Localization Error Bounds for 5G mmWave Systems under I/Q Imbalance

Location awareness is expected to play a significant role in 5G millimeter-wave (mmWave) communication systems. One of the basic elements of these systems is quadrature amplitude modulation (QAM), which has in-phase and quadrature (I/Q) modulators. It is not uncommon for transceiver hardware to exhibit an imbalance in the I/Q components, causing degradation in data rate and signal quality. Under an amplitude and phase imbalance model at both the transmitter and receiver, 2D positioning performance in 5G mmWave systems is considered. Towards that, we derive the position and orientation error bounds and study the effects of the I/Q imbalance parameters on the derived bounds. The numerical results reveal that I/Q imbalance impacts the performance similarly, whether it occurs at the transmitter or the receiver, and can cause a degradation up to 12% in position and orientation estimation accuracy

Multidimensional Index Modulation for 5G and Beyond Wireless Networks

This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits information bits through antenna indices, application of IM to orthogonal frequency division multiplexing (OFDM) subcarriers has opened the door for the extension of IM into different dimensions, such as radio frequency (RF) mirrors, time slots, codes, and dispersion matrices. Recent studies have introduced the concept of multidimensional IM by various combinations of one-dimensional IM techniques to provide higher spectral efficiency (SE) and better bit error rate (BER) performance at the expense of higher transmitter (Tx) and receiver (Rx) complexity. Despite the ongoing research on the design of new IM techniques and their implementation challenges, proper use of the available IM techniques to address different requirements of 5G and beyond networks is an open research area in the literature. For this reason, we first provide the dimensional-based categorization of available IM domains and review the existing IM types regarding this categorization. Then, we develop a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). Additionally, this work defines key performance indicators (KPIs) to quantify the advantages and disadvantages of IM techniques in time, frequency, space, and code dimensions. Finally, future recommendations are given regarding the design of flexible IM-based communication systems for 5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible publicatio

Ischemic Stroke Thrombus Characterization through Quantitative Magnetic Resonance Imaging

Stroke is a pervasive, devastating disease and remains one of the most challenging conditions to treat. In particular, risk of recurrence is dramatically heightened after a primary stroke and requires urgent preventative therapy to effectively mitigate. However, the appropriate preventative therapy depends on the underlying source of the stroke, known as etiology, which is challenging to determine promptly. Current diagnostic tests for determining etiology underwhelm in both sensitivity and specificity, and in as much as 35% of cases etiology is never determined. In ischemic stroke, the composition of the occluding thrombus, specifically its red blood cell (RBC) content, has been shown to be indicative of etiology but remains largely ignored within clinical practice. Currently, composition can only be quantified through histological analysis, an involved process that can be completed in only the minority of cases where a thrombus has been physically retrieved from the patient during treatment. The goal of this thesis is to develop a quantitative MR imaging method which is capable of non-invasive prediction of ischemic stroke etiology through assessment of thrombus RBC content. To achieve this goal, we employed quantitative MR parameters that are sensitive to both RBC content and oxygenation, R2* and quantitative susceptibility mapping (QSM), as well as fat fraction (FF) mapping, and evaluated the ability of modern artificial intelligence techniques to form predictions of RBC content and etiology based on these quantitative MR parameters alone and in combination with patient clinical data. First, we performed an in vitro blood clot imaging experiment, which sought to explicitly define the relationship between clot RBC content, oxygenation and our quantitative MR parameters. We show that both R2* and QSM are sensitive to RBC content and oxygenation, as expected, and that the relationship between R2* and QSM can be used to predict clot RBC content independent of oxygenation status, a necessary step for stroke thrombus characterization where oxygenation is an unknown quantity. Second, we trained a deep convolutional neural network to predict histological RBC content from ex vivo MR images of ischemic stroke thrombi. We demonstrate that a convolutional neural network is capable of RBC content prediction with 66% accuracy and 8% mean absolute error when trained on a limited thrombus dataset, and that prediction accuracy can be improved to up to 74% through data augmentation. Finally, we used a random forest classifier to predict clinical stroke etiology using the same ex vivo thrombus MR image dataset. Here, quantitative thrombus R2*, QSM and FF image texture features were used to train the classifier, and we demonstrate that this method is capable of accurate etiology prediction from thrombus texture information alone (accuracy = 67%, area under the curve (AUC) = 0.68), but that when combined with patient clinical data the performance of the classifier improves dramatically to an accuracy and AUC of 93% and 0.89, respectively. Together, the works presented in this thesis offer extensive ex vivo evidence that quantitative MR imaging is capable of effective stroke thrombus etiology characterization. Such a technique could enable clinical consideration of thrombus composition and bolster stroke etiology determination, thereby improving the management and care of acute ischemic stroke patients

