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Optical Single Sideband for Broadband and Subcarrier Systems
University of Alberta Optical Single Sideband for Broadband And Subcarrier Systems Robert James Davies 0 A thesis submitted to the faculty of Graduate Studies and Research in partial fulfillrnent of the requirernents for the degree of Doctor of Philosophy Department of Electrical And Computer Engineering Edmonton, AIberta Spring 1999 National Library Bibliothèque nationale du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue Wellington Ottawa ON KlA ON4 Ottawa ON KIA ON4 Canada Canada Yom iUe Votre relérence Our iSie Norre reference The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fkom it Ni la thèse ni des extraits substantiels may be printed or otheMrise de celle-ci ne doivent être Unprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Abstract Radio systems are being deployed for broadband residential telecommunication services such as broadcast, wideband lntemet and video on demand. Justification for radio delivery centers on mitigation of problems inherent in subscriber loop upgrades such as Fiber to the Home (WH)and Hybrid Fiber Coax (HFC). -
Chapter 1 Experiment-8
Chapter 1 Experiment-8 1.1 Single Sideband Suppressed Carrier Mod- ulation 1.1.1 Objective This experiment deals with the basic of Single Side Band Suppressed Carrier (SSB SC) modulation, and demodulation techniques for analog commu- nication.− The student will learn the basic concepts of SSB modulation and using the theoretical knowledge of courses. Upon completion of the experi- ment, the student will: *UnderstandSSB modulation and the difference between SSB and DSB modulation * Learn how to construct SSB modulators * Learn how to construct SSB demodulators * Examine the I Q modulator as SSB modulator. * Possess the necessary tools to evaluate and compare the SSB SC modulation to DSB SC and DSB_TC performance of systems. − − 1.1.2 Prelab Exercise 1.Using Matlab or equivalent mathematics software, show graphically the frequency domain of SSB SC modulated signal (see equation-2) . Which of the sign is given for USB− and which for LSB. 1 2 CHAPTER 1. EXPERIMENT-8 2. Draw a block diagram and explain two method to generate SSB SC signal. − 3. Draw a block diagram and explain two method to demodulate SSB SC signal. − 4. According to the shape of the low pass filter (see appendix-1), choose a carrier frequency and modulation frequency in order to implement LSB SSB modulator, with minimun of 30 dB attenuation of the USB component. 1.1.3 Background Theory SSB Modulation DEFINITION: An upper single sideband (USSB) signal has a zero-valued spectrum for f <fcwhere fc, is the carrier frequency. A lower single| | sideband (LSSB) signal has a zero-valued spectrum for f >fc where fc, is the carrier frequency. -
3 Characterization of Communication Signals and Systems
63 3 Characterization of Communication Signals and Systems 3.1 Representation of Bandpass Signals and Systems Narrowband communication signals are often transmitted using some type of carrier modulation. The resulting transmit signal s(t) has passband character, i.e., the bandwidth B of its spectrum S(f) = s(t) is much smaller F{ } than the carrier frequency fc. S(f) B f f f − c c We are interested in a representation for s(t) that is independent of the carrier frequency fc. This will lead us to the so–called equiv- alent (complex) baseband representation of signals and systems. Schober: Signal Detection and Estimation 64 3.1.1 Equivalent Complex Baseband Representation of Band- pass Signals Given: Real–valued bandpass signal s(t) with spectrum S(f) = s(t) F{ } Analytic Signal s+(t) In our quest to find the equivalent baseband representation of s(t), we first suppress all negative frequencies in S(f), since S(f) = S( f) is valid. − The spectrum S+(f) of the resulting so–called analytic signal s+(t) is defined as S (f) = s (t) =2 u(f)S(f), + F{ + } where u(f) is the unit step function 0, f < 0 u(f) = 1/2, f =0 . 1, f > 0 u(f) 1 1/2 f Schober: Signal Detection and Estimation 65 The analytic signal can be expressed as 1 s+(t) = − S+(f) F 1{ } = − 2 u(f)S(f) F 1{ } 1 = − 2 u(f) − S(f) F { } ∗ F { } 1 The inverse Fourier transform of − 2 u(f) is given by F { } 1 j − 2 u(f) = δ(t) + . -
Design and Application of a Hilbert Transformer in a Digital Receiver
Proceedings of the SDR 11 Technical Conference and Product Exposition, Copyright © 2011 Wireless Innovation Forum All Rights Reserved DESIGN AND APPLICATION OF A HILBERT TRANSFORMER IN A DIGITAL RECEIVER Matt Carrick (Northrop Grumman, Chantilly, VA, USA; [email protected]); Doug Jaeger (Northrop Grumman, Chantilly, VA, USA; [email protected]); fred harris (San Diego State University, San Diego, CA, USA; [email protected]) ABSTRACT A common method of down converting a signal from an intermediate frequency (IF) to baseband is using a quadrature down-converter. One problem with the quadrature down-converter is it requires two low pass filters; one for the real branch and one for the imaginary branch. A more efficient way is to transform the real signal to a complex signal and then complex heterodyne the resultant signal to baseband. The transformation of a real signal to a complex signal can be done using a Hilbert transform. Building a Hilbert transform directly from its sampled data sequence produces suboptimal results due to time series truncation; another method is building a Hilbert transformer by synthesizing the filter coefficients from half Figure 1: A quadrature down-converter band filter coefficients. Designing the Hilbert transform filter using a half band filter allows for a much more Another way of viewing the problem is that the structured design process as well as greatly improved quadrature down-converter not only extracts the desired results. segment of the spectrum it rejects the undesired spectral image, the spectral replica present in the Hermetian 1. INTRODUCTION symmetric spectra of a real signal. Removing this image would result in a single sided spectrum which being non- The digital portion of a receiver is typically designed to Hermetian symmetric is the transform of a complex signal. -
2 the Wireless Channel
CHAPTER 2 The wireless channel A good understanding of the wireless channel, its key physical parameters and the modeling issues, lays the foundation for the rest of the book. This is the goal of this chapter. A defining characteristic of the mobile wireless channel is the variations of the channel strength over time and over frequency. The variations can be roughly divided into two types (Figure 2.1): • Large-scale fading, due to path loss of signal as a function of distance and shadowing by large objects such as buildings and hills. This occurs as the mobile moves through a distance of the order of the cell size, and is typically frequency independent. • Small-scale fading, due to the constructive and destructive interference of the multiple signal paths between the transmitter and receiver. This occurs at the spatialscaleoftheorderofthecarrierwavelength,andisfrequencydependent. We will talk about both types of fading in this chapter, but with more emphasis on the latter. Large-scale fading is more relevant to issues such as cell-site planning. Small-scale multipath fading is more relevant to the design of reliable and efficient communication systems – the focus of this book. We start with the physical modeling of the wireless channel in terms of elec- tromagnetic waves. We then derive an input/output linear time-varying model for the channel, and define some important physical parameters. Finally, we introduce a few statistical models of the channel variation over time and over frequency. 2.1 Physical modeling for wireless channels Wireless channels operate through electromagnetic radiation from the trans- mitter to the receiver. -
An FPGA-BASED Implementation of a Hilbert Filter for Real-Time Estimation of Instantaneous Frequency, Phase and Amplitude of Power System Signals
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 23 (2018) pp. 16333-16341 © Research India Publications. http://www.ripublication.com An FPGA-BASED implementation of a Hilbert filter for Real-time Estimation of Instantaneous Frequency, Phase and Amplitude of Power System Signals Q. Bart and R. Tzoneva Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Cape town, 7535, South Africa. Abstract apriori knowledge for a particular fixed sampling rate. The DFT is periodic in nature and assumes that the window of The instantaneous parameters, amplitude, phase and sampled data contains an integer number of waveform cycles. frequency of power system signals provide valuable During frequency perturbations the sampled data may not information about the status of the power system, particularly comprise an integer number of cycles resulting in when these parameters can be obtained simultaneously from discontinuities at the endpoints. This is one of the locations that are remotely located from each other. Phasor disadvantages of the DFT for real-time estimation of Measurement Units (PMU’s) placed at these remote locations synchrophasors and this phenomenon is commonly known as can perform these simultaneous time synchronized spectral leakage. In order to accommodate the dynamic measurements. In this work digital signal processing nature of power system signals an extension of the DFT techniques and Field Programmable Gate Array (FPGA) known as the Taylor-Fourier Transform [4],[5] has also been technology was used for the real-time estimation of power utilized as a synchrophasor estimator. Alternative techniques system signal parameters. These parameters were estimated such as Kalman Filtering [6] and Enhanced Phase Locked based upon an approach utilizing the Hilbert transform and Loop techniques [7] have also been proposed. -
Arxiv:1611.05269V3 [Cs.IT] 29 Jan 2018 Graph Analytic Signal, and Associated Amplitude and Frequency Modulations Reveal Com
