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

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

New challenges in wireless and free space optical communications

AbstractThis manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced

Automatic Modulation Classification Using Cyclic Features via Compressed Sensing

Cognitive Radios (CRs) are designed to operate with minimal interference to the Primary User (PU), the incumbent to a radio spectrum band. To ensure that the interference generated does not exceed a specific level, an estimate of the Signal to Interference plus Noise Ratio (SINR) for the PU’s channel is required. This can be accomplished through determining the modulation scheme in use, as it is directly correlated with the SINR. To this end, an Automatic Modulation Classification (AMC) scheme is developed via cyclic feature detection that is successful even with signal bandwidths that exceed the sampling rate of the CR. In order to accomplish this, Compressed Sensing (CS) is applied, allowing for reconstruction, even with very few samples. The use of CS in spectrum sensing and interpretation is becoming necessary for a growing number of scenarios where the radio spectrum band of interest cannot be fully measured, such as low cost sensor networks, or high bandwidth radio localization services. In order to be able to classify a wide range of modulation types, cumulants were chosen as the feature to use. They are robust to noise and provide adequate discrimination between different types of modulation, even those that are fairly similar, such as 16-QAM and 64-QAM. By fusing cumulants and CS, a novel method of classification was developed which inherited the noise resilience of cumulants, and the low sample requirements of CS. Comparisons are drawn between the proposed method and existing ones, both in terms of accuracy and resource usages. The proposed method is shown to perform similarly when many samples are gathered, and shows improvement over existing methods at lower sample counts. It also uses less resources, and is able to produce an estimate faster than the current systems

Influence du mapping sur la reconnaissance d'un système de communication

Le contexte de cette thèse est la reconnaissance de systèmes de communication dans un contexte non coopératif. Nous nous intéressons au problème de la reconstruction de codes convolutifs et à la reconstruction du mapping (la bijection utilisée pour associer une séquence binaire à un signal modulé). Nous avons élaboré une nouvelle méthode statistique qui à partir d'une séquence binaire bruitée observée permet de détecter si une séquence binaire est codée par un codeur convolutif. Cette méthode consiste à former des blocs de séquence suffisamment grands pour contenir le support d'une équation de parité et à compter le nombre de blocs identiques. Elle a l'avantage de fournir la longueur du code utilisé lorsque le mapping est inconnu. Cette méthode peut également être utilisée pour reconstruire le dual d'un code convolutif lorsque le mapping est connu. Nous proposons par ailleurs un algorithme de reconnaissance de mapping basé sur le parcours de classes d'équivalences. Deux types de classes sont définies. Nous disposons d'un signal bruité partiellement démodulé (démodulé avec un mapping par défaut) et supposons que les données sont codées par un codeur convolutif. Nous utilisons la reconnaissance d'un tel code comme testeur et parcourons enfin les classes d'équivalences faisant apparaître une structure de codes convolutifs. Cette classification améliore la complexité de la recherche pour les petites constellations (4 et 8-PSK). Dans le cas des constellations 16 à 256-QAM l'algorithme est appliqué aux mappings Gray ou quasi-Gray. L'algorithme ne fournit pas un résultat unique mais il permet de trouver un ensemble de mappings possibles à partir de données bruitées.The context of this thesis is the recognition of communication systems in a non-cooperative context. We are interested in the convolutional code reconstruction problem and in the constellation labeling reconstruction (the mapping used to associate a binary sequence to a modulated signal). We have defined a new statistical method for detecting if a given binary sequence is a noisy convolutional code-word obtained from an unknown convolutional code. It consists in forming blocks of sequence which are big enough to contain the support of a parity check equation and counting the number of blocks which are equal. It gives the length of the convolutional code without knowledge of the constellation labeling. This method can also be used to reconstruct the dual of a convolutional code when the constellation labeling is known. Moreover we propose a constellation labeling recognition algorithm using some equivalence classes. Two types of classes are defined: linear and affine. We observe a noisy signal which is partially demodulated (with a default labeling) and assume that the data are coded by a convolutional encoder. Thus we use the reconstruction of a code as a test and run through the classes which reveal a code structure. This classification improves the complexity of the search for small constellations (4-PSK and 8-PSK). In case of 16-QAM to 256-QAM constellations we apply the algorithm to Gray or quasi-Gray labelings. The algorithm does not give a unique result but it allows to find a small set of possible constellation labelings from noisy data.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF