Blind equalization

An equalizer is an adaptive filter that compensates for the non-ideal characteristics of a communication channel by processing the received signal. The adaptive algorithm searches for the inverse impulse response of the channel, and it requires knowledge of a training sequence, in order to generate an error signal necessary for the adaptive process. There are practical situations where it would be highly desirable to achieve complete adaptation without the use of a training sequence, hence the the term blind . Examples of these situations are multipoint data networks, high-capacity line-of-sight digital radio, and reflection seismology. A blind adaptive algorithm has been developed, based on simplified equalization criteria. These criteria are that the second- and fourth-order moments of the input and output sequences are equalized. The algorithm is entirely driven by statistics, only requiring knowledge of the variance of the input signal. Because of the insensitivity of higher-order statistics to Gaussian processes, the algorithm performs well when additive white Gaussian noise is present in the channel. Simulations are presented in which the new blind equalizer developed is compared to other equalization algorithms

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

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

Robust and Reliable Modulation Classification for MIMO Systems

Abstract: This paper develops a feature-based Automatic Modulation Classification (AMC) algorithm for spatially multiplexed Multiple-Input Multiple-Output (MIMO) systems. The proposed algorithm employs two Higher Order Cumulants (HOCs) of the estimated transmit signal streams as discriminating features, and a multiclass Support Vector Machine (SVM) as a classification system. A multi-classifier classification system is introduced to improve the robustness of the decision made by the classifier at each estimated transmit signal stream. Furthermore, an optimal decision fusion scheme using a Maximum-Likelihood (ML) criterion is also introduced to improve the accuracy and reliability of the final classification decision made in the fusion center. The proposed algorithm shows good performance under different operating conditions, over an acceptable range of SNR, without any prior information about the channel state

Convolutive Blind Source Separation Methods

In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks

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