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

A General Framework for Analyzing, Characterizing, and Implementing Spectrally Modulated, Spectrally Encoded Signals

Fourth generation (4G) communications will support many capabilities while providing universal, high speed access. One potential enabler for these capabilities is software defined radio (SDR). When controlled by cognitive radio (CR) principles, the required waveform diversity is achieved via a synergistic union called CR-based SDR. Research is rapidly progressing in SDR hardware and software venues, but current CR-based SDR research lacks the theoretical foundation and analytic framework to permit efficient implementation. This limitation is addressed here by introducing a general framework for analyzing, characterizing, and implementing spectrally modulated, spectrally encoded (SMSE) signals within CR-based SDR architectures. Given orthogonal frequency division multiplexing (OFDM) is a 4G candidate signal, OFDM-based signals are collectively classified as SMSE since modulation and encoding are spectrally applied. The proposed framework provides analytic commonality and unification of SMSE signals. Applicability is first shown for candidate 4G signals, and resultant analytic expressions agree with published results. Implementability is then demonstrated in multiple coexistence scenarios via modeling and simulation to reinforce practical utility

Non-data aided digital feedforward timing estimators for linear and nonlinear modulations

We propose to develop new non-data aided (NDA) digital feedforward symbol timing estimators for linear and nonlinear modulations, with a view to reducing the sampling rate of the estimators. The proposed estimators rely on the fact that sufficient statistics exist for a signal sampled at the Nyquist rate. We propose an ad hoc extension to the timing estimator based on the log nonlinearity which performs better than existing estimators at this rate when the operating signal-to-noise ratio (SNR) and the excess bandwidth are low. We propose another alternative estimator for operating at the Nyquist rate that has reduced self-noise at high SNR for large rolloff factors. This can be viewed as an extension of the timing estimator based on the square law nonlinearity. For continuous phase modulations (CPM), we propose two novel estimators that can operate at the symbol rate for MSK type signals. Among the class of NDA feedforward timing estimators we are not aware of any other estimator that can function at symbol rate for this type of signals. We also propose several new estimators for the MSK modulation scheme which operate with reduced sampling rate and are robust to carrier frequency offset and phase offset

Co-channel digital signal separation : application and practice

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (leaves 84-86).This thesis studies the theory and application of co-channel digital signal separation techniques. We set up a test-bed with the GNU Software Defined Radio (SDR) platform where we implement and experiment with single-antenna signal separation algorithms. We mainly investigate linearly-modulated digital signals. To do this, we design a multiple RFID card reader capable of decoding multiple commodity ID cards simultaneously. These passive RFID cards transmit DBPSK waveforms once activated. A signal separation function at the receiver delivers great convenience to the users without increasing the complexity and cost of the cards. Second, we derive the optimal criteria for deciding the start of an RFID frame. We show that the commonly utilized correlation rule is suboptimal and that a correction term needs to be considered to achieve the best detection performance. Several rules for frame synchronization are proposed and analyzed numerically using Monte Carlo simulation. These signal separation techniques present an opportunity to improve the capacity of wireless systems and combat interference. This thesis documents design issues in the physical and application layers, thereby demonstrating the great flexibility and strength of the GNU SDR system.by Dawei Shen.S.M

New advances in symbol timing synchronization of single-carrier, multi-carrier and space-time multiple-antenna systems

In this dissertation, the problem of symbol timing synchronization for the following three different communication systems is studied: 1) conventional single-carrier transmissions with single antenna in both transmitter and receiver; 2) single-carrier transmissions with multiple antennas at both transmitter and receiver; and 3) orthogonal frequency division multiplexing (OFDM) based IEEE 802.11a wireless local area networks (WLANs). For conventional single-carrier, single-antenna systems, a general feedforward symbol-timing estimation framework is developed based on the conditional maximum likelihood principle. The proposed algorithm is applied to linear modulations and two commonly used continuous phase modulations: MSK and GMSK. The performance of the proposed estimator is analyzed analytically and via simulations. Moreover, using the newly developed general estimation framework, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-square error expression for this class of estimators is established and the best estimators are determined. Simulation results are presented to corroborate the analytical results. Moving on to single-carrier, multiple-antenna systems, we present two algorithms. The first algorithm is based on a heuristic argument and it improves the optimum sample selection algorithm by Naguib et al. so that accurate timing estimates can be obtained even if the oversampling ratio is small. The performance of the proposed algorithm is analyzed both analytically and via simulations. The second algorithm is based on the maximum likelihood principle. The data aided (DA) and non-data aided (NDA) ML symbol timing estimators and their cor- responding CCRB and MCRB in MIMO correlated ??at-fading channels are derived. It is shown that the improved algorithm developed based on the heuristic argument is just a special case of the DA ML estimator. Simulation results under different operating conditions are given to assess and compare the performances of the DA and NDA ML estimators with respect to their corresponding CCRBs and MCRBs. In the last part of this dissertation, the ML timing synchronizer for IEEE 802.11a WLANs on frequency-selective fading channels is developed. The proposed algorithm is compared with four of the most representative timing synchronization algorithms, one specically designed for IEEE 802.11a WLANs and three other algorithms designed for general OFDM frame synchronization

Sparse graph-based coding schemes for continuous phase modulations

The use of the continuous phase modulation (CPM) is interesting when the channel represents a strong non-linearity and in the case of limited spectral support; particularly for the uplink, where the satellite holds an amplifier per carrier, and for downlinks where the terminal equipment works very close to the saturation region. Numerous studies have been conducted on this issue but the proposed solutions use iterative CPM demodulation/decoding concatenated with convolutional or block error correcting codes. The use of LDPC codes has not yet been introduced. Particularly, no works, to our knowledge, have been done on the optimization of sparse graph-based codes adapted for the context described here. In this study, we propose to perform the asymptotic analysis and the design of turbo-CPM systems based on the optimization of sparse graph-based codes. Moreover, an analysis on the corresponding receiver will be done

Learning and identification of wireless network internode dynamics using software defined radio

The recently developed paradigm of cognitive radio wireless devices has been developed with the goal of achieving more customizable and efficient spectrum utilization of commonly used wireless frequency bands. The primary focus of such spectrum utilization approaches has been to discern occupancies and vacancies over portions of the wireless spectrum without necessarily identifying how specific radio frequency (RF) devices contribute to the temporal dynamics of these occupancy patterns within the spectrum. The aim of this thesis is to utilize a hidden semi-Markov model (HSMM) statistical analysis to infer the individual occupancy patterns of specific users from wireless RF observation traces. It is proposed that the HSMM approach for RF device characterization over time may act as a first step towards performing a more complete characterization of the RF spectrum in which the inferred traffic patterns may demonstrate the coexistence of multiple networks, the specific devices comprising each distinct network, and the level of mutual interference between the component networks resultant from such coexistence. The first main portion of this thesis is the development of a Bayesian learning framework for HSMM characterization of the wireless RF observations, with occupancy periods and each individual RF device being classified as distinct states in the HSMM. The traditional HSMM approach is supplemented with the concept of the hierarchical Dirichlet random process to achieve a minimal number of states needed to effectively capture each distinct device, without the need for strong a priori assumptions regarding the number of devices seen in the RF trace prior to computational analysis. The second portion of the thesis utilizes user-programmed cognitive radios to construct a real-time software-defined RF network environment emulation testbed to assess the accuracy of the HSMM characterization. Finally, the HSMM algorithm is tested on wireless devices operating under an actual implementation of the ubiquitous IEEE 802.11 wireless standard

Synchronisation in sampled receivers for narrowband digital modulation schemes.

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