Signal-To-Noise, Carrier-To-Noise, Ebno on Signal Quality Ratios
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Boundaries of Signal-To-Noise Ratio for Adaptive Code Modulations
BOUNDARIES OF SIGNAL-TO-NOISE RATIO FOR ADAPTIVE CODE MODULATIONS A Thesis by Krittetash Pinyoanuntapong Bachelor of Science in Electrical Engineering, Wichita State University, 2014 Submitted to the Department of Electrical Engineering and Computer Science and the faculty of the Graduate School of Wichita State University in partial fulfillment of the requirements for the degree of Master of Science July 2016 © Copyright 2016 by Krittetash Pinyoanuntapong All Rights Reserved BOUNDARIES OF SIGNAL-TO-NOISE RATIO FOR ADAPTIVE CODE MODULATIONS The following faculty members have examined the final copy of this thesis for form and content, and recommend that it be accepted in partial fulfillment of the requirement for the degree of Master of Science, with a major in Electrical Engineering. ______________________________________ Hyuck M. Kwon, Committee Chair ______________________________________ Pu Wang, Committee Member ______________________________________ Thomas DeLillo, Committee Member iii DEDICATION To my parents, my sister, my brother, and my dear friends iv ACKNOWLEDGEMENTS I would like to thank all the people who contributed in some way to the work described in this thesis. First and foremost, I am very grateful to my advisor, Dr. Hyuck M. Kwon, for giving me the opportunity to be part of this research and for providing the impetus, means, and support that enabled me to work on the project. I deeply appreciate his concern, appreciation, and encouragement, which enabled me to graduate. Additionally, I thank Dr. Thomas K. DeLillo and Dr. Pu Wang, who graciously agreed to serve on my committee and for spending their time at my thesis presentation. Thank you and the best of luck in your future endeavors. -
All You Need to Know About SINAD Measurements Using the 2023
applicationapplication notenote All you need to know about SINAD and its measurement using 2023 signal generators The 2023A, 2023B and 2025 can be supplied with an optional SINAD measuring capability. This article explains what SINAD measurements are, when they are used and how the SINAD option on 2023A, 2023B and 2025 performs this important task. www.ifrsys.com SINAD and its measurements using the 2023 What is SINAD? C-MESSAGE filter used in North America SINAD is a parameter which provides a quantitative Psophometric filter specified in ITU-T Recommendation measurement of the quality of an audio signal from a O.41, more commonly known from its original description as a communication device. For the purpose of this article the CCITT filter (also often referred to as a P53 filter) device is a radio receiver. The definition of SINAD is very simple A third type of filter is also sometimes used which is - its the ratio of the total signal power level (wanted Signal + unweighted (i.e. flat) over a broader bandwidth. Noise + Distortion or SND) to unwanted signal power (Noise + The telephony filter responses are tabulated in Figure 2. The Distortion or ND). It follows that the higher the figure the better differences in frequency response result in different SINAD the quality of the audio signal. The ratio is expressed as a values for the same signal. The C-MES signal uses a reference logarithmic value (in dB) from the formulae 10Log (SND/ND). frequency of 1 kHz while the CCITT filter uses a reference of Remember that this a power ratio, not a voltage ratio, so a 800 Hz, which results in the filter having "gain" at 1 kHz. -
Bit Error Rate Analysis of OFDM
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 04| Apr -2016 www.irjet.net p-ISSN: 2395-0072 Bit Error Rate Analysis of OFDM Nishu Baliyan1, Manish Verma2 1M.Tech Scholar, Digital Communication Sobhasaria Engineering College (SEC), Sikar (Rajasthan Technical University) (RTU), Rajasthan India 2Assistant Professor, Department of Electronics and Communication Engineering Sobhasaria Engineering College (SEC), Sikar (Rajasthan Technical University) (RTU), Rajasthan India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Orthogonal Frequency Division Multiplexing or estimations and translations or, as mentioned OFDM is a modulation format that is being used for many of previously, in mobile digital links that may also the latest wireless and telecommunications standards. OFDM introduce significant Doppler shift. Orthogonal has been adopted in the Wi-Fi arena where the standards like frequency division multiplexing has also been adopted 802.11a, 802.11n, 802.11ac and more. It has also been chosen for a number of broadcast standards from DAB Digital for the cellular telecommunications standard LTE / LTE-A, Radio to the Digital Video Broadcast standards, DVB. It and in addition to this it has been adopted by other standards has also been adopted for other broadcast systems as such as WiMAX and many more. The paper focuses on The MATLAB simulation of Orthogonality of multiple input well -
Measurement of In-Band Optical Noise Spectral Density 1
