Doctor of Philosophy

dissertationThis dissertation deals with blind modulation identification of quadrature amplitude modulations (QAM) and phase-shift keying (PSK) signals in dual-polarized channels in digital communication systems. The problems addressed in this dissertation are as follows: First, blind modulation identification of QAM and PSK signals in single noisy channels and multipath channels are explored. Second, methods for blind separation of two information streams in a dual-polarized channel and identification of the modulation types of the two information streams are developed. A likelihood-based blind modulation identification for QAM and PSK signals in a single channel with additive white Gaussian noise (AWGN) is developed first. This algorithm selects the modulation type that maximizes a log-likelihood function based on the known probability distribution associated with the phase or amplitude of the received signals for the candidate modulation types. The approach of this paper does not need prior knowledge of carrier frequency or baud rate. Comparisons of theory and simulation demonstrate good agreement in the probability of successful modulation identification under different signal-to-noise ratios (SNRs). Simulation results show that for the signals in AWGN channels containing 10000 symbols and 20 samples per symbol, the system can identify BPSK, QPSK, 8PSK and QAMs of order 16, 32, 64, 128 and 256 with better than 99% accuracy at 4 dB SNR. Under the same condition, the simulation results indicate the two competing methods available in the literature can only reach at most 85% accuracy even at 20 dB SNR for all the modulation types. The simulation results also suggest that when the symbol length decreases, the system needs higher SNRs in order to get accurate identification results. Simulations using different noisy environments indicate that the algorithm is robust to variations of noise environments from the models assumed for derivation of the algorithm. In addition, the combination of a constant modulus amplitude (CMA) equalizer and the likelihood-based modulation identification algorithm is able to identify the QAM signals in multipath channels in a wide range of SNRs. When compared with the results for the signals in AWGN channels, the combination of the CMA equalizer and the likelihood-based modulation identification algorithm needs higher SNRs and longer signal lengths in order to obtain accurate identification results. The second contribution of this dissertation is a new method for blindly identifying PSK and QAM signals in dual-polarized channels. The system combines a likelihood-based adaptive blind source separation (BSS) method and the likelihood-based blind modulation identification method. The BSS algorithm is based on the likelihood functions of the amplitude of the transmitted signals. This system tracks the time-varying polarization coefficients and recovers the input signals to the two channels. The simulation results presented in this paper demonstrate that the likelihood-based adaptive BSS method is able to recover the source signals of different modulation types for a wide range of input SNRs. Comparisons with a natural gradient-based BSS algorithm indicate that the likelihood-based method results in smaller symbol error rates. When a modulation identification algorithm is applied to the separated signals, the overall system is able to identify different PSK and QAM signals with high accuracy at sufficiently high SNRs. For example, with 20,000 symbols, the system identified BPSK and 16-QAM signals with better than 99% accuracy when the input SNR was 8dB and the polarization coefficients rotated with a rate of 1.3 ms. Higher SNRs are needed to obtain similar levels of accuracy when the polarization changes faster or when the number of input symbols is shorter. When compared with the identification results for signals in AWGN channels, the system needs higher SNRs and longer signal length to obtain accurate results for signals in dual-polarized channels

A phase likelihood-based algorithm for blind identification of PSK signals

pre-printThis paper presents a phase likelihood-based method for automatically identifying different phase-shift keying (PSK) modulations. This method identifies the PSK signals as the hypothesis for which the likelihood function of phase difference between nearby samples of the received signal is the maximum. This method does not need prior knowledge of carrier frequency or baud rate and can identify modulation types at relatively low signal-to-noise ratio (SNR) and using small number of input samples. Simulation results demonstrate that this algorithm can identify BPSK, QPSK and 8PSK signals with 100% accuracy with only 1000 symbols when the SNR of the input signal is better than 7 dB. Additional simulation results demonstrating the robustness of the algorithm to variations of the noise characteristics from the assumed Gaussian model are also included in the paper. Performance comparisons indicate that the approach of this paper can achieve 100% accuracy in modulation identification at 5-7 dB lower SNR than competing methods available in the literature

An OFDM Signal Identification Method for Wireless Communications Systems

Distinction of OFDM signals from single carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed signals vanish in contrary to the cumulants of other signals. Thus fourth order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques

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

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 data detection and channel estimation for OFDM systems

We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers from rank deficiency; for this, we give a low-complexity solution. Avoiding a computationally prohibitive exhaustive search, we solve our data detectors using sphere decoding (SD) and V-BLAST and provide simple adaptations of the SD algorithm. We consider how the blind detector performs under mismatch, generalize the basic data detectors to nonunitary constellations, and extend them to systems with pilots and virtual carriers. Simulations show that our data detectors perform well