Advanced Diagnosis Techniques for Radio Telescopes in Astronomical Applications

The performance of radio telescopes in astronomical applications can be affected by structural variations due to: 1. Misalignment of the feeding structure, resulting in a lateral or axial displacement of the receiver; 2. Wind stress; 3. Gravitational distortion as the antenna is tilted; 4. Thermal distortion with ambient temperature or sunlight. Diagnosis methods are necessary to estimate any deviation of the antenna system from its nominal behavior in order to guarantee the maximum performance. Several approaches have been developed during the years, and among them the electromagnetic diagnosis appears today as the most appealing, because it allows a relatively simple measurement setup and a reduced human intervention. Electromagnetic diagnosis is based on the acquisition of the antenna Far Field Pattern (FFP), with the Antenna Under Test (AUT) working in receiving mode. A natural radio star or a satellite beacon provides the signal source. The acquisition of the FFP typically requires a very large number of field samples to get the complete information about the AUT, and the subsequent measurement process may span over several hours. A prolonged acquisition has significant drawbacks related to the continuous tracking of the source and the inconstancy of the environmental conditions. The purpose of the PhD activity has been focused on an optimized formulation of the diagnosis of radio telescopes aimed at reducing the number of field samples to acquire, and so at minimizing the measurement time. A diagnosis approach has been developed, based on the Aperture Field method for the description of the AUT radiation mechanism. A Principal Component Analysis (PCA) has been employed to restore a linear relationship between the unknowns describing the AUT status and the far field data. An optimal far field sampling grid is selected by optimizing the singular values behavior of the relevant linearized operator. During the activity, a computational tool based on Geometrical Optics (GO) has been developed to improve the diagnosis approach. Indeed, once the Aperture Field is recovered from the inversion of the measured FFP, an additional step is required to assess the AUT status from the phase distribution. Obviously, the computation of the phase distribution should be based on efficient algorithms in order to properly manage electrically large reflectors. The developed GO technique relies on the Fast Marching Method (FMM) for the direct solution of the eikonal equation. A GO approach based on the FMM is appealing because it shows a favorable computational trend. Furthermore, the explicit solution of the eikonal equation opens the possibility to set up an inverse ray tracing scheme, which proves particularly convenient compared to direct ray tracing because it allows to easily select the minimum number of rays to be traced. The FMM is also amenable for parallel execution. In particular, in the present work, the Fast Iterative Method has been implemented on Graphics Processing Units (GPUs). Moreover, the FMM has been accelerated by introducing a tree data structure. The tree allows to manage the mutual interactions between multiple scattering surfaces and the parallelization of the ray tracing step. The method has been numerically tested on simple canonical cases to show its performance in terms of accuracy and speed. Then, it has been applied to the evaluation of the Aperture Field phase required by the reflector diagnosis. During the research activity, the problem of validating the diagnosis algorithms has been also faced. Obviously, a numerical analysis can been carried out to test the model employed to describe the system and to evaluate the performance of the algorithm. To this end, a reliable commercial software exploited to simulate reflector antennas has been exploited. However, to complete the analysis, the experimental validation becomes mandatory, and an experimental outdoor far field test range is required. Accordingly, a test range has been set up thanks to the collaboration with Istituto Nazionale di Astrofisica (INAF) of Naples, Italy. Its realization has involved the full development of the software to drive an Alt-Azimuth positioner and to remotely control the instrumentation. In addition, an upgrade of the internal connections of a Vector Network Analyzer has been performed in order to allow the interferometric acquisition

Analysis of and techniques for adaptive equalization for underwater acoustic communication

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2011Underwater wireless communication is quickly becoming a necessity for applications in ocean science, defense, and homeland security. Acoustics remains the only practical means of accomplishing long-range communication in the ocean. The acoustic communication channel is fraught with difficulties including limited available bandwidth, long delay-spread, time-variability, and Doppler spreading. These difficulties reduce the reliability of the communication system and make high data-rate communication challenging. Adaptive decision feedback equalization is a common method to compensate for distortions introduced by the underwater acoustic channel. Limited work has been done thus far to introduce the physics of the underwater channel into improving and better understanding the operation of a decision feedback equalizer. This thesis examines how to use physical models to improve the reliability and reduce the computational complexity of the decision feedback equalizer. The specific topics covered by this work are: how to handle channel estimation errors for the time varying channel, how to use angular constraints imposed by the environment into an array receiver, what happens when there is a mismatch between the true channel order and the estimated channel order, and why there is a performance difference between the direct adaptation and channel estimation based methods for computing the equalizer coefficients. For each of these topics, algorithms are provided that help create a more robust equalizer with lower computational complexity for the underwater channel.This work would not have been possible without support from the O ce of Naval Research, through a Special Research Award in Acoustics Graduate Fellowship (ONR Grant #N00014-09-1-0540), with additional support from ONR Grant #N00014-05- 10085 and ONR Grant #N00014-07-10184