On Hilbert Transform, Analytic Signal, and Modulation Analysis for Signals over Graphs Arun Venkitaraman, Saikat Chatterjee, Peter Handel¨ Department of Information Science and Engineering, School of Electrical Engineering and ACCESS Linnaeus Center KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden . Abstract We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and mod- ulation analysis in conventional signal processing, and the observation that comple- mentary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a ’one-sided’ spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to high- light anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed con- cepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that arXiv:1611.05269v3 [cs.IT] 29 Jan 2018 graph analytic signal, and associated amplitude and frequency modulations reveal com- plementary information in speech signals. Keywords: Graph signal, analytic signal, Hilbert transform, demodulation, anomaly detection. Email addresses: [email protected] (Arun Venkitaraman), [email protected] (Saikat Chatterjee), [email protected] (Peter Handel)¨ Preprint submitted to Signal Processing 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 (a) (b) Figure 1: Anomaly highlighting behavior of the graph Hilbert transform for 2D image signal graph. -
7.3.7 Video Cassette Recorders (VCR) 7.3.8 Video Disk Recorders
/7 7.3.5 Fiber-Optic Cables (FO) 7.3.6 Telephone Company Unes (TELCO) 7.3.7 Video Cassette Recorders (VCR) 7.3.8 Video Disk Recorders 7.4 Transmission Security 8. Consumer Equipment Issues 8.1 Complexity of Receivers 8.2 Receiver Input/Output Characteristics 8.2.1 RF Interface 8.2.2 Baseband Video Interface 8.2.3 Baseband Audio Interface 8.2.4 Interfacing with Ancillary Signals 8.2.5 Receiver Antenna Systems Requirements 8.3 Compatibility with Existing NTSC Consumer Equipment 8.3.1 RF Compatibility 8.3.2 Baseband Video Compatibility 8.3.3 Baseband Audio Compatibility 8.3.4 IDTV Receiver Compatibility 8.4 Allows Multi-Standard Display Devices 9. Other Considerations 9.1 Practicality of Near-Term Technological Implementation 9.2 Long-Term Viability/Rate of Obsolescence 9.3 Upgradability/Extendability 9.4 Studio/Plant Compatibility Section B: EXPLANATORY NOTES OF ATTRIBUTES/SYSTEMS MATRIX Items on the Attributes/System Matrix for which no explanatory note is provided were deemed to be self-explanatory. I. General Description (Proponent) section I shall be used by a system proponent to define the features of the system being proposed. The features shall be defined and organized under the headings ot the following subsections 1 through 4. section I. General Description (Proponent) shall consist of a description of the proponent system in narrative form, which covers all of the features and characteris tics of the system which the proponent wishe. to be included in the public record, and which will be used by various groups to analyze and understand the system proposed, and to compare with other propo.ed systems. -
AM Demodulation(Peak Detect.)
AM Demodulation (peak detect.) Demodulation is about recovering the original signal--Crystal Radio Example Antenna = Long WireFM AM A simple Diode! (envelop of AM Signal) Tuning Demodulation Filter Circuit Circuit (Mechanical) Basically a “tapped” Inductor (L) and variable Capacitor (C) We’ll not spend a lot of time on the AM “crystal radio”, although I love it dearly as a COOL, ultra-minimal piece of electronics-- Imagine, you get radio FREE with no batteries required. But… The things we will look at and actually do a bit in lab is to consider the “peak detector” (I.e. the means for demodulating the AM signal) From a block diagram point of view, the circuit has a tuning component (frequency selective filter) attached to the antenna (basically a wire for the basic X-tal radio). The demodulation consists of a diode (called the “crystal” from the good old days of “Empire of the Air”…movie we’ll watch) and an R-C filter to get rid of the carrier frequency. In the Radio Shack version there is no “C” needed; your ear bones can’t respond to the carrier so they act as “the filter”. The following slide gives a more electronics-oriented view of the circuit… 1 Signal Flow in Crystal Radio-- +V Circuit Level Issues Wire=Antenna -V time Filter: BW •fo set by LC •BW set by RLC fo music “tuning” ground=0V time “KX” “KY” “KZ” frequency +V (only) So, here’s the incoming (modulated) signal and the parallel L-C (so-called “tank” circuit) that is hopefully selective enough (having a high enough “Q”--a term that you’ll soon come to know and love) that “tunes” the radio to the desired frequency. -
Baseband Harmonic Distortions in Single Sideband Transmitter and Receiver System
