Efficient blind symbol rate estimation and data symbol detection algorithms for linearly modulated signals

Blind estimation of unknown channel parameters and data symbol detection represent major open problems in non-cooperative communication systems such as automatic modulation classification (AMC). This thesis focuses on estimating the symbol rate and detecting the data symbols. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. The thesis consists of two parts: a symbol rate estimator and a symbol detector. First, the symbol rate is estimated using the EM algorithm. In the EM algorithm, it is difficult to obtain the closed form of the log-likelihood function and the density function. Therefore, both functions are approximated by using the Particle Filter (PF) technique. In addition, the symbol rate estimator based on cyclic correlation is proposed as an initialization estimator since the EM algorithm requires initial estimates. To take advantage of the cyclostationary property of the received signal, there is a requirement that the sampling period should be at least four times less than the symbol period on the receiver side. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage inter-symbol interference, which is caused by over- sampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coe±cients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coe±cients. At the end, two resampling schemes are adopted

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

Mixture Kalman filtering for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels

This paper proposes a new blind algorithm, based on Mixture Kalman Filtering (MKF), for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels. MKF is a powerful tool for estimating unknown parameters in non-linear, non-Gaussian, real-time applications. We use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and joint posterior probability density of the unknown carrier offset and transmitted data respectively. We study the effect of Signal to Noise Ratio (SNR) and doppler shift on Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm through computer simulations. The results show that BER of the proposed algorithm achieves the theoreti-cal performance slope for the full acquisition range of normalized carrier frequency offset

Efficient real-time monitoring of an emerging influenza pandemic: How feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here, we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter nonidentifiability

Particle Filter for Joint Blind Carrier Frequency Offset Estimation and Data Detection

Abstract-This paper proposes a new blind algorithm for joint carrier offset estimation and data detection, which is based on particle filtering and recursively estimates the joint posterior probability density function of the unknown transmitted data and the unknown carrier offset. We develop new guidelines for resampling of the particles to take into account carrier offset estimation ambiguity at the edges of the range, and for fine tuning estimates to achieve fast, accurate convergence. The Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm is studied through computer simulations. The results show that the proposed algorithm achieves fast convergence for the full acquisition range for normalized carrier frequency offsets

Multi-stage Wireless Signal Identification for Blind Interception Receiver Design

Protection of critical wireless infrastructure from malicious attacks has become increasingly important in recent years, with the widespread deployment of various wireless technologies and dramatic growth in user populations. This brings substantial technical challenges to the interception receiver design to sense and identify various wireless signals using different transmission technologies. The key requirements for the receiver design include estimation of the signal parameters/features and classification of the modulation scheme. With the proper identification results, corresponding signal interception techniques can be developed, which can be further employed to enhance the network behaviour analysis and intrusion detection. In detail, the initial stage of the blind interception receiver design is to identify the signal parameters. In the thesis, two low-complexity approaches are provided to realize the parameter estimation, which are based on iterative cyclostationary analysis and envelope spectrum estimation, respectively. With the estimated signal parameters, automatic modulation classification (AMC) is performed to automatically identify the modulation schemes of the transmitted signals. A novel approach is presented based on Gaussian Mixture Models (GMM) in Chapter 4. The approach is capable of mitigating the negative effect from multipath fading channel. To validate the proposed design, the performance is evaluated under an experimental propagation environment. The results show that the proposed design is capable of adapting blind parameter estimation, realize timing and frequency synchronization and classifying the modulation schemes with improved performances

Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers

Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.Ph.D.Committee Chair: Douglas B. Williams; Committee Member: Brani Vidakovic; Committee Member: G. Tong zhou; Committee Member: Gordon Stuber; Committee Member: James H. McClella

Energy-detection based spectrum sensing for cognitive radio on a real-time SDR platform

There has been an increase in wireless applications due to the technology boom; consequently raising the level of radio spectrum demand. However, spectrum is a limited resource and cannot be infinitely subdivided to accommodate every application. At the same time, emerging wireless applications require a lot of bandwidth for operation, and have seen exponential growth in their bandwidth usage in recent years. The current spectrum allocation technique, proposed by the Federal Communications Commission (FCC) is a fixed allocation technique. This is inefficient as the spectrum is vacant during times when the primary user is not using the spectrum. This strain on the current available bandwidth has revealed signs of an upcoming spectrum crunch; hence the need to find a solution that satisfies the increasing spectrum demand, without compromising the performance of the applications. This work leverages on cognitive radio technology as a potential solution to the spectrum usage challenge. Cognitive radios have the ability to sense the spectrum and determine the presence or absence of the primary user in a particular subcarrier band. When the spectrum is vacant, a cognitive radio (secondary user) can opportunistically occupy the radio spectrum, optimizing the radio frequency band. The effectiveness of the cognitive radio is determined by the performance of the sensing techniques. Known spectrum-sensing techniques are reviewed, which include energy detection, entropy detection, matched-filter detection, and cyclostationary detection. In this dissertation, the energy sensing technique is examined. A real-time energy detector is developed on the Software-Defined Radio (SDR) testbed that is built with Universal Software Radio Peripheral (USRP) devices, and on the GNU Radio software platform. The noise floor of the system is first analysed to determine the detection threshold, which is obtained using the empirical cumulative distribution method. Simulations are carried out using MATrix LABoratory (MATLAB) to set a benchmark. In both simulations and the SDR development platform, an Orthogonal Frequency Division Multiplexing (OFDM) signal with Quadrature Phase Shift Keying (QPSK) modulation is generated and used as the test signal