Introduction to Constrained Estimation
|
|
- Clemence Summers
- 5 years ago
- Views:
Transcription
1 Introduction to Constrained Estimation Graham C. Goodwin September 2004
2 2.1 Background Constraints are also often present in estimation problems. A classical example of a constrained estimation problem is the case in which binary data (say ±1) are transmitted through a communication channel where it suffers dispersion causing the data to overlay itself. In the field of communications, this is commonly referred to as intersymbol interference [ISI]. The associated estimation problem is: Given the output of the channel, provide an estimate of the transmitted signal.
3 To illustrate some of the ideas involved in the above problem, let us assume, for simplicity, that the intersymbol interference produced by the channel can be modelled via a finite impulse response [FIR] model of the form: m y k = g l u k l + n k, (1) l=0 where y k, u k, n k denote the channel output, input and noise.
4 Heuristically, one might expect that one should invert the channel so as to recover the input sequence {u k } from a given sequence of output data {y k }. Such an inverse can be readily found by utilising feedback ideas.
5 Expand the channel transfer function as: G(z) = g g m z m = g 0 + G(z), then we can form an inverse by the feedback circuit shown in Figure 1.
6 Figure 1 placements y k + 1 g 0 ũ k G(z) Figure: Feedback inverse circuit.
7 To verify that the circuit of Figure 1 does, indeed, produce an inverse, we see that the transfer function from y k to ũ k is T(z) = 1 g G(z) g 0 = 1 g 0 + G(z) = 1 G(z).
8 Running Example Consider the channel model y k = u k 1.7u k u k 2 + n k, where u k is a random binary signal and n k is an independent identically distributed [i.i.d.] noise having a Gaussian distribution of variance σ 2.
9 Figure uk, ũk g replacements Figure: Data u k (circle-solid line) and estimate ũ k (triangle-solid line) using the feedback inverse circuit of Figure 1. Noise variance: σ 2 = 0. k
10 Next, we simulate the inversion estimator when the received signal is affected by noise n k of variance σ 2 = 0.1.
11 Figure uk, ũk g replacements Figure: Data u k (circle-solid line) and estimate ũ k (triangle-solid line) using the feedback inverse circuit of Figure 1. Noise variance: σ 2 = 0.1. k
12 An improvement seems to be to simply take the nearest value from the set {+1, 1} corresponding to ũ k. This leads to the circuit shown in Figure 4, where +1 if ũ k 0, sign(ũ k ) 1 if ũ k < 0.
13 Figure 4 placements y k + 1 ũ k sign g 0 û k G(z) Figure: Constrained feedback inverse circuit.
14 Figure uk, ûk g replacements Figure: Data u k (circle-solid line) and estimate û k (triangle-solid line) using the constrained feedback inverse circuit of Figure 4. Noise variance: σ 2 = 0.1. k
15 Our belief is that û k should be a better estimate of the input than ũ k since we have forced the constraint û k {+1, 1}. This suggests that we could try feeding back û k instead of ũ k, as shown in Figure 6. This is called a Decision Feedback Equaliser (DFE) in the Communications Literature.
16 Figure 6 placements y k + 1 g 0 sign û k G(z) Figure: Constrained estimation with decision feedback, or decision feedback equaliser [DFE].
17 Figure uk, ûk g replacements Figure: Data u k (circle-solid line) and estimate û k (triangle-solid line) using the DFE of Figure 6. Noise variance: σ 2 = 0.1. k
18 We see that this circuit has led to perfect recovery of the transmitted data! One might wonder if the DFE circuit would always perform so well. We next investigate the performance of the DFE of Figure 6 when the noise variance is increased by a factor of 2; that is, σ 2 = 0.2.
19 Figure uk, ûk g replacements Figure: Data u k (circle-solid line) and estimate û k (triangle-solid line) using the DFE of Figure 6. Noise variance: σ 2 = 0.2. k
20 We see that the circuit now performs badly in the case of increased measurement noise. We can gain some insight as to from where further improvements might come by expressing the result shown in Figure 6 as the solution to an optimisation problem. Specifically, assume that we are given (estimates of) past values of the input, {û k 