Baseband Harmonic Distortions in Single Sideband Transmitter and Receiver System Kang Hsia Abstract: Telecommunications industry has widely adopted single sideband (SSB or complex quadrature) transmitter and receiver system, and one popular implementation for SSB system is to achieve image rejection through quadrature component image cancellation. Typically, during the SSB system characterization, the baseband fundamental tone and also the harmonic distortion products are important parameters besides image and LO feedthrough leakage. To ensure accurate characterization, the actual frequency locations of the harmonic distortion products are critical. While system designers may be tempted to assume that the harmonic distortion products are simply up-converted in the same fashion as the baseband fundamental frequency component, the actual distortion products may have surprising results and show up on the different side of spectrum. This paper discusses the theory of SSB system and the actual location of the baseband harmonic distortion products. Introduction Communications engineers have utilized SSB transmitter and receiver system because it offers better bandwidth utilization than double sideband (DSB) transmitter system. The primary cause of bandwidth overhead for the double sideband system is due to the image component during the mixing process. Given data transmission bandwidth of B, the former requires minimum bandwidth of B whereas the latter requires minimum bandwidth of 2B. While the filtering of the image component is one type of SSB implementation, another type of SSB system is to create a quadrature component of the signal and ideally cancels out the image through phase cancellation. M(t) M(t) COS(2πFct) Baseband Message Signal (BB) Modulated Signal (RF) COS(2πFct) Local Oscillator (LO) Signal Figure 1. -
Energy-Detecting Receivers for Wake-Up Radio Applications
Energy-Detecting Receivers for Wake-Up Radio Applications Vivek Mangal Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2020 © 2019 Vivek Mangal All Rights Reserved Abstract Energy-Detecting Receivers for Wake-Up Radio Applications Vivek Mangal In an energy-limited wireless sensor node application, the main transceiver for communication has to operate in deep sleep mode when inactive to prolong the node battery lifetime. Wake-up is among the most efficient scheme which uses an always ON low-power receiver called the wake-up receiver to turn ON the main receiver when required. Energy-detecting receivers are the best fit for such low power operations. This thesis discusses the energy-detecting receiver design; challenges; techniques to enhance sensitivity, selectivity; and multi-access operation. Self-mixers instead of the conventional envelope detectors are proposed and proved to be op- timal for signal detection in these energy-detection receivers. A fully integrated wake-up receiver using the self-mixer in 65 nm LP CMOS technology has a sensitivity of −79.1 dBm at 434 MHz. With scaling, time-encoded signal processing leveraging switching speeds have become attractive. Baseband circuits employing time-encoded matched filter and comparator with DC offset compen- sation loop are used to operate the receiver at 420 pW power. Another prototype at 1.016 GHz is sensitive to −74 dBm signal while consuming 470 pW. The proposed architecture has 8 dB better sensitivity at 10 dB lower power consumption across receiver prototypes. -
Baseband Video Testing with Digital Phosphor Oscilloscopes
Application Note Baseband Video Testing With Digital Phosphor Oscilloscopes Video signals are complex pose instrument that can pro- This application note demon- waveforms comprised of sig- vide accurate information – strates the use of a Tektronix nals representing a picture as quickly and easily. Finally, to TDS 700D-series Digital well as the timing informa- display all of the video wave- Phosphor Oscilloscope to tion needed to display the form details, a fast acquisi- make a variety of common picture. To capture and mea- tion technology teamed with baseband video measure- sure these complex signals, an intensity-graded display ments and examines some of you need powerful instru- give the confidence and the critical measurement ments tailored for this appli- insight needed to detect and issues. cation. But, because of the diagnose problems with the variety of video standards, signal. you also need a general-pur- Copyright © 1998 Tektronix, Inc. All rights reserved. Video Basics Video signals come from a for SMPTE systems, etc. The active portion of the video number of sources, including three derived component sig- signal. Finally, the synchro- cameras, scanners, and nals can then be distributed nization information is graphics terminals. Typically, for processing. added. Although complex, the baseband video signal Processing this composite signal is a sin- begins as three component gle signal that can be carried analog or digital signals rep- In the processing stage, video on a single coaxial cable. resenting the three primary component signals may be combined to form a single Component Video Signals. color elements – the Red, Component signals have an Green, and Blue (RGB) com- composite video signal (as in NTSC or PAL systems), advantage of simplicity in ponent signals.