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

A Study on Three Dimensional Spatial Scattering Modulation Systems

With an explosive growth of data traffic demand, the researchers of the mobile communication era forecast that the traffic volume will have a 1000x increase in the forthcoming beyond fifth generation (B5G) network. To satisfy the growing traffic demand, the three-dimensional (3-D) multiple-input-and-multiple-output (MIMO) system is considered as a key technology to enhance spectrum efficiency (SE), which explores degrees of freedom in both the vertical and the horizontal dimensions. Combined with 3-D MIMO technology, index modulation (IM) is proposed to improve both energy efficiency (EE) and SE in the B5G era. Existing IM technologies can be categorized according to the domain in which the additional IM bits are modulated, e.g., the spatial-domain IM, the frequency-domain IM and the beamspace-domain IM etc. As one of the mainstream IM techniques, spatial scattering modulation (SSM) is proposed, which works in the beamspace-domain. For SSM systems, the information bits are denoted by the distinguishable signal scattering paths and the modulated symbols. Therein, two information bit streams are transmitted simultaneously by selections of modulated symbols and scattering paths. However, the existing papers only discuss two-dimensional (2-D) SSM systems. The 2-D SSM system applies linear antenna arrays, which only take the azimuth angles to recognise the direction of scattering paths. Therefore, to take the full advantage of the beamspace-domain resources, this thesis mainly focuses on the 3-D SSM system design and the performance evaluation. Firstly, a novel 3-D SSM system is designed. For the 3-D SSM system, besides the azimuth angles of arrival (AoA) and angles of departure (AoD), the elevation AoA and AoD are considered. Then the optimum detection algorithm is obtained, and the closed-form union upper bound expression on average bit error probability (ABEP) is derived. Moreover, the system performance is evaluated under a typical indoor environment. Numerical results indicate that the novel 3-D SSM system outperforms the conventional 2-D SSM system, which reduces the ABEP by 10 times with the same signal-to-noise ratio (SNR) level under the typical indoor environment. Secondly, for the system equipped with large-scale antenna arrays, hybrid beamforming schemes with several RF-chains have attracted more attention. To further explore the throughput of the 3-D SSM system, a generalised 3-D SSM system is proposed, which generates several RF-chains in a transmission time slot to convey modulated symbols. A system model of generalised 3-D SSM is proposed at first. Then an optimum detection algorithm is designed. Meanwhile, a closed-form expression of the ABEP is also derived and validated by Monte-Carlo simulation. For the performance evaluation, three stochastic propagation environments with randomly distributed scatterers are adopted. The results reveal that the generalised 3-D SSM system has better ABEP performance compared with the system with a single RF-chain. Considering different propagation environments, the SSM system has better ABEP performance under the statical propagation environments than the stochastic propagation environments. Thirdly, to reduce the hardware and computational complexities, two optimisation schemes are proposed for the generalised 3-D SSM systems. The 2-D fast Fourier transform (FFT) based transceivers are designed to improve the hardware friendliness, which replace the analogue phase shift networks by the multi-bit phase shifter networks. To reduce the computational complexity of the optimum detection algorithm, a low-complexity detection scheme is designed based on the linear minimum mean square error (MMSE) algorithm. Meanwhile, to quickly evaluate, the asymptotic ABEP performance and the diversity gain of the generalised 3-D SSM system are obtained

Quantitative susceptibility mapping and susceptibility-based distortion correction of echo planar images

Thesis (Ph. D. in Medical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 143-153).The field of medical image analysis continues to expand as magnetic resonance imaging (MRI) technology advances through increases in field strength and the development of new image acquisition and reconstruction methods. The advent of echo planar imaging (EPI) has allowed volumetric data sets to be obtained in a few seconds, making it possible to image dynamic physiological processes in the brain. In order to extract meaningful information from functional and diffusion data, clinicians and neuroscientists typically combine EPI data with high resolution structural images. Image registration is the process of determining the correct correspondence. Registration of EPI and structural images is difficult due to distortions in EPI data. These distortions are caused by magnetic field perturbations that arise from changes in magnetic susceptibility throughout the object of interest. Distortion is typically corrected by acquiring an additional scan called a fieldmap. A fieldmap provides a direct measure of the magnetic perturbations, allowing distortions to be easily computed and corrected. Fieldmaps, however, require additional scan time, may not be reliable in the presence of significant motion or respiration effects, and are often omitted from clinical protocols. In this thesis, we develop a novel method for correcting distortions in EPI data and registering the EPI to structural MRI. A synthetic fieldmap is computed from a tissue/air segmentation of a structural image using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmnaps, however, neither fieldmaps nor knowledge of shim coefficients is required. In addition, we describe a method for atlas-based segmentation of structural images for calculation of synthetic fieldmaps. CT data sets are used to construct a probabilistic atlas of the head and corresponding MRI is used to train a classifier that segments soft tissue, air, and bone. Synthetic fieldmap results agree well with acquired fieldmaps: 90% of voxel shifts show subvoxel disagreement with those computed from acquired fieldmaps. In addition, synthetic fieldmaps show statistically significant improvement following inclusion of the atlas. In the second part of this thesis, we focus on the inverse problem of reconstructing quantitative magnetic susceptibility maps from acquired fieldmaps. Iron deposits change the susceptibility of tissue, resulting in magnetic perturbations that are detectable with high resolution fieldmaps. Excessive iron deposition in specific regions of the brain is associated with neurodegenerative disorders such as Alzheimer's and Parkinson's disease. In addition, iron is known to accumulate at varying rates throughout the brain in normal aging. Developing a non-invasive method to calculate iron concentration may provide insight into the role of iron in the pathophysiology of neurodegenerative disease. Calculating susceptibility maps from measured fieldmaps is difficult, however, since iron-related field inhomogeneity may be obscured by larger field perturbations, or 'biasfields', arising from adjacent tissue/air boundaries. In addition, the inverse problem is ill-posed, and fieldmap measurements are only valid in limited anatomical regions. In this dissertation, we develop a novel atlas-based susceptibility mapping (ASM) technique that requires only a single fieldmap acquisition and successfully inverts a spatial formulation of the forward field model. We derive an inhomogeneous wave equation that relates the Laplacian of the observed field to the D'Alembertian of susceptibility, and eliminates confounding biasfields. The tissue/air atlas we constructed for susceptibility-based distortion correction is applied to resolve ambiquity in the forward model arising from the ill-posed inversion. We include fourier-based modeling of external susceptibility sources and the associated biasfield in a variational approach, allowing for simultaneous susceptibility estimation and biasfield elimination. Results show qualitative improvement over two methods commonly used to infer underlying susceptibility values and quantitative susceptibility estimates show stronger correlation with postmortem iron concentrations than competing methods.by Clare Poynton.Ph.D.in Medical Engineerin