Browsing by Author "Alexandra Duel-Hallen, Committee Member"

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Antenna Selection and Space-Time Spreading Methods for Multiple-Antenna Systems
(2005-01-05) Sudarshan, Pallav; Jack Silverstein, Committee Member; Alexandra Duel-Hallen, Committee Member; Brian Hughes, Committee Chair; Hamid Krim, Committee Member
The use of multiple antennas at the transmitter and receiver can significantly improve the performance of a wireless communication system. In recent years, there has been a lot of interest in deriving efficient receiver architectures and designing signalling and coding schemes that maximize the performance gains of a multi-antenna system. In this dissertation, we focus on two such issues: space-time spreading methods at the transmitter, and antenna selection techniques at the receiver. For a synchronous code-division multiple-access (CDMA) system that employs multiple transmit antennas, we characterize the asymptotic spectral efficiency in terms of the number of users, processing gain, signal to noise ratio (SNR), array size, etc. Using this formula, we design the linear space-time spreading methods that maximize the spectral efficiency. The strategy for optimal spreading sequence allocation across antennas, and across users is also addressed. We show that the system capacity per chip is maximized when each user employs all the spreading sequences allocated to it on each transmit antenna. We then study reduced complexity receiver designs for multiple-antenna systems. A RF pre-processing architecture, that processes the received signal at carrier frequency, followed by selection, and down-conversion is considered. Recent results show that this architecture can outperform conventional antenna selection with the same number of RF chains. We derive the optimum RF pre-processing that is based only on the large-scale parameters of the channel. For a correlated channel, we show that RF pre-processing using channel statistics gives good results, and that instantaneous channel knowledge is not required for pre-processing. A beam pattern based geometric intuition is also developed to justify the performance gains. To accommodate the practical design constraints imposed by current variable phase-shifter technology, a sub-optimal phase approximation is also introduced. We show that this scheme is extremely robust to RF imperfections, such as phase and quantization errors. The impact of imperfect channel estimates on the performance of RF pre-processing is also studied, and the scheme is shown to be robust to channel estimate imperfections, as well. Finally, we focus on antenna selection for multi-access channels. For a multi-user system, we derive the statistics-based selection criteria that maximizes tight bounds on ergodic capacity. Two different receiver architectures are considered, and the performance gain compared to sub-optimal selection is quantified.
Asymptotic Analysis of Large Antenna Arrays for Communications and Radar Applications
(2006-03-28) Kamath, Ajith Mulki; Brian L.Hughes, Committee Chair; Hamid Krim, Committee Member; Jack W. Silverstein, Committee Member; Alexandra Duel-Hallen, Committee Member
In recent years there has been a growing interest in using antenna arrays at both ends of a wireless communication link. Such multiple input multiple output (MIMO) systems are beneficial both in terms of providing greatly improved data rates, as well as in terms of robustness in combating errors compared to systems which use only one antenna. These benefits are obtained without requiring extra transmit power or spectral bandwidth, but come at the cost of additional processing power. In radar, multiple antenna arrays have been in use for several decades. Even so, the idea of measuring the full received electro-magnetic (EM) wave for parameter estimation has been a recent one. In this dissertation, we address two issues through asymptotics: in MIMO systems, we develop insights into finite MIMO array performance by deriving precise results for asymptotically large MIMO arrays, and in radar we derive the gain from measuring the complete field over a spherical surface versus measuring only one polarization component using an equal number of sensors. First, we consider the distribution of the mutual information of a MIMO system with an uncorrelated Rayleigh fading channel. We show that, as the transmit and receive array sizes tend to infinity while maintaining their ratio constant, the mutual information distribution tends to Gaussian distribution at all signal to noise ratios (SNRs), and give a closed-form expression for its mean and variance. Through simulations, we observe that the mutual information distribution of a finite MIMO system with as few as 4 array elements at either end has a variance which depends only on the ratio of the two arrays and is also closely approximated by the asymptotic distribution variance. We show that the mean of the distribution can also be approximated much closer than previously shown, and hence combined with the asymptotic variance, this yields close approximations for outage capacities. We next consider the problem of determining the best possible tradeoff between diversity and multiplexing gains in an uncorrelated Rayleigh fading channel. Zheng and Tse have characterized this tradeoff in the large signal to noise ratio(SNR) limit. We apply our asymptotic results on mutual information to compute the finite SNR diversity-multiplexing tradeoffs at high outage probabilities in the range of practical interest. We show that the asymptotic results match the tradeoffs derived by Zheng and Tse only in the equal antenna MIMO array case. We then propose a linear dispersion coding scheme which modulates a block of data by picking a random unitary matrix, which was previously shown to produce full-rank full-diversity code-books with probability one. Through simulations using rectangular code-books, we show that these may also achieve the full Zheng-Tse diversity multiplexing tradeoff after using a maximum likelihood (ML) decoder. Having developed fundamental insights into MIMO arrays through the use of asymptotic analysis, we consider the impact of using vector antennas in large radar arrays. Specifically, we compare the performance of range and direction-of-arrival (DOA) estimation of a single source using an array of vector electro-magnetic (EM) sensors packed densely on the surface of a sphere, with a similarly shaped array with identically oriented dipole elements. We compute the Cramer-Rao lower bound on maximum-likelihood range and DOA estimation using either array. By taking the ratio of the confidence volumes as the gain, we compare the vector array estimate with the uni-polarized array as a function of target location.
Compression and Transmission of Facial Images Over Very Narrowband Channels
(2003-07-13) Gunduz, Aysegul; Christopher G. Healey, Committee Member; Alexandra Duel-Hallen, Committee Member; Hamid Krim, Committee Chair; Paul Allan Sadowski, Committee Member
Law enforcement officers on mobile duty are often confronted with ID authentication of subjects, requiring the transmission of a driver's license picture over wireless channels with very narrow bandwidths. To access mug shots in a reliable and timely manner, real time compression and decompression methods with high compression ratios are required at the server database and at the mobile client unit. This thesis presents a methodology, which minimizes the size of the data sent over the channel by locally storing common features of the human face in the client computers. Pre-processing of server database images, such as facial feature extraction, are used to extract these common facial features, and are obtained via topological methods, in particular, via ravine extraction. The implemented file transfer protocols are based on basic TCP/IP client-server models and make use of socket programming. Experimental results show a 4x improvement in transfer time over typically saturated channels.
Cooperative Communication and Information Processing in Distributed Wireless Networks
(2009-12-04) Zhang, Yanbing; J. Keith Townsend, Committee Member; Ilse Ipsen, Committee Member; Brian L. Hughes, Committee Member; Alexandra Duel-Hallen, Committee Member; Huaiyu Dai, Committee Chair
Large-scale wireless networked systems of intelligent devices are playing an increasingly important role in our life. In such systems, finding solutions in a collaborative and distributed fashion, in the absence of a central coordinator, is of great importance. In this dissertation, we explore some important problems in this area, making certain contributions to both theory and practice of this broad research topic. Recent research has shown that cooperation of wireless nodes can achieve much better energy efficiency, which is known as a main concern for wireless ad-hoc and sensor networks. But whether cooperation benefits the total energy consumption or not highly depends on system demands and network topology. In Chapter 2 of this dissertation, we take an initial step to determine the switching criteria for non-cooperative transmission and some representative cooperative transmission strategies. Energy efficiency of relevant transmission strategies is studied both for wideband asymptotes and realistic system settings. General guidelines are presented for optimal transmission strategy selection in some typical scenarios involving system level metrics, aiming at minimum energy consumption with a target BER. We also address the criteria for choosing the optimal strategy according to instantaneous channel knowledge. Belief propagation (BP) is considered as a prominent information processing framework for wireless networks recently. However, infeasible computation and communication requirement involved in applications entailing non-discrete distributions limits its use in practical situations. In Chapter 3, an effective approach to address the message representation/approximation problem in BP algorithms is studied, exploiting the recently proposed Gaussian particle filtering technique. The effectiveness of our approach is testified through the self-calibration problem in wireless networks, where the system dynamism, largely unexplored in the BP study, is explicitly considered. Distributed and energy efficient in nature, message passing algorithms (such as belief propagation) are attractive for wireless applications. To this end, we propose a variational message passing framework for Markov random fields, with more energy and computation saved compared to the traditional belief propagation algorithm. Based on this framework, structured variational methods are explored to take advantage of the simplicity of approximation and the high accuracy of exact inferences. To investigate the asymptotic performance of this structured distributed inference framework, we first distinguish the intra- and inter-cluster inference algorithms as vertex and edge processes (corresponding to reversible and non-reversible Markov chains respectively). Their difference is illustrated, and convergence rate is derived for the intra-cluster inference procedure which is based on an edge process (the inter-cluster process has been well studied as reversible Markov chains). Then, viewed as a mixed vertex-edge process, the overall performance of structured variational methods is characterized via the coupling approach. The tradeoff between the complexity and performance of this algorithm is also addressed, which provides insights for network design and analysis. This constitutes the Chapter 4 of this dissertation. The structured inference algorithms invoke the requirement of distributed node clustering. To tackle this problem, we also devise a novel distributed network decomposition algorithm with the aid of the factor graph model and the max-product algorithm. Formulating the network decomposition as an optimization problem, we derive a fully distributed procedure to cluster nodes and achieve the (approximate) minimum cut weight. Simple local operations and message forms also make it particularly suitable for wireless networks with limited capability and resource. Moreover, the proposed algorithm is readily extensible, thus providing a potentially powerful, data-independent clustering scheme for a wide range of data processing and networking applications.
Distributed and Collaborative Processing in Wireless Sensor Networks
(2007-08-21) Li, Wenjun; Huaiyu Dai, Committee Chair; Brian Hughes, Committee Member; Alexandra Duel-Hallen, Committee Member; Hamid Krim, Committee Member; Hien Tran, Committee Member
GPS-Gyan: An Open-Source GPS Software Simulator Using Object-Oriented System Design And Modeling Framework.
(2010-06-16) Kumar, Priyank; William Edmonson, Committee Chair; Winser Alexander, Committee Member; Alexandra Duel-Hallen, Committee Member
MIMO Communications Systems: Antenna Selection and Interference Mitigation
(2006-10-24) Zhang, Hongyuan; Alexandra Duel-Hallen, Committee Member; Huaiyu Dai, Committee Chair; Brian L. Hughes, Committee Member; J. Keith Townsend, Committee Member
Multiple-input multiple-output (MIMO) techniques have evolved as one of the key enabling technologies to address the ever-increasing demand for high-speed wireless data access. In this dissertation, we explore some important problems in point-to-point and multiuser MIMO systems. Antenna selection provides a new form of diversity in MIMO systems with low cost and relatively small overhead increase. In the first part, we investigate this technique, concerning (1) associated diversity property analysis in MIMO spatial multiplexing systems with practical transmitter/receiver; (2) fast algorithm design, especially in correlated fading channels; (3) practical implementation. The first two problems above are addressed via novel geometric tools, which are also extended to analyze some open problems in MIMO study, in particular the diversity-multiplexing tradeoff in V-BLAST and SDMA systems employing ordered successive interference cancellation (SIC). Finally we focus on the study of interference mitigation in multiuser multicell MIMO downlink, and investigate the potential of cooperative transmission among adjacent base stations (BS) for effectively mitigating co-channel interference. Our study starts with a quasi-synchronous model to obtain performance upper bounds, by which we also explore some other advantages like channel rank/conditioning improvement and macro-diversity protection. When considering a more practical channel model, in which the inter-cell interfering signals from different BS?s in the downlink are by nature asynchronous at each MS, we propose some novel and effective pre-coding algorithms achieving different levels of tradeoffs between interference mitigation and computational complexity. In summary, we have tackled some open and interesting problems in MIMO study, in particular, the diversity analysis for MIMO spatial multiplexing systems with antenna selection and practical coding and decoding schemes, and the impact of ordering on the performance of SIC (V-BLAST) receivers. The underlying geometric tools for these analyses may find applications in other relevant fields as well. We have also endeavored to improve the performance of MIMO systems in real operating scenarios, with contributions in fast and practical antenna selection algorithms, and co-channel interference mitigation with base-station cooperation explicitly considering the effect of signal asynchronism.
Nonlinear image denoising methodologies
(2003-06-24) Bao, Yufang; Alexandra Duel-Hallen, Committee Member; Robert Cohen, Committee Member; Zhilin Li, Committee Member; Hamid Krim, Committee Chair; Arne A. Nilsson, Committee Member; Alexandra Duel-Hallen, Committee Member; Arne A. Nilsson, Committee Member; Hamid Krim, Committee Chair; Zhilin Li, Committee Member; Robert Cohen, Committee Member; Alexandra Duel-Hallen, Committee Member; Arne A. Nilsson, Committee Member; Hamid Krim, Committee Chair; Zhilin Li, Committee Member; Robert Cohen, Committee Member; Alexandra Duel-Hallen, Committee Member; Arne A. Nilsson, Committee Member; Hamid Krim, Committee Chair; Zhilin Li, Committee Member; Robert Cohen, Committee Member
In this thesis, we propose a theoretical as well as practical framework to combine geometric prior information to a statistical/probabilitstic methodology in the investigation of a denoising problem in its generic form together with its various applications in signal/image analysis. We are able in the process, to investigate, understand and mitigate existing limitations of so-called nonlinear diffusion techniques (such as the Perona-Malik equation) from a probabilistic view point, and propose a new nonlinear denoising method that is based on a random walk whose transition probabilities are selected by the information of a two-sided gradient. This results in a piecewise constant filtered image and lifts the long-standing problem of an unknown evolution stopping time. Our second contribution is in establishing a direct link between multi-resolution analysis techniques and so-called scale space analysis methods, which we in turn utilize to improve the performance of segmentation-optimized image analysis techniques. This is accomplished by using wavelets of higher order vanishing moments, specifically, we achieve a reduction in the typical "blocky" artifacts and a better preservation of texture information. Our third and final contribution is to propose a drastically different approach by isolating statistically independent components in a signal, which we later use as a basis for discrimination against noise, or potentially as plain features. This is related to the well known independent component analysis ( ICA ), for which we first propose Jensen-Rényi divergence as an information- theoretic criterion. In addition, we propose a Rényi mutual divergence as a better criterion to separate mixed signals along with a non-parametric estimation technique for such a measure for 1-D problems. A particle system simulation method is on our future plan of work and is currently ongoing to further investigate the stochastic properties of our diffusion framework.
Performance analysis of power management in WLAN and UMTS
(2006-08-15) Lei, Hongyan; Arne A. Nilsson, Committee Chair; Alexandra Duel-Hallen, Committee Member; Wenye Wang, Committee Member; Mihail Devetsikiotis, Committee Member
Wireless networks have enjoyed the exponential development, and wireless communication has become an essential part of modern life. Many new wireless applications demand higher speed and consume more energy. However, wireless devices are always powered by batteries, which have limited life time and constrain the use of wireless devices and the growth of wireless networks. Energy efficiency becomes an important issue in wireless networks. We study the energy efficiency in the IEEE 802.11 based WLAN (Wireless Local Area Network) and the third generation cellular system UMTS (Universal Mobile Telecommunication System), in which the basic mechanism is to put a mobile device into a low power consumption state when it is idle and wake it up periodically to transmit/receive traffic. In WLANs, the study is focused on the MAC (Media Access Control) sublayer. Two queueing models for the power management mechanisms in an infrastructure network are proposed: the M/G/1 queue with bulk service and the D/G/1 queue. Based on the analytical and simulation results, suggestions are given about how to optimally configure the power management parameters. We also propose the enhanced power management schemes for both infrastructure and independent networks, which outperform the original schemes based on our analysis and simulation. In UMTS, the impacts of Discontinuous Reception (DRX) mechanism and inactivity timer are studied. The simulation of the performance of power saving mechanism is carried out by inputting several typical traffic models specified by 3GPP (third generation partnership project). From the results that different traffic models demand different optimal parameters, we propose to adaptively configure the DRX cycle and inactivity timer parameters based on real-time measurements.
Shape Modeling and Analysis for Object Representation, Reconstruction, and Recognition
(2006-05-15) Baloch, Sajjad; Brian Hughes, Committee Member; Hamid Krim, Committee Chair; Alexandra Duel-Hallen, Committee Member; Irina Kogan, Committee Member
Shape Modeling constitutes a fundamental problem in computer vision. Complexity of the problem arises from the variability of shape realizations, which may be due to their inherent variability or introduced because of adverse situations like noise, pose problem, occlusion, etc. In this thesis, we address this fundamental problem for 2D and 3D shapes in statistical as well as algorithmic settings. In a probabilistic setting, we present a novel method for 2D shape modeling and template learning, which we call Flexible Skew-symmetric Shape Model ($FSSM$). It uses an extended class of semiparametric skew-symmetric distributions. The proposed model aims at capturing the inherent variability of shapes so long as the realization contours remain within a certain neighborhood range around a 'mean' with high probability. It is flexible enough to capture the non-Gaussianity of underlying data, and allows automatic selection of landmarks. We explore several applications of $FSSM$, such as, sampling new shapes, learning templates, and classifying shapes. The algorithmic 2D and 3D shape models are formulated in a Morse theoretic framework, where shapes of arbitrary topology are represented completely by topo-geometric graphs. The idea is to capture topology by localizing critical points of distance function as the Morse function, thereby representing it through skeletal graphs. Geometry, on the other hand, is captured by tracking radii of the corresponding level curves of the distance function (for planar shapes), or by modeling the evolution of these level curves (for 3D shapes). This leads to a weighted skeletal representation, which is then employed for reconstruction, and recognition applications.
Wireless Communications with MIMO Systems: Analysis and Practice
(2007-07-07) Zhou, Quan; Alexandra Duel-Hallen, Committee Member; Jack W. Silverstein, Committee Member; Keith Townsend, Committee Member; Brian L. Hughes, Committee Member; Huaiyu Dai, Committee Chair
Multiple input multiple output (MIMO) systems using multiple transmit and receive antennas are widely considered as the vital breakthrough that will allow future wireless systems to achieve higher date rates and link reliability with limited bandwidth and power resources. In this dissertation, we address four interesting topics in the wireless MIMO systems, in both point-to-point and multiuser environments. First, in a point-to-point MIMO spatial diversity system, usually the probability distribution function (PDF) of the received SNR is rather involved, which leads to the difficulty in analyzing the average symbol error rate (SER). We provide a succinct result at the high SNR region. Second, in point-to-point wireless MIMO communications, in order to protect the transmitted data against random channel impairment, we consider the problem of link adaptation, including rate adaptation and power control to improve the system performance and guarantee certain quality of service. Third, in a multiuser MIMO wireless network, there is another form of diversity called multiuser diversity which can be exploited to increase the system throughput. By analyzing the scheduling gain (defined as the rate difference between the opportunistic scheduling and round-robin scheduling scheme), we provide a complete analysis on the interaction between the spatial diversity and multiuser diversity. Fourth, in a multiuser MIMO wireless network, we propose a crosslayer-based scheduling scheme that exploits Tomlinson-Harashima Precoding (THP) at the physical (PHY) layer to reduce the multiuser scheduling burden at the medium access control (MAC) layer. Compared with some existing scheduling schemes, the proposed scheme greatly reduces the scheduling complexity while simultaneously improves overall system performance.