I present in this thesis the design, implementation and measurement results of a narrowband quantum light source based on cavity-enhanced Parametric Down-Conversion (PDC). Spontaneous Parametric Down-Conversion (SPDC) is the workhorse in the field of optical quantum information and quantum computation, yet it is not suitable for applications where deterministic nonlinearities are required due to its low spectral brightness. By placing the nonlinear crystal inside a cavity, the spectrum of down-conversion is actively modified, such that all the non-resonant modes of down-conversion experience destructive interference, while the resonant mode sees constructive interference, resulting in great enhancement in spectral brightness. I design and construct such a cavity-enhanced down-conversion source with record high spectral brightness, making it possible to use cold atoms as the interaction medium to achieve large nonlinearity between photons. The frequency of the photons is tunable and their coherence time is measured to be on the order of 10 nanoseconds, matching the lifetime of the excited state of typical alkali atoms. I characterize extensively the output of the source by measuring the second-order correlation function, quantifying two-photon indistinguishability, performing quantum state tomography of entangled states, and showing different statistics of the source. The unprecedented long coherence time of the photon pairs has also made possible the encoding of quantum information in the time domain of the photons. I present a theoretical proposal of multi-dimensional quantum information with such long-coherence-time photons and analyze its performance with realistic parameter settings. I implement this proposal with the quantum light source I have built, and show for the first time that a qutrit can be encoded in the time domain of the single photons. I demonstrate the coherence is preserved for the qutrit state, thus ruling out any classical probabilistic explanation of the experimental data. Such an encoding scheme provides an easy access to multi-dimensional systems and can be used as a versatile platform for many quantum information and quantum computation tasks.

The thesis analyses and validates Beamforming methods using Convex Optimization.  CVX which is a Matlab supported tool for convex optimization has been used to develop this concept. An algorithm is designed by which an appropriate system has been identified by varying parameters such as number of antennas, passband width, and stopbands widths of a beamformer. We have observed the beamformer by minimizing the error for Least-square and Infinity norms. A graph obtained by the optimum values between least-square and infinity norms shows us a trade-off between these two norms. We have observed convex optimization for double passband of a beamformer which has proven the flexibility of convex optimization. On extension for this, we designed a filter in which stopband is arbitrary. A constraint is used by which the stopband would be varying depending upon the upper boundary (limiting) line which varies w.r.t y-axis (dB). The beamformer has been observed for feasibility by varying parameters such as number of antennas, arbitrary upper boundaries, stopbands and passband. This proves that there is flexibility for designing a beamformer as desired.

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Smart meters (SMs) are key components of the smart grid (SG) as they gather electrical usage data from residences and businesses.Home area networks (HANs) are used to provide two-way communications between SMs and devices within a building such as appliances. This can be implemented using power line communications (PLCs) on home wiring topologies. In this thesis, a HAN PLC channel model is designed based on a split-phase power system which includes branch circuits, a panel with circuit breakers and bars, a secondary transformer and the wiring of neighboring residences. A cell division (CD) method is employed to construct the channel model. Further, arc fault circuit interrupter (AFCI) and ground fault circuit interrupter (GFCI) circuit breaker models are developed. Several PLC channels are presented and compared with those obtained using existing models. PLC communication systems are affected by noise, thus a noise model is developed which is comprised of background and impulse noise. This noise model can be used to obtain the noise power spectral density (PSD) at receivers in the wiring topology. / Graduate

This diploma thesis deals with the design and testing of a system for powerline communication, enabling control of LED lights. The work first describes the basic principles of powerline communication, specifies the communication bands and summarizes the basic functional requirements for communication within the system for LED lighting control. It also describes the method of selecting a modem for powerline communication, the design of a test module with this modem and also the method of testing real communication between two test modules, including test results. The most comprehensive part of the work describes the design of hardware and software of the universal PLC module and how to use it. In addition, it referes to the development tools used as an example of implementation in LED lighting. At the end of the work are summarized the results of testing the proposed system.

Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c) distributed estimation of sparse signals. Extensive experiments are conducted to demonstrate the power and robustness of our proposed sparse signal estimation algorithms. Specifically, we target the problems of a) channel estimation in massive-MIMO, and b) Narrowband interference mitigation in SC-FDMA. We model these problems as sparse recovery problems and demonstrate how these could be solved naturally using the proposed algorithms.

Multi-carrier systems suffer from several impairments, and communication system engineers use powerful signal processing tools to combat these impairments and keep up with the capacity/rate demands. Compressed sensing (CS) is one such tool that allows recovering any sparse signal, requiring only a few measurements in a domain that is incoherent with the domain of sparsity. Almost all signals of interest have some degree of sparsity, and in this work we utilize the sparsity of impairments in orthogonal frequency division multiplexing (OFDM) and its variants (i.e., orthogonal frequency division multiplexing access (OFDMA) and single-carrier frequency-division multiple access (SC-FDMA)) to combat them using CS. We start with the problem of peak-to-average power ratio (PAPR) reduction in OFDM. OFDM signals suffer from high PAPR and clipping is the simplest PAPR reduction scheme. However, clipping introduces inband distortions that result in compromised performance and hence needs to be mitigated at the receiver. Due to the high PAPR nature of the OFDM signal, only a few instances are clipped, these clipping distortions can be recovered at the receiver by employing CS. We then extend the proposed clipping recovery scheme to an interleaved OFDMA system. Interleaved OFDMA presents a special structure that result in only self-inflicted clipping distortions. In this work, we prove that distortions do not spread over multiple users (while utilizing interleaved carrier assignment in OFDMA) and construct a CS system that recovers the clipping distortions on each user. Finally, we address the problem of narrowband interference (NBI) in SC-FDMA. Unlike OFDM and OFDMA systems, SC-FDMA does not suffer from high PAPR, but (as the data is encoded in time domain) is seriously vulnerable to information loss owing to NBI. Utilizing the sparse nature of NBI (in frequency domain) we combat its effect on SC-FDMA system by CS recovery.

IoT är en marknad som har växt fort under de senaste åren och skapat sig en egen industri. Kärnan i IoT är internetanslutningen och i många fall är mobil kommunikation den bästa lösningen för en IoT-produkt. Problemet är att det inte finns något självklart val av mobil kommunikation för användning i en IoT-produkt. Den mobila kommunikationsbranschen har reagerat på det nya behovet av mobil kommunikationsstandard för IoT och 2016 släppte 3GPP en ny standard av typen LPWAN kallad NB-IoT. Flera företag verkar för att implementera denna standard, och det finns ett behov av att undersöka hur applikationer kan utnyttja standarden på ett effektivt sätt. Denna uppsats presenterar en jämförelse mellan två applikationer som använder olika ALP, HTTP och CoAP, i en LPWAN-kontext. Resultaten av denna jämförelse visar att det finns mycket att vinna på att välja CoAP istället för HTTP, speciellt i en IoT-miljö som applikationerna presenterade i denna uppsats. Uppsatsen presenterar även en samling egenskaper som en applikation bör ha för att utnyttja en LPWAN-kommunikationsstandard effektivt. / The Internet of Things (IoT) is a market that has grown very fast in the last few years,creating an industry of its own. The core of IoT is the Internet connectivity and many times, the best solution for an IoT device is to use some form of mobile connection to solve this. The problem is that there is no obvious choice of mobile communication standard for use in an IoT device. The mobile communications industry has reacted to this newly emerged need of amobile communications standard designed for the IoT domain and in 2016 the 3rd Generation Partnership Project (3GPP) released a Low-Power Wide-Area Network (LPWAN) type of standard named Narrowband IoT (NB-IoT). Several companies are working on implementing this standard, and there is a need to investigate how applications can utilize the standard effectively. This thesis presents a comparison between two applications using different ApplicationLayer Protocol (ALP)s, Hyper-Text Transfer Protocol (HTTP) and Constrained Application Protocol (CoAP), in an LPWAN context. The results of this comparison shows that there is a lot to gain by choosing CoAP over HTTP, especially in an IoT environment such as the applications presented in this thesis. The thesis also presents a collection of properties that applications should have to use an LPWAN effectively.

This research solves current RF propagation modeling gaps by modifying a single-frequency electromagnetic propagation analysis technique for use on communication signals and propagation channels. This research extended the Methods of Ordered Multiple Interactions (MOMI) algorithm to communication signal propagation studies through the use of Fourier decomposition thereby allowing the analysis and prediction of communication signals propagating over rough surfaces. Current methods of predicting and analyzing communication signal propagation rely on either using only a single frequency instead of a band of frequencies, stochastic techniques that model the environmental effect on the propagated signal, or on empirical models based of large amounts of measured situational data. None of these methods fully capture the actual effect that an environment imparts on a communication signal as it propagates. This research also modifies the Physical Optics (PO) algorithm utilizing Fourier decomposition to compare the Extended MOMI algorithm to. Both algorithms are applied to propagation scenarios utilizing frequencies in the 1-GHz and 5-GHz bands against a series of signal bandwidths and surface roughnesses. The results are analyzed singularly for Extended-MOMI and against Extended-Physical Optics to better understand the benefits associated with using the Extended-MOMI, the limits of the narrowband approximation, the errors incurred when utilizing a simpler or faster propagation algorithm, and to generally characterize these rough surface propagation channels. This research also defines and explores which metrics provide the best characterization and utility for communication signal propagation with the additional insights of amplitude-frequency-phase relationships the new algorithm provides. / Doctor of Philosophy / Communication signal propagation, defined as the propagation of signals that have non-zero bandwidths from one point to another, has significant importance in communication signal design, system design, and deployment as well as in spectrum planning applications. Current methods of predicting and analyzing communication signal propagation rely on either using only a single frequency instead of a band of frequencies, stochastic techniques that model the environmental effect on the propagated signal, or on empirical models based of large amounts of measured situational data. None of these methods fully capture the actual effect that an environment imparts on a communication signal as it propagates. A technique that accurately models the environmental effect on propagating communication signals would result in knowledge about a communication signal strength and shape as it passes through the propagation space. Analyzing communication signals with single frequency propagation algorithms requires assuming all the frequencies that make up the communication signal propagate exactly the same way, an assumption known as the narrowband approximation. It is not known when the narrowband approximation breaks down in various circumstances. Consequently a more rigorous approach needed to be identified to enable a more accurate and complete analysis of communication signals, which is the objective of the research. This research solves these modeling gaps by modifying a single-frequency electromagnetic propagation analysis technique, the Method of Ordered Multiple Interactions, for use on communication signals and propagation channels. The new algorithm, Extended-MOMI, allows for an examination of communication signal propagation over rough surfaces. This new algorithm incorporates all of the information needed for communication signal propagation analysis; something that is missing from current methods. This technique enables tailored communication signal propagation studies as well as an investigations into when the narrowband assumption is valid and when simpler and faster algorithms could be utilized for a now known increase in error. This research also explores which metrics are best utilized with the additional signal information the new algorithm enables.

Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficients by solving a set of linear equations referred to as the generalized covariance equations. Conventional approaches such as Gaussian elimination or direct matrix inversion are computationally inefficient for solving such a system of equations especially when the covariance matrix has a high order. We implement two recursive algorithms for efficiently inverting the covariance matrix. First, we implement the Akaike algorithm which exploits the block-Toeplitz structure of the covariance matrix for its recursive inversion. In the second approach, we implement the Wax-Kailath algorithm that achieves a factor of 2 reduction over the Akaike algorithm in the number of recursions involved and the computational effort required to form the inverse matrix. Although a TVAR model works well for IF estimation of frequency modulated (FM) components in white noise, when the model is applied to a signal containing a finitely correlated signal in addition to the white noise, estimation performance degrades; especially when the correlated signal is not weak relative to the FM components. We propose a decorrelating TVAR (DTVAR) model based IF estimation and a DTVAR model based linear prediction error filter for FM interference rejection in a finitely correlated environment. Simulations show notable performance gains for a DTVAR model over the TVAR model for moderate to high SIRs. / Master of Science