesquisa implementou um algoritmo de classificação automática de modulação baseado em um conjunto de dados sintético contendo cinco classes: BPSK, QPSK, 8-PSK, 16-QAM e ruído. A partir dos dados das modulações e do ruído, foram extraídos alguns parâmetros, os quais foram utilizados para treinar uma rede neural com arquitetura perceptron multi-camada, desenvolvida e treinada utilizando a API do Tensorflow/Keras. A rede neural desenvolvida foi exaustivamente testada com diferentes configurações, variando-se a quantidade de camadas, o numero de neurônios, funções de ativação, entre outros parâmetros, resultando em mais de 2000 modelos possíveis, testados em mais de 200 horas. O melhor modelo destes testes foi escolhido para ser implementado em uma FPGA, demandando que ele fosse atualizado para atender as limitações do circuito. Para esta aplicação, o numero de parâmetros utilizado para a classificação foi reduzido e diferentes arquitetura de redes neurais foram testadas, tendo em vista as limitações do hardware, como o limite de precisão para os cálculos e a quantidade finita de elementos lógicos disponíveis. O desenvolvimento da rede para a FPGA recorreu ao VHDL como linguagem de descrição e foi testada utilizando-se os mesmos dados da implementação no software. A implementação no hardware, entretanto, não contempla o cálculo das features, demandando que ele seja alimentado com estes dados já calculados em suas entradas. Tanto a aplicação em software, bem como a do hardware, foram capazes de classificar corretamente aproximadamente 90% das amostras, quando o SNR era >4 dB. Entretanto, a implementação na FPGA apresentou um rápido decréscimo na sua precisão quando os níveis de SNR chegam a valores negativos, haja vista as limitações impostas nessa aplicação. Todavia, as arquiteturas implementadas nesta pesquisa superam os trabalhos similares disponíveis ate o momento, tendo em vista que utiliza um conjunto de parâmetros de entrada selecionados para a classificação que requerem menos tempo para serem processados e consomem menos recursos para a sua execução. As melhorias aplicadas na rede neural resultaram, ainda, em uma rede menor, capaz de ser implementada em uma FPGA com recursos limitados, sem que a sua precisão final fosse comprometida.Abstract: The wireless communication systems face rapid growth over the past few years, driven by the advances in new technologies such as 5G, combined with the progress of electronic devices that demand a high data flow. Although these technologies represent a breakthrough to the telecommunication area, they face a physical limitation: the scarcity of electromagnetic spectrum frequencies. This limitation demands creating mechanisms capable of improving the spectrum utilization efficiency and delivering a fast, reliable, and secure environment in which the wireless equipment may operate. This research developed a modulation classification algorithm that shall be applied to cognitive radio environments based on neural networks and implemented in an FPGA to address this subject. The modulation classification is one of the tasks performed in a cognitive radio environment, which aims to identify the incoming signal's modulation, primarily applied where two users want to share a frequency band without interfering in each other communication. This research implemented a modulation classification algorithm based on a synthetic dataset containing five classes: BPSK, QPSK, 8-PSK, 16-QAM, and a Noise dataset. A few parameters are extracted from these classes and then used to train a multi-layer perceptron neural network, developed and trained using the Keras/Tensorflow API. The neural network model developed was exhaustively tested with multiple configurations, varying its layers, the number of neurons, activation functions, among other parameters, resulting in more than 2000 possible models tested in more than 200 hours. The best architecture was chosen from the resulting model to be implemented to an FPGA, demanding new improvements to suit the hardware limitations. The input features used for the classification were reduced, and the network itself adapted to the hardware constraints, such as limited precision and a finite number of available logical resources. The hardware implementation used the VHDL language to its conception and was tested using the same software-based implementation data. However, the hardware implementation does not calculate the input features, requiring that the implemented neural network receive the already calculated data. The software and the hardware-based implementations of the modulation classification achieved approximated 90% of accuracy when the SNR is equals to >4 dB. However, the hardware implementation shows a rapid decrease in its precision as the noise levels attain negative levels. Nonetheless, the architecture implemented in this research outperforms similar works developed so far, as it utilizes a set of selected input features for the classification that require less computational time and resources for its execution. The optimizations performed in the neural network architecture resulted in a tinier network, which can be implemented in a limited hardware resource FPGA without compromising the final classification capability

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

Modulation classification of digital communication signals

Modulation classification of digital communications signals plays an important role in both military and civilian sectors. It has the potential of replacing several receivers with one universal receiver. An automatic modulation classifier can be defined as a system that automatically identifies the modulation type of the received signal given that the signal exists and its parameters lie in a known range. This thesis addresses the need for a universal modulation classifier capable of classifying a comprehensive list of digital modulation schemes. Two classification approaches are presented: a decision-theoretic (DT) approach and a neural network (NN) approach. First classifiers are introduced that can classify ASK, PSK, and FSK signals. A decision tree is designed for the DT approach and a NN structure is formulated und trained to classify these signals. Both classifiers use the same key features derived from the intercepted signal. These features are based on the instantaneous amplitude, instantaneous phase, and instantaneous frequency of the intercepted signal, and the cumulates of its complex envelope. Threshold values for the DT approach are found from the minimum total error probabilities of the extracted key features at SNR of 20 to -5dB. The NN parameters are found by training the networks on the same data. The DT and NN classifiers are expanded to include CPM signals. Signals within the CPM class are also added to the classifiers and a separate decision tree and new NN structure are found far these signals. New key features to classify these signals are also introduced. The classifiers are then expanded further to include multiple access signals, followed by QAM, PSK8 and FSK8 signals. New features arc found to classify these signals. The final decision tree is able to accommodate a total of fifteen different modulation types. The NN structure is designed in a hierarchical fashion to optimise the classification performance of these fifteen digital modulation schemes. Both DT and NN classifiers are able to classify signals with more than 90% accuracy in the presence of additive white Gaussian within SNR ranging from 20 to 5dB. However, the performance of the NN classifier appears to be more robust as it degrades gradually at the SNRs of 0 and -5dB. At -5dB, the NN has an overall accuracy of 73.58%, whereas the DT classifier achieves only 47.3% accuracy. The overall accuracy of the NN classifier, over the combined SNR range of 20 to -5dB, is 90.7% compared to 84.56% for the DT classifier. Finally, the performances of these classifiers are tested in the presence of Rayleigh fading. The DT and NN classifier structures are modified to accommodate fading and again, new key features are introduced to accomplish this. With the modifications, the overall accuracy of the NN classifier, over the combined SNR range of 20 to -5dB and 120Hz Doppler shift, is 87.34% compared to 80.52% for the DT classifier

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

Pattern recognition using genetic programming for classification of diabetes and modulation data

The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been utilised in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are utilised to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods