Robust frequency-domain turbo equalization for multiple-input multiple-output (MIMO) wireless communications

This dissertation investigates single carrier frequency-domain equalization (SC-FDE) with multiple-input multiple-output (MIMO) channels for radio frequency (RF) and underwater acoustic (UWA) wireless communications. It consists of five papers, selected from a total of 13 publications. Each paper focuses on a specific technical challenge of the SC-FDE MIMO system. The first paper proposes an improved frequency-domain channel estimation method based on interpolation to track fast time-varying fading channels using a small amount of training symbols in a large data block. The second paper addresses the carrier frequency offset (CFO) problem using a new group-wise phase estimation and compensation algorithm to combat phase distortion caused by CFOs, rather than to explicitly estimate the CFOs. The third paper incorporates layered frequency-domain equalization with the phase correction algorithm to combat the fast phase rotation in coherent communications. In the fourth paper, the frequency-domain equalization combined with the turbo principle and soft successive interference cancelation (SSIC) is proposed to further improve the bit error rate (BER) performance of UWA communications. In the fifth paper, a bandwidth-efficient SC-FDE scheme incorporating decision-directed channel estimation is proposed for UWA MIMO communication systems. The proposed algorithms are tested by extensive computer simulations and real ocean experiment data. The results demonstrate significant performance improvements in four aspects: improved channel tracking, reduced BER, reduced computational complexity, and enhanced data efficiency --Abstract, page iv

Systematic performance of the ASKAP Fast Radio Burst search algorithm

Detecting fast radio bursts (FRBs) requires software pipelines to search for dispersed single pulses of emission in radio telescope data. In order to enable an unbiased estimation of the underlying FRB population, it is important to understand the algorithm efficiency with respect to the search parameter space and thus the survey completeness. The Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) search pipeline is a single pulse detection pipeline designed to identify radio pulses over a large range of dispersion measures (DM) with low latency. It is used on the Australian Square Kilometre Array Pathfinder (ASKAP) for the Commensal Real-time ASKAP Fast Transients (CRAFT) project . We utilise simulated single pulses in the low- and high-frequency observation bands of ASKAP to analyse the performance of the pipeline and infer the underlying FRB population. The simulation explores the Signal-to-Noise Ratio (S/N) recovery as a function of DM and the temporal duration of FRB pulses in comparison to injected values. The effects of intra-channel broadening caused by dispersion are also carefully studied in this work using control datasets. Our results show that for Gaussian-like single pulses, $> 85 \%$ of the injected signal is recovered by pipelines such as FREDDA at DM < 3000 $\mathrm{pc\ cm^{-3}}$ using standard boxcar filters compared to an ideal incoherent dedispersion match filter. Further calculations with sensitivity implies at least $\sim 10\%$ of FRBs in a Euclidean universe at target sensitivity will be missed by FREDDA and HEIMDALL, another common pipeline, in ideal radio environments at 1.1 GHz.Comment: 11 pages 13 figures. Accepted for MNRAS; Data and simulation code available onlin

Comparison of Conventional and Bayesian Analysis for the Ultrasonic Characterization of Cancellous Bone

This dissertation investigates the physics underlying the propagation of ultrasonic waves in cancellous bone. Although quantitative ultrasound has the potential to evaluate bone quality even better than the current gold standard X-ray based modality, its clinical utility has been hampered by the incomplete understanding of the mechanisms governing the interaction between ultrasound and bone. Therefore, studies that extend the understanding of the fundamental physics of the relationship between ultrasound and trabecular bone tissue may result in improved clinical capabilities. Ultrasonic measurements were carried out on excised human calcaneal specimens in order to study the effects of overlapping fast and slow compressional mode waves on the ultrasonic parameters of attenuation and velocity. Conventional analysis methods were applied to received sample signals that appeared to contain only a single wave mode. The same signals were also analyzed using a Bayesian parameter estimation technique that showed that the signals, which appeared to be only a single wave, could be separated into fast and slow wave components. Results demonstrated that analyzing the data under the assumption that only a single wave mode is present, instead of two interfering waves, yielded a phase velocity that lay between the fast and slow wave velocities and a broadband ultrasound attenuation that was much larger than the ultrasound attenuations of the individual fast and slow waves. The fast and slow wave ultrasonic parameters were found to correlate with microstructural parameters, including porosity, determined by microCT measurements. Simulations of fast and slow wave propagation in cancellous bone were carried out to demonstrate the plausibility of a proposed explanation for an anticipated sample-thickness dependence of the apparent attenuation in bovine bone. The results showed that an apparent sample-thickness dependence could arise if the fast and slow waves are not separated sufficiently and if frequency-domain analysis is not performed on broadband data. The sample-thickness dependence of the ultrasonic parameters was explored further using experimental data acquired on an equine cancellous bone specimen that was systematically shortened. The thickness of the sample varied the degree to which the fast and slow waves overlapped, permitting the use of conventional analysis methods for sufficiently long sample lengths. Bayesian parameter estimation was performed successfully on data from all sample lengths. The ultrasonic parameters obtained by both conventional and Bayesian analysis methods were found unexpectedly to display small, systematic variations with sample thickness. A very thorough and systematic series of studies were carried out on one-mode Lexan phantoms to investigate the potential cause of the observed sample-thickness dependence. These studies ruled out a series of potential contributors to the sample-thickness dependence, but yielded no clear cause. Although the clinical implications of the small but systematic sample-thickness dependence may be negligible, these studies may provide additional insights into the propagation of ultrasonic waves in cancellous bone and how to maximize the quality of information obtained

Data Detection and Channel Estimation of OFDM Systems Using Differential Modulation

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique which is robust against multipath fading and very easy to implement in transmitters and receivers using the inverse fast Fourier transform and the fast Fourier transform. A guard interval using cyclic prefix is inserted in each OFDM symbol to avoid the inter-symbol interference. This guard interval should be at least equal to, or longer than the maximum delay spread of the channel to combat against inter-symbol interference properly. In coherent detection, channel estimation is required for the data detection of OFDM systems to equalize the channel effects. One of the popular techniques is to insert pilot tones (reference signals) in OFDM symbols. In conventional method, pilot tones are inserted into every OFDM symbols. Channel capacity is wasted due to the transmission of a large number of pilot tones. To overcome this transmission loss, incoherent data detection is introduced in OFDM systems, where it is not needed to estimate the channel at first. We use differential modulation based incoherent detection in this thesis for the data detection of OFDM systems. Data can be encoded in the relative phase of consecutive OFDM symbols (inter-frame modulation) or in the relative phase of an OFDM symbol in adjacent subcarriers (in-frame modulation). We use higher order differential modulation for in-frame modulation to compare the improvement of bit error rate. It should be noted that the single differential modulation scheme uses only one pilot tone, whereas the double differential uses two pilot tones and so on. Thus overhead due to the extra pilot tones in conventional methods are minimized and the detection delay is reduced. It has been observed that the single differential scheme works better in low SNRs (Signal to Noise Ratios) with low channel taps and the double differential works better at higher SNRs. Simulation results show that higher order differential modulation schemes don¡¯t have any further advantages. For inter-frame modulation, we use single differential modulation where only one OFDM symbol is used as a reference symbol. Except the reference symbol, no other overhead is required. We also perform channel estimation using differential modulation. Channel estimation using differential modulation is very easy and channel coefficients can be estimated very accurately without increasing any computational complexity. Our simulation results show that the mean square channel estimation error is about ¡¼10¡½^(-2) at an SNR of 30 dB for double differential in-frame modulation scheme, whereas channel estimation error is about ¡¼10¡½^(-4) for single differential inter-frame modulation. Incoherent data detection using classical DPSK (Differential Phase Shift Keying) causes an SNR loss of approximately 3 dB compared to coherent detection. But in our method, differential detection can estimate the channel coefficients very accurately and our estimated channel can be used in simple coherent detection to improve the system performance and minimize the SNR loss that happens in conventional method

ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions

This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems

Advanced methods in automatic modulation classification for emerging technologies

Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate the reception performance. Although the average likelihood ratio test method can provide an optimal solution to the MC problem with unknown parameters, it suffers from high computational complexity and in some cases mathematical intractability. Instead, in this research, an array-based quasi-hybrid likelihood ratio test (qHLRT) algorithm is proposed, which depicts two major advantages. First, it is simple yet accurate enough parameter estimation with reduced complexity. Second the incorporation of antenna arrays offers an effective ability to combat fading. Furthermore, a practical array-based qHLRT classifier scheme is implemented, which applies maximal ratio combining (MRC) to increase the accuracy of both carrier frequency offset (CFO) estimation and likelihood function calculation in channel fading. In fact, double CFO estimations are executed in this classifier. With the first the unknown CFO, phase offsets and amplitudes are estimated as prerequisite for MRC operation. Then, MRC is performed using these estimates, followed by a second CFO estimator. Since the input of the second CFO estimator is the output of the MRC, fading effects on the incoming signals are removed significantly and signal-to-noise ratio (SNR) is augmented. As a result, a more accurate CFO estimate is obtained. Consequently, the overall classification performance is improved, especially in low SNR environment. Recently, many state-of-the-arts communication technologies, such as orthogonal frequency division multiplexing (OFDM) modulations, have been emerging. The need for distinguishing OFDM signal from single carrier has become obvious. Besides, some vital parameters of OFDM signals should be extracted for further processing. In comparison to the research on MC for single carrier single antenna transmission, much less attention has been paid to the MC for emerging modulation methods. A comprehensive classification system is proposed for recognizing the OFDM signal and extracting its parameters. An automatic OFDM modulation classifier is proposed, which is based on the goodness-of-fittest. Since OFDM signal is Gaussian, Cramer-von Mises technique, working on the empirical distribution function, has been applied to test the presence of the normality. Numerical results show that such approach can successfully identify OFDM signals from single carrier modulations over a wide SNR range. Moreover, the proposed scheme can provide the acceptable performance when frequency-selective fading is present. Correlation test is then applied to estimate OFDM cyclic prefix duration. A two-phase searching scheme, which is based on Fast Fourier Transform (FFT) as well as Gaussianity test, is devised to detect the number of subcarriers. In the first phase, a coarse search is carried out iteratively. The exact number of subcarriers is determined by the fine tune in the second phase. Both analytical work and numerical results are presented to verify the efficiency of the proposed scheme