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

Design and Analysis of OFDM System for Powerline Based Communication

Research on digital communication systems has been greatly developed in the past few years and offers a high quality of transmission in both wired and wireless communication environments. Coupled with advances in new modulation techniques, Orthogonal Frequency Division Multiplexing (OFDM) is a well-known digital multicarrier communication technique and one of the best methods of digital data transmission over a limited bandwidth [1]. In this paper, design and analysis of OFDM system for powerline based communication is proposed. In doing so, MATLAB and embedded Digital Signal Processing (DSP) systems are used to simulate the operation of virtual transmitter and receiver. The performance of the system design is then analysed by adding noise (additive white Gaussian noise, Powerline coloured background noise and Middleton Class A noise) in an attempt to corrupt the signal. In this paper results will show that performance is improved by using lower order modulation formats e.g. Binary Phase Shift Keying (BPSK), QPSK, etc. compared to the higher modulation schemes e.g. 64 Quadrature Amplitude Modulation (QAM); as they offer lower data rates but are more robust in the presence of noise. The performance study of OFDM scheme is also examined with and without presence of noise and application of forward error correction (FEC)

Design and Analysis of OFDM System for Powerline Based Communication

Research on digital communication systems has been greatly developed in the past few years and offers a high quality of transmission in both wired and wireless communication environments. Coupled with advances in new modulation techniques, Orthogonal Frequency Division Multiplexing (OFDM) is a well-known digital multicarrier communication technique and one of the best methods of digital data transmission over a limited bandwidth. The main aim of this research is to design an OFDM modem for powerline-based communication in order to propose and examine a novel approach in comparing the different modulation order, different modulation type, application of Forward Error Correction (FEC) scheme and also application of different noise types and applying them to the two modelled channels, Additive White Gaussian Noise (AWGN) and Powerline modelled channel. This is an attempt to understand and recognise the most suitable technique for the transmission of message or image within a communication system. In doing so, MATLAB and embedded Digital Signal Processing (DSP) systems are used to simulate the operation of virtual transmitter and receiver. The simulation results presented in this project suggest that lower order modulation formats (Binary Phase Shift Keying (BPSK) and 4-Quadrature Amplitude Modulation (QAM)), are the most preferred modulation techniques (in both type and order) for their considerable performance. The results also indicated that, Convolutional Channel Encoding (CCE)-Soft and Block Channel Encoding (BCE)-Soft are by far the best encoding techniques (in FEC type) for their best performance in error detection and correction. Indeed, applying these techniques to the two modelled channels has proven very successful and will be accounted as a novel approach for the transmission of message or image within a powerline based communication system

Automatic Modulation Classification of Common Communication and Pulse Compression Radar Waveforms using Cyclic Features

This research develops a feature-based MAP classification system and applies it to classify several common pulse compression radar and communication modulations. All signal parameters are treated as unknown to the classifier system except SNR and the signal carrier frequency. The features are derived from estimated duty cycle, cyclic spectral correlation, and cyclic cumulants. The modulations considered in this research are BPSK, QPSK, 16-QAM, 64-QAM, 8-PSK, and 16-PSK communication modulations, as well as Barker coded, Barker coded, Barker coded, Frank coded, Px49 coded, and LFM pulse compression modulations. Simulations show that average correct signal modulation type classification %C 90% is achieved for SNR 9dB, average signal modulation family classification %C 90% is achieved for SNR 1dB, and an average communication versus pulse compression radar modulation classification %C 90% is achieved for SNR -4dB. Also, it is shown that the classification cation performance using selected input features is sensitive to signal bandwidth but not to carrier frequency. Mismatched bandwidth between training and testing signals caused degraded classification cation of %C 10% - 14% over the simulated SNR range

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise

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

Deep learning for wireless communications : flexible architectures and multitask learning

Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML.Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML