High Performance Adaptive Transmit Beamforming forWireless Networks using Binary CSIT

University of Minnesota Ph.D. dissertation. May 2015. Major: Electrical Engineering. Advisor: Nicholaos Sidiropoulos. 1 computer file (PDF); x, 87 pages.Transmit beamforming is a characteristic feature of the modern wireless communication standards like 4G-LTE and 802.11 ac because of the increasing demand for higher data rates and better Quality of Service at the user-end. Transmit beamforming uses multiple transmit antennas and channel state information (CSI) at the transmitter (Tx) to steer the radiated power towards the intended receiver (Rx) while limiting the leakage caused in other directions. In the absence of channel reciprocity, this channel information is acquired at the transmitter by channel estimation at the intended Rx and subsequent feedback of the quantized channel information back to the Tx. This conventional training method requires a complex Rx design and high communication overhead, which could be a burden when the receivers operate on battery power and have limited computational resources and restricted communication capabilities. Obtaining CSI for limiting interference caused due to leakage, can be much more challenging especially when the Rx affected by the spatial leakage interference (side lobes) is not cooperating with the Tx (as in secondary transmit beamforming in underlay cognitive radio networks, for achieving a high Quality of Service at the secondary Rx while limiting the interference to the primary Rx). This thesis proposes various algorithms that enable the Tx, which has no initial CSI, learn to beamform on-the-fly and asymptotically attain the performance achievable using perfect CSI at the Tx, using 1-bit direct or implicit periodic feedbacks from the receivers of interest. The receivers are assumed to have limited computational capability. This thesis starts by considering long-term transmit beamforming for point-to-point Multiple-Input Single-Output (MISO) links and proposes an online beamforming and learning algorithm using the analytic center cutting plane method which is shown to asymptotically attain optimal performance. A robust maximum-likelihood formulation is next developed to combat feedback errors and correlation drift. The setup is then extended to an underlay cognitive radio network for designing secondary transmit beamforming vectors that maximize the average Signal-to-Noise Ratio (SNR) at the secondary Rx while limiting the interference to the primary Rx, using direct binary channel quality indicator feedback bits from the secondary Rx and indirect ACK-NACK feedback from the primary Rx. When the primary interference threshold is known at the secondary Tx, it is analytically shown that the proposed algorithm converges to maximum average SNR at the secondary Rx achieved using perfect CSI at the Tx. Subsequently, the thesis considers max-min fair transmit beamforming for single group multicast networks (which is NP-hard in general) and introduces a new class of adaptive beamforming algorithms that features guaranteed convergence and state-of-the-art performance at low complexity, when perfect CSI is available at the Tx. Convergence to a Karush-Kuhn-Tucker (KKT) point of a related proportionally fair beamforming is established. Simulations show that the proposed approach outperforms the prior state-of-art in terms of multicast rate, at considerably lower complexity. When there is no initial CSIT, an extension of the online algorithm developed for point to point MISO links is proposed for designing the beamforming vector to maximize the minimum SNR among the users, using only periodic binary SNR feedback from each Rx. The design methodology for the multicast beamforming problem is finally extended in a novel fashion to obtain feasible solutions for non-convex Quadratically Constrained Quadratic Programs (QCQP) with two-sided constraints when the associated matrices are positive semi-definite. In this context, the proposed algorithm starts with a infeasible solution which is iteratively updated using a gradient of the log-barrier function of the non-convex constraints followed by projection onto the intersection of the set of convex constraints and a refining step using successive linear approximation. Convergence of the algorithm is established using the Descent lemma and simulations show that the algorithm obtains feasible solutions with a high probability at a much lower complexity compared to the state-of-the-art

Joint Back-pressure Power Control and Interference Cancellation in Wireless Multi-Hop Networks

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Sub-optical resolution of single spins using magnetic resonance imaging at room temperature in diamond

There has been much recent interest in extending the technique of magnetic resonance imaging (MRI) down to the level of single spins with sub-optical wavelength resolution. However, the signal to noise ratio for images of individual spins is usually low and this necessitates long acquisition times and low temperatures to achieve high resolution. An exception to this is the nitrogen-vacancy (NV) color center in diamond whose spin state can be detected optically at room temperature. Here we apply MRI to magnetically equivalent NV spins in order to resolve them with resolution well below the optical wavelength of the readout light. In addition, using a microwave version of MRI we achieved a resolution that is 1/270 size of the coplanar striplines, which define the effective wavelength of the microwaves that were used to excite the transition. This technique can eventually be extended to imaging of large numbers of NVs in a confocal spot and possibly to image nearby dark spins via their mutual magnetic interaction with the NV spin.Comment: 10 pages, 8 figures, Journal of Luminescence (Article in Press

Efficient nitrogen-vacancy centers' fluorescence excitation and collection from micrometer-sized diamond by a tapered optical fiber

Efficiently excite nitrogen-vacancy (NV) centers in diamond and collect their fluorescence significantly benefit the fiber-optic-based NV sensors. Here, using a tapered optical fiber (TOF) tip, we significantly improve the efficiency of the laser excitation and fluorescence collection of the NV, thus enhance the sensitivity of the fiber-optic based micron-sized diamond magnetic sensor. Numerical calculation shows that the TOF tip delivers a high numerical aperture (NA) and has a high fluorescence excitation and collection efficiency. Experiments demonstrate that using such TOF tip can obtain up to over 7-fold the fluorescence excitation efficiency and over15-fold the fluorescence collection efficiency of a flat-ended (non-TOF) fiber. Such fluorescence collection enhances the sensitivity of the optical fiber-based diamond NV magnetometer, thus extending its potential application region.Comment: 11 pages, 13 figure

Enhancing fluorescence excitation and collection from the nitrogen-vacancy center in diamond through a micro-concave mirror

We experimentally demonstrate a simple and robust optical fibers based method to achieve simultaneously efficient excitation and fluorescence collection from Nitrogen-Vacancy (NV) defects containing micro-crystalline diamond. We fabricate a suitable micro-concave (MC) mirror that focuses scattered excitation laser light into the diamond located at the focal point of the mirror. At the same instance, the mirror also couples the fluorescence light exiting out of the diamond crystal in the opposite direction of the optical fiber back into the optical fiber within its light acceptance cone. This part of fluorescence would have been otherwise lost from reaching the detector. Our proof-of-principle demonstration achieves a 25 times improvement in fluorescence collection compared to the case of not using any mirrors. The increase in light collection favors getting high signal-to-noise ratio (SNR) optically detected magnetic resonance (ODMR) signals hence offers a practical advantage in fiber-based NV quantum sensors. Additionally, we compacted the NV sensor system by replacing some bulky optical elements in the optical path with a 1x2 fiber optical coupler in our optical system. This reduces the complexity of the system and provides portability and robustness needed for applications like magnetic endoscopy and remote-magnetic sensing.Comment: 6 pages, 8 figure

Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial in the number of predictor variables in the model. We relax these global constraints to learn a more expressive local structure with BRL-LSS. BRL-LSS entails a more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data

Studies on Distribution of Biosurfactant Producing Bacteria in Contaminated and Undisturbed Soils of Kanchipuram

Abstract: Ever increasing environmental concern about chemical surfactants triggers attention to microbial derived surface-active compounds, essentially due to their low toxicity and biodegradable nature. At present, biosurfactants are predominantly used in remediation of pollutants, in the enhanced transport of metabolites in bacteria, in enhanced oil recovery, as cosmetic additives, in biological control. However, little is known about the distribution and prevalence of biosurfactant-producing bacteria in the environment. The primary objective of this study was to determine how common culturable surfactant producing bacteria are present in contaminated and undisturbed soil samples in and around Kanchipuram (12°50'23"N 79°42'0"E), Tamilnadu, India. A series of each 5 contaminated and undisturbed soils were collected and plated on R2A agar. Totally, 155 morphologically different bacterial isolates were obtained and qualitatively screened for biosurfactant production in mineral salts medium containing 2% glucose. Out of 155 isolates, eight isolates were positive for biosurfactant production, representing most of the soils tested. Quantitative estimation of surface activity identified two potent biosurfactant producing strains Bacillus sp.BS3 and Pseudomonas sp. Maximum surface activity was observed to be 26.58 x 10 -3 nm -1 and 20.60 x 10 -3 nm -1 respectively for Bacillus sp.BS3 and Pseudomonas sp. BS5. The present study is a preliminary demonstration that the Indian soils are rich in biosurfactant producing bacteria, which can be exploited for industrial production of biosurfactants