A Slight Extension of Coherent Integration Loss Due to White Gaussian Phase Noise Mark A. Richards

Similar documents
The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

Let us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f.

1 Approximating Integrals using Taylor Polynomials

Monte Carlo Integration

Expectation and Variance of a random variable

Estimation for Complete Data

Problem Set 4 Due Oct, 12

Lecture 2: Monte Carlo Simulation

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes

4. Partial Sums and the Central Limit Theorem

1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series.

Quadratic Functions. Before we start looking at polynomials, we should know some common terminology.

Chapter 6. Sampling and Estimation

Estimation of the Mean and the ACVF

Filter banks. Separately, the lowpass and highpass filters are not invertible. removes the highest frequency 1/ 2and

Lecture 19. sup y 1,..., yn B d n

Orthogonal Gaussian Filters for Signal Processing

Entropy and Ergodic Theory Lecture 5: Joint typicality and conditional AEP

Frequentist Inference

Bayesian Methods: Introduction to Multi-parameter Models

Random Variables, Sampling and Estimation

Notes 19 Bessel Functions

THE KALMAN FILTER RAUL ROJAS

2.004 Dynamics and Control II Spring 2008

Stat 421-SP2012 Interval Estimation Section

Kernel density estimator

LECTURE 8: ASYMPTOTICS I

Reliability and Queueing

Bernoulli numbers and the Euler-Maclaurin summation formula

Area As A Limit & Sigma Notation

Spectral Analysis. This week in lab. Next classes: 3/26 and 3/28. Your next experiment Homework is to prepare

Design and Analysis of Algorithms

x 2 x x x x x + x x +2 x

Kolmogorov-Smirnov type Tests for Local Gaussianity in High-Frequency Data

ECE Spring Prof. David R. Jackson ECE Dept. Notes 8

Chapter 5.4 Practice Problems

Discrete Mathematics for CS Spring 2008 David Wagner Note 22

Optimally Sparse SVMs

Topic 9: Sampling Distributions of Estimators

17 Phonons and conduction electrons in solids (Hiroshi Matsuoka)

Standard Bayesian Approach to Quantized Measurements and Imprecise Likelihoods

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss

Statistical Noise Models and Diagnostics

Machine Learning Brett Bernstein

HARMONIC ANALYSIS FOR OPTICALLY MODULATING BODIES USING THE HARMONIC STRUCTURE FUNCTION (HSF) Lockheed Martin Hawaii

4 Conditional Distribution Estimation

Section 11.8: Power Series

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

Polynomial Functions and Their Graphs

RAINFALL PREDICTION BY WAVELET DECOMPOSITION

The Random Walk For Dummies

Session 5. (1) Principal component analysis and Karhunen-Loève transformation

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

(all terms are scalars).the minimization is clearer in sum notation:

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t

Linear Regression Models

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5

Computing Confidence Intervals for Sample Data

ECON 3150/4150, Spring term Lecture 3

Problem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t =

An Introduction to Randomized Algorithms

OPTIMAL PIECEWISE UNIFORM VECTOR QUANTIZATION OF THE MEMORYLESS LAPLACIAN SOURCE

B Supplemental Notes 2 Hypergeometric, Binomial, Poisson and Multinomial Random Variables and Borel Sets

Random Signals and Noise Winter Semester 2017 Problem Set 12 Wiener Filter Continuation

ECE 901 Lecture 14: Maximum Likelihood Estimation and Complexity Regularization

Statistics 511 Additional Materials

Lecture 19: Convergence

Sequential Monte Carlo Methods - A Review. Arnaud Doucet. Engineering Department, Cambridge University, UK

arxiv: v1 [math.ca] 29 Jun 2018

1 6 = 1 6 = + Factorials and Euler s Gamma function

Using the IML Procedure to Examine the Efficacy of a New Control Charting Technique

Math 2784 (or 2794W) University of Connecticut

Math 105: Review for Final Exam, Part II - SOLUTIONS

Frequency Response of FIR Filters

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Law of the sum of Bernoulli random variables

MDIV. Multiple divisor functions

SOLUTIONS: ECE 606 Homework Week 7 Mark Lundstrom Purdue University (revised 3/27/13) e E i E T

FIR Filter Design: Part II

CS284A: Representations and Algorithms in Molecular Biology

On the Beta Cumulative Distribution Function

1 Introduction to reducing variance in Monte Carlo simulations

n n i=1 Often we also need to estimate the variance. Below are three estimators each of which is optimal in some sense: n 1 i=1 k=1 i=1 k=1 i=1 k=1

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1

Hauptman and Karle Joint and Conditional Probability Distributions. Robert H. Blessing, HWI/UB Structural Biology Department, January 2003 ( )

Chapter 10: Power Series

The Poisson Process *

Chapter 9 - CD companion 1. A Generic Implementation; The Common-Merge Amplifier. 1 τ is. ω ch. τ io

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula

Direction: This test is worth 250 points. You are required to complete this test within 50 minutes.

A Simple Probabilistic Explanation of Term Frequency-Inverse Document Frequency (tf-idf) Heuristic (and Variations Motivated by This Explanation)

STATISTICAL INFERENCE

1.010 Uncertainty in Engineering Fall 2008

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4

MATH/STAT 352: Lecture 15

Probability of error for LDPC OC with one co-channel Interferer over i.i.d Rayleigh Fading

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

Machine Learning Assignment-1

Transcription:

A Slight Extesio of Coheret Itegratio Loss Due to White Gaussia Phase oise Mark A. Richards March 3, Goal I [], the itegratio loss L i computig the coheret sum of samples x with weights a is cosidered. The coheret sum w is w= ax () = Whe the samples are cotamiated by statioary white Gaussia phase oise of variace σ, L is give by the formula L = σ + = = m= m = m a e aa a () Referece [] is icluded i its etirety i Sectio 5 of this ote, ad i the remaider we refer to the derivatios ad equatios there to avoid repeatig them. As oted i [], the Gaussia probability desity fuctio (PDF) assumed for the phase oise is ot physically possible, sice it is ot cofied to the iterval [,). While the Gaussia PDF is still a very good approximatio for small σ, i this ote we add a small extesio to that aalysis by re-derivig the result for a more realistic phase oise PDF. The Phase oise PDF The PDF of iterest is the Tikhoov distributio give by Va Trees []. I the otatio of [], this is αcosφ pφ ( φ; α) = e I (3) ( α) This PDF evidetly describes the statistics of the steady state phase estimate of a first order phase locked loop trackig a siusoid i additive white Gaussia oise. For our A Slight Extesio to Coheret Itegratio Page of March 3,

purposes, it provides a family of phase PDFs that vary from uiform (α = ) to, i the limit, a oradom phase of zero as α, but is always cofied to [,) ad thus is always a strictly valid PDF for phase. Thus, we ca cosider the effect of varyig degrees of phase oise, previously achieved by varyig the variace i the Gaussia PDF model of [], by istead varyig α i the Tikhoov PDF. Figure illustrates the Tikhoov PDF for several differet values of the parameter α. Relative Probability 3.5.5.5 α = 5 α = 5 α = α = -3 - - 3 θ (radias) Figure. The Tikhoov PDF for phase. 3 Coheret Itegratio Loss Followig the process i [], we eed to compute the quatity Φ, which is φ jφ αcosφ α j { } I ( ) Φ= E e = e e dφ (4) The itegral portio of Eq. (4) ca be put ito a more coveiet form as follows: A Slight Extesio to Coheret Itegratio Page of March 3,

jφ αcosφ jφ αcosφ jφ αcosφ φ = φ + φ e e d e e d e e d jφ αcos ( φ ) jφ αcosφ = e e dφ + e e dφ jφ αcosφ jφ αcosφ αcosφ e e dφ e e dφ cos( φ) e dφ = + = = I ( α) The last step follows from oe itegral form of the defiitio of the modified Bessel fuctio of the first kid [3]. Thus, ( α ) ( α ) I Φ= (6) I It the follows from the argumet i [] that the coheret itegratio loss is (5) L = I ( α ) a + ( ) = I α = m= m = a m aa (7) ad, for the case where all the a, ( α) ( α) ( ) + I I L = (8) Fially, whe is large, ( α) ( α) L I I, (9) Figure illustrates the loss for the case where all a =, i.e., Eq. (8), as a fuctio of the umber of samples itegrated ad the PDF spread parameter α. For large α (phase early costat), the loss approaches L = ( db), as would be expected. As the phase PDF becomes more spread (α approachig zero), idicatig a greater degree of phase oise, the loss icreases, becomig relatively severe for α < 5. A Slight Extesio to Coheret Itegratio Page 3 of 3 March 3,

L.9.8.7.6.5.4 = 5 L (db) - -4-6 -8 = 5.3 -.. - 5 5 α -4 α Figure. Loss as a fuctio of α ad. Left: liear scale. Right: decibel scale. The radom phase case correspods to α =. For this case, Eq. (8) shows that the loss becomes, for a =, L = /. This example shows that whe the phase of the summed samples are all radom, they add o a power basis istead of a voltage basis. That is, the power of the sum is times the power of a sigle sample, istead of, a reductio i gai by the factor. This, of course, is exactly the way radom oise samples behave. 4 Refereces [] M. A. Richards, Coheret Itegratio, IEEE Sigal Processig Letters, vol., o. 7, pp. 8-, July 3. [] H. L. Va Trees, Detectio, Estimatio, ad Modulatio Theory, Part, Wiley, 968, p. 338. [3] M. Abramowitz ad I. A. Stegu, editors, Hadbook of Mathematical Fuctios. U.S. atioal Bureau of Stadards, Applied Mathematics Series, th pritig, Dec. 97. See formula 9.6.9, p. 376. 5 Origial Paper A copy of referece [] is icluded i the ext three pages. A trivial correctio cocers Eq. (5) i []. That expressio for L is ot i decibels, so the db at the ed of the equatio should be deleted. A Slight Extesio to Coheret Itegratio Page 4 of 4 March 3,

A Slight Extesio to Coheret Itegratio Page 5 of 5 March 3,

A Slight Extesio to Coheret Itegratio Page 6 of 6 March 3,

A Slight Extesio to Coheret Itegratio Page 7 of 7 March 3,