Channel Prediction for Mobile MIMO Wireless Communication Systems

Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes

Filtered gradient algorithms for inverse design problems of one-dimensional burgers equation

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-49262-9_7Inverse design for hyperbolic conservation laws is exemplified through the 1D Burgers equation which is motivated by aircraft’s sonic-boom minimization issues. In particular, we prove that, as soon as the target function (usually a Nwave) isn’t continuous, there is a whole convex set of possible initial data, the backward entropy solution being possibly its centroid. Further, an iterative strategy based on a gradient algorithm involving “reversible solutions” solving the linear adjoint problem is set up. In order to be able to recover initial profiles different from the backward entropy solution, a filtering step of the backward adjoint solution is inserted, mostly relying on scale-limited (wavelet) subspaces. Numerical illustrations, along with profiles similar to F-functions, are presentedAcknowledgements This work was partially supported by the Advanced Grant 694126-DYCON (Dynamic Control) of the European Research Council Executive Agency, ICON of the French ANR (2016-ACHN-0014-01), FA9550-15-1-0027 of AFOSR, A9550-14-1-0214 of the EOARD-AFOSR, and the MTM2014-52347 Grant of the MINECO (Spain

Radio Channel Prediction Based on Parametric Modeling

Long range channel prediction is a crucial technology for future wireless communications. The prediction of Rayleigh fading channels is studied in the frame of parametric modeling in this thesis. Suggested by the Jakes model for Rayleigh fading channels, deterministic sinusoidal models were adopted for long range channel prediction in early works. In this thesis, a number of new channel predictors based on stochastic sinusoidal modeling are proposed. They are termed conditional and unconditional LMMSE predictors respectively. Given frequency estimates, the amplitudes of the sinusoids are modeled as Gaussian random variables in the conditional LMMSE predictors, and both the amplitudes and frequency estimates are modeled as Gaussian random variables in the unconditional LMMSE predictors. It was observed that a part of the channels cannot be described by the periodic sinusoidal bases, both in simulations and measured channels. To pick up this un-modeled residual signal, an adjusted conditional LMMSE predictor and a Joint LS predictor are proposed. Motivated by the analysis of measured channels and recently published physics based scattering SISO and MIMO channel models, a new approach for channel prediction based on non-stationary Multi-Component Polynomial Phase Signal (MC-PPS) is further proposed. The so-called LS MC-PPS predictor models the amplitudes of the PPS components as constants. In the case of MC-PPS with time-varying amplitudes, an adaptive channel predictor using the Kalman filter is suggested, where the time-varying amplitudes are modeled as auto-regressive processes. An iterative detection and estimation method of the number of PPS components and the orders of polynomial phases is also proposed. The parameter estimation is based on the Nonlinear LS (NLLS) and the Nonlinear Instantaneous LS (NILS) criteria, corresponding to the cases of constant and time-varying amplitudes, respectively. The performance of the proposed channel predictors is evaluated using both synthetic signals and measured channels. High order polynomial phase parameters are observed in both urban and suburban environments. It is observed that the channel predictors based on the non-stationary MC-PPS models outperform the other predictors in Monte Carlo simulations and examples of measured urban and suburban channels

Analysis of and techniques for adaptive equalization for underwater acoustic communication

Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-215).Underwater wireless communication is quickly becoming a necessity for applications in ocean science, defense, and homeland security. Acoustics remains the only practical means of accomplishing long-range communication in the ocean. The acoustic communication channel is fraught with difficulties including limited available bandwidth, long delay-spread, time-variability, and Doppler spreading. These difficulties reduce the reliability of the communication system and make high data-rate communication challenging. Adaptive decision feedback equalization is a common method to compensate for distortions introduced by the underwater acoustic channel. Limited work has been done thus far to introduce the physics of the underwater channel into improving and better understanding the operation of a decision feedback equalizer. This thesis examines how to use physical models to improve the reliability and reduce the computational complexity of the decision feedback equalizer. The specific topics covered by this work are: how to handle channel estimation errors for the time varying channel, how to use angular constraints imposed by the environment into an array receiver, what happens when there is a mismatch between the true channel order and the estimated channel order, and why there is a performance difference between the direct adaptation and channel estimation based methods for computing the equalizer coefficients. For each of these topics, algorithms are provided that help create a more robust equalizer with lower computational complexity for the underwater channel.by Ballard J. S. Blair.Ph.D