Synchronization Algorithms for FBMC Systems

Filter bank multicarrier (FBMC) systems, such as FMT and OFDM/OQAM systems, can provide reduced sensitivity to narrowband interference, high flexibility to allocate group of subchannels to different users and a high spectral containment. On the other hand, as all the multicarrier modulation schemes, one of their major drawbacks is their sensitivity to CFO and symbol timing errors. In this thesis the problem of CFO and symbol timing synchronization is examined and new data-aided and blind estimation techniques are proposed. Specifically, it is presented a new joint symbol timing and CFO synchronization algorithm based on the LS approach. Moreover, the joint ML phase offset, CFO and symbol timing estimator for a multiple access OFDM/OQAM system is considered. It is also proposed a closed-form CFO estimator based on the best linear unbiased estimation principle for FMT systems. Blind CFO estimators based on the ML principle for low SNR are also considered and, moreover, a closed-form CFO synchronization algorithm based on the LS method is derived. Finally, it is also proposed, under the assumption of low SNR, the joint ML symbol timing and phase offset estimator

The Telecommunications and Data Acquisition Report

This quarterly publication provides archival reports on developments in programs managed by JPL's Telecommunications and Mission Operations Directorate (TMOD), which now includes the former Telecommunications and Data Acquisition (TDA) Office. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC)

Analog, hybrid, and digital simulation

Analog, hybrid, and digital computerized simulation technique

Doctor of Philosophy

dissertationStochastic methods, dense free-form mapping, atlas construction, and total variation are examples of advanced image processing techniques which are robust but computationally demanding. These algorithms often require a large amount of computational power as well as massive memory bandwidth. These requirements used to be ful lled only by supercomputers. The development of heterogeneous parallel subsystems and computation-specialized devices such as Graphic Processing Units (GPUs) has brought the requisite power to commodity hardware, opening up opportunities for scientists to experiment and evaluate the in uence of these techniques on their research and practical applications. However, harnessing the processing power from modern hardware is challenging. The di fferences between multicore parallel processing systems and conventional models are signi ficant, often requiring algorithms and data structures to be redesigned signi ficantly for efficiency. It also demands in-depth knowledge about modern hardware architectures to optimize these implementations, sometimes on a per-architecture basis. The goal of this dissertation is to introduce a solution for this problem based on a 3D image processing framework, using high performance APIs at the core level to utilize parallel processing power of the GPUs. The design of the framework facilitates an efficient application development process, which does not require scientists to have extensive knowledge about GPU systems, and encourages them to harness this power to solve their computationally challenging problems. To present the development of this framework, four main problems are described, and the solutions are discussed and evaluated: (1) essential components of a general 3D image processing library: data structures and algorithms, as well as how to implement these building blocks on the GPU architecture for optimal performance; (2) an implementation of unbiased atlas construction algorithms|an illustration of how to solve a highly complex and computationally expensive algorithm using this framework; (3) an extension of the framework to account for geometry descriptors to solve registration challenges with large scale shape changes and high intensity-contrast di fferences; and (4) an out-of-core streaming model, which enables developers to implement multi-image processing techniques on commodity hardware

Doctor of Philosophy

dissertationWireless communications pervade all avenues of modern life. The rapid expansion of wireless services has increased the need for transmission schemes that are more spectrally efficient. Dynamic spectrum access (DSA) systems attempt to address this need by building a network where the spectrum is used opportunistically by all users based on local and regional measurements of its availability. One of the principal requirements in DSA systems is to initialize and maintain a control channel to link the nodes together. This should be done even before a complete spectral usage map is available. Additionally, with more users accessing the spectrum, it is important to maintain a stable link in the presence of significant interference in emergency first-responders, rescue, and defense applications. In this thesis, a new multicarrier spread spectrum (MC-SS) technique based on filter banks is presented. The new technique is called filter bank multicarrier spread spectrum (FB-MC-SS). A detailed theory of the underlying properties of this signal are given, with emphasis on the properties that lend themselves to synchronization at the receiver. Proposed algorithms for synchronization, channel estimation, and detection are implemented on a software-defined radio platform to complete an FB-MC-SS transceiver and to prove the practicality of the technique. FB-MC-SS is shown through physical experimentation to be significantly more robust to partial band interference compared to direct sequence spread spectrum. With a higher power interfering signal occupying 90% of its band, FB-MC-SS maintains a low bit error rate. Under the same interference conditions, DS-SS fails completely. This experimentation leads to a theoretical analysis that shows in a frequency selective channel with additive white noise, the FB-MC-SS system has performance that equals that obtained by a DS-SS system employing an optimal rake receiver. This thesis contains a detailed chapter on implementation and design, including lessons learned while prototyping the system. This is to assist future system designers to quickly gain proficiency in further development of this technology

A Linear Algebraic Framework for Autofocus in Synthetic Aperture Radar

Synthetic aperture radar (SAR) provides a means of producing high-resolution microwave images using an antenna of small size. SAR images have wide applications in surveillance, remote sensing, and mapping of the surfaces of both the Earth and other planets. The defining characteristic of SAR is its coherent processing of data collected by an antenna at locations along a trajectory in space. In principle, we can produce an image of extraordinary resolution. However, imprecise position measurements associated with data collected at each location cause phase errors that, in turn, cause the reconstructed image to suffer distortion, sometimes so severe that the image is completely unrecognizable. Autofocus algorithms apply signal processing techniques to restore the focused image. This thesis focuses on the study of the SAR autofocus problem from a linear algebraic perspective. We first propose a general autofocus algorithm, called Fourier-domain Multichannel Autofocus (FMCA), that is developed based on an image support constraint. FMCA can accommodate nearly any SAR imaging scenario, whether it be wide-angle or bistatic (transmit and receive antennas at separate locations). The performance of FMCA is shown to be superior compared to current state-of-the-art autofocus techniques. Next, we recognize that at the heart of many autofocus algorithms is an optimization problem, referred to as a constant modulus quadratic program (CMQP). Currently, CMQP generally is solved by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing applications. Preliminary results show that the new method provides promising performance advantages at the expense of increasing computational cost. Lastly, we propose a novel autofocus algorithm based on maximum likelihood estimation, called maximum likelihood autofocus (MLA). The main advantage of MLA is its reliance on a rigorous statistical model rather than on somewhat heuristic reverse engineering arguments. We show both the analytical and experimental advantages of MLA over existing autofocus methods.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86443/1/khliu_1.pd

Doctor of Philosophy

dissertationMagnetic Resonance (MR) is a relatively risk-free and flexible imaging modality that is widely used for studying the brain. Biophysical and chemical properties of brain tissue are captured by intensity measurements in T1W (T1-Weighted) and T2W (T2-Weighted) MR scans. Rapid maturational processes taking place in the infant brain manifest as changes in co{\tiny }ntrast between white matter and gray matter tissue classes in these scans. However, studies based on MR image appearance face severe limitations due to the uncalibrated nature of MR intensity and its variability with respect to changing conditions of scan. In this work, we develop a method for studying the intensity variations between brain white matter and gray matter that are observed during infant brain development. This method is referred to by the acronym WIVID (White-gray Intensity Variation in Infant Development). WIVID is computed by measuring the Hellinger Distance of separation between intensity distributions of WM (White Matter) and GM (Gray Matter) tissue classes. The WIVID measure is shown to be relatively stable to interscan variations compared with raw signal intensity and does not require intensity normalization. In addition to quantification of tissue appearance changes using the WIVID measure, we test and implement a statistical framework for modeling temporal changes in this measure. WIVID contrast values are extracted from MR scans belonging to large-scale, longitudinal, infant brain imaging studies and modeled using the NLME (Nonlinear Mixed Effects) method. This framework generates a normative model of WIVID contrast changes with time, which captures brain appearance changes during neurodevelopment. Parameters from the estimated trajectories of WIVID contrast change are analyzed across brain lobes and image modalities. Parameters associated with the normative model of WIVID contrast change reflect established patterns of region-specific and modality-specific maturational sequences. We also detect differences in WIVID contrast change trajectories between distinct population groups. These groups are categorized based on sex and risk/diagnosis for ASD (Autism Spectrum Disorder). As a result of this work, the usage of the proposed WIVID contrast measure as a novel neuroimaging biomarker for characterizing tissue appearance is validated, and the clinical potential of the developed framework is demonstrated

Space programs summary no. 37-60, volume 2, for the period 1 September to 31 October 1969. The deep space network

Telemetry and ground support equipment design and developments for Deep Space Networ