Measurement of In-Band Optical Noise Spectral Density 1 Measurement of In-Band Optical Noise Spectral Density Sylvain Almonacil, Matteo Lonardi, Philippe Jennevé and Nicolas Dubreuil We present a method to measure the spectral density of in-band optical transmission impairments without coherent electrical reception and digital signal processing at the receiver. We determine the method’s accuracy by numerical simulations and show experimentally its feasibility, including the measure of in-band nonlinear distortions power densities. I. INTRODUCTION UBIQUITUS and accurate measurement of the noise power, and its spectral characteristics, as well as the determination and quantification of the different noise sources are required to design future dynamic, low-margin, and intelligent optical networks, especially in open cable design, where the optical line must be intrinsically characterized. In optical communications, performance is degraded by a plurality of impairments, such as the amplified spontaneous emission (ASE) due to Erbium doped-fiber amplifiers (EDFAs), the transmitter-receiver (TX-RX) imperfection noise, and the power-dependent Kerr-induced nonlinear impairments (NLI) [1]. Optical spectrum-based measurement techniques are routinely used to measure the out-of-band optical signal-to-noise ratio (OSNR) [2]. However, they fail in providing a correct assessment of the signal-to-noise ratio (SNR) and in-band noise statistical properties. Whereas the ASE noise is uniformly distributed in the whole EDFA spectral band, TX-RX noise and NLI mainly occur within the signal band [3]. Once the latter impairments dominate, optical spectrum-based OSNR monitoring fails to predict the system performance [4]. Lately, the scientific community has significantly worked on assessing the noise spectral characteristics and their impact on the SNR, trying to exploit the information in the digital domain by digital signal processing (DSP) or machine learning. -
AN279: Estimating Period Jitter from Phase Noise
AN279 ESTIMATING PERIOD JITTER FROM PHASE NOISE 1. Introduction This application note reviews how RMS period jitter may be estimated from phase noise data. This approach is useful for estimating period jitter when sufficiently accurate time domain instruments, such as jitter measuring oscilloscopes or Time Interval Analyzers (TIAs), are unavailable. 2. Terminology In this application note, the following definitions apply: Cycle-to-cycle jitter—The short-term variation in clock period between adjacent clock cycles. This jitter measure, abbreviated here as JCC, may be specified as either an RMS or peak-to-peak quantity. Jitter—Short-term variations of the significant instants of a digital signal from their ideal positions in time (Ref: Telcordia GR-499-CORE). In this application note, the digital signal is a clock source or oscillator. Short- term here means phase noise contributions are restricted to frequencies greater than or equal to 10 Hz (Ref: Telcordia GR-1244-CORE). Period jitter—The short-term variation in clock period over all measured clock cycles, compared to the average clock period. This jitter measure, abbreviated here as JPER, may be specified as either an RMS or peak-to-peak quantity. This application note will concentrate on estimating the RMS value of this jitter parameter. The illustration in Figure 1 suggests how one might measure the RMS period jitter in the time domain. The first edge is the reference edge or trigger edge as if we were using an oscilloscope. Clock Period Distribution J PER(RMS) = T = 0 T = TPER Figure 1. RMS Period Jitter Example Phase jitter—The integrated jitter (area under the curve) of a phase noise plot over a particular jitter bandwidth. -
Performance Comparisons of Broadband Power Line Communication Technologies
applied sciences Article Performance Comparisons of Broadband Power Line Communication Technologies Young Mo Chung Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Korea; [email protected]; Tel.: +82-2-760-4342 Received: 5 April 2020; Accepted: 6 May 2020 ; Published: 9 May 2020 Abstract: Broadband power line communication (PLC) is used as a communication technique for advanced metering infrastructure (AMI) in Korea. High-speed (HS) PLC specified in ISO/IEC12139-1 and HomePlug Green PHY (HPGP) are deployed for remote metering. Recently, internet of things (IoT) PLC has been proposed for reliable communications on harsh power line channels. In this paper, the physical layer performance of IoT PLC, HPGP, and HS PLC is evaluated and compared. Three aspects of the performance are evaluated: the bit rate, power spectrum, and bit error rate (BER). An expression for the bit rate for IoT PLC and HPGP is derived while taking the padding bits and number of tones in use into consideration. The power spectrum is obtained through computer simulations. For the BER performance comparisons, the upper bound of the BER for each PLC standard is evaluated through computer simulations. Keywords: advanced metering infrastructure; power line communication; IoT PLC; HPGP; ISO/IEC 12139-1; HS PLC; bit error rate 1. Introduction In a smart grid, the advanced metering infrastructure (AMI) is responsible for collecting data from consumer utilities and giving commands to them. AMI consists of smart meters, communication networks, and data managing systems [1,2]. An important challenge in building an AMI is choosing a cost-effective communication network [2–4]. -
Estimation of BER from Error Vector Magnitude for Optical Coherent Systems
hv photonics Article Estimation of BER from Error Vector Magnitude for Optical Coherent Systems Irshaad Fatadin National Physical Laboratory, Teddington, Middlesex TW11 0LW, UK; [email protected]; Tel.: +44-020-8943-6244 Received: 22 March 2016; Accepted: 19 April 2016; Published: 21 April 2016 Abstract: This paper presents the estimation of bit error ratio (BER) from error vector magnitude (EVM) for M-ary quadrature amplitude modulation (QAM) formats in optical coherent systems employing carrier phase recovery with differential decoding to compensate for laser phase noise. Simulation results show that the relationship to estimate BER from EVM analysis for data-aided reception can also be applied to nondata-aided reception with a correction factor for different combined linewidth symbol duration product at the target BER of 10−3. It is demonstrated that the calibrated BER, which would otherwise be underestimated without the correction factor, can reliably monitor the performance of optical coherent systems near the target BER for quadrature phase shift keying (QPSK), 16-QAM, and 64-QAM. Keywords: error vector magnitude (EVM); quadrature amplitude modulation (QAM); carrier phase recovery 1. Introduction Spectrally-efficient modulation formats are currently receiving significant attention for next-generation high-capacity optical communication systems [1–3]. With information encoded on both the amplitude and phase of the optical carrier for M-ary quadrature amplitude modulation (QAM), accurate measurement analysis needs to be developed to quantify the novel modulation schemes and to reliably predict the performance of optical coherent systems. A number of performance metrics, such as bit error ratio (BER), eye diagram, Q-factor, and, more recently, error vector magnitude (EVM), can be used in optical communications to assess the quality of the transmitted signals [4–6]. -
Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth
11/08/2016 Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth Parent Category: 2014 HFE By Dennis Layne Introduction Receivers often contain narrow bandpass hardware filters as well as narrow lowpass filters implemented in digital signal processing (DSP). The equivalent noise bandwidth (ENBW) is a way to understand the noise floor that is present in these filters. To predict the sensitivity of a receiver design it is critical to understand noise including ENBW. This paper will cover each of the building block characteristics used to calculate receiver sensitivity and then put them together to make the calculation. Receiver Sensitivity Receiver sensitivity is a measure of the ability of a receiver to demodulate and get information from a weak signal. We quantify sensitivity as the lowest signal power level from which we can get useful information. In an Analog FM system the standard figure of merit for usable information is SINAD, a ratio of demodulated audio signal to noise. In digital systems receive signal quality is measured by calculating the ratio of bits received that are wrong to the total number of bits received. This is called Bit Error Rate (BER). Most Land Mobile radio systems use one of these figures of merit to quantify sensitivity. To measure sensitivity, we apply a desired signal and reduce the signal power until the quality threshold is met. SINAD SINAD is a term used for the Signal to Noise and Distortion ratio and is a type of audio signal to noise ratio. In an analog FM system, demodulated audio signal to noise ratio is an indication of RF signal quality. -
Signal-To-Noise Ratio and Dynamic Range Definitions
Signal-to-noise ratio and dynamic range definitions The Signal-to-Noise Ratio (SNR) and Dynamic Range (DR) are two common parameters used to specify the electrical performance of a spectrometer. This technical note will describe how they are defined and how to measure and calculate them. Figure 1: Definitions of SNR and SR. The signal out of the spectrometer is a digital signal between 0 and 2N-1, where N is the number of bits in the Analogue-to-Digital (A/D) converter on the electronics. Typical numbers for N range from 10 to 16 leading to maximum signal level between 1,023 and 65,535 counts. The Noise is the stochastic variation of the signal around a mean value. In Figure 1 we have shown a spectrum with a single peak in wavelength and time. As indicated on the figure the peak signal level will fluctuate a small amount around the mean value due to the noise of the electronics. Noise is measured by the Root-Mean-Squared (RMS) value of the fluctuations over time. The SNR is defined as the average over time of the peak signal divided by the RMS noise of the peak signal over the same time. In order to get an accurate result for the SNR it is generally required to measure over 25 -50 time samples of the spectrum. It is very important that your input to the spectrometer is constant during SNR measurements. Otherwise, you will be measuring other things like drift of you lamp power or time dependent signal levels from your sample. -
Image Denoising by Autoencoder: Learning Core Representations
Image Denoising by AutoEncoder: Learning Core Representations Zhenyu Zhao College of Engineering and Computer Science, The Australian National University, Australia, [email protected] Abstract. In this paper, we implement an image denoising method which can be generally used in all kinds of noisy images. We achieve denoising process by adding Gaussian noise to raw images and then feed them into AutoEncoder to learn its core representations(raw images itself or high-level representations).We use pre- trained classifier to test the quality of the representations with the classification accuracy. Our result shows that in task-specific classification neuron networks, the performance of the network with noisy input images is far below the preprocessing images that using denoising AutoEncoder. In the meanwhile, our experiments also show that the preprocessed images can achieve compatible result with the noiseless input images. Keywords: Image Denoising, Image Representations, Neuron Networks, Deep Learning, AutoEncoder. 1 Introduction 1.1 Image Denoising Image is the object that stores and reflects visual perception. Images are also important information carriers today. Acquisition channel and artificial editing are the two main ways that corrupt observed images. The goal of image restoration techniques [1] is to restore the original image from a noisy observation of it. Image denoising is common image restoration problems that are useful by to many industrial and scientific applications. Image denoising prob- lems arise when an image is corrupted by additive white Gaussian noise which is common result of many acquisition channels. The white Gaussian noise can be harmful to many image applications. Hence, it is of great importance to remove Gaussian noise from images. -
On the Bit Error Rate of Repeated Error-Correcting Codes
On the bit error rate of repeated error-correcting codes Weihao Gao and Yury Polyanskiy Abstract—Classically, error-correcting codes are studied with We emphasize that the last observation suggests that conven- respect to performance metrics such as minimum distance tional decoders of block codes should not be used in the cases (combinatorial) or probability of bit/block error over a given of significant defect densities. stochastic channel. In this paper, a different metric is considered. We proceed to discussing the basic framework and some It is assumed that the block code is used to repeatedly encode user data. The resulting stream is subject to adversarial noise of known results. given power, and the decoder is required to reproduce the data A. General Setting of Joint-Source Channel Coding with minimal possible bit-error rate. This setup may be viewed as a combinatorial joint source-channel coding. The aforementioned problem may be alternatively formal- Two basic results are shown for the achievable noise-distortion ized as a combinatorial (or adversarial) joint-source channel tradeoff: the optimal performance for decoders that are informed coding (JSCC) as proposed in [1]. The gist of it for the binary of the noise power, and global bounds for decoders operating in source and symmetric channel (BSSC) can be summarized by complete oblivion (with respect to noise level). General results the following are applied to the Hamming [7; 4; 3] code, for which it is k n Definition 1: Consider a pair of maps f : F2 ! F2 demonstrated (among other things) that no oblivious decoder n k exist that attains optimality for all noise levels simultaneously. -
AN10062 Phase Noise Measurement Guide for Oscillators
Phase Noise Measurement Guide for Oscillators Contents 1 Introduction ............................................................................................................................................. 1 2 What is phase noise ................................................................................................................................. 2 3 Methods of phase noise measurement ................................................................................................... 3 4 Connecting the signal to a phase noise analyzer ..................................................................................... 4 4.1 Signal level and thermal noise ......................................................................................................... 4 4.2 Active amplifiers and probes ........................................................................................................... 4 4.3 Oscillator output signal types .......................................................................................................... 5 4.3.1 Single ended LVCMOS ........................................................................................................... 5 4.3.2 Single ended Clipped Sine ..................................................................................................... 5 4.3.3 Differential outputs ............................................................................................................... 6 5 Setting up a phase noise analyzer ...........................................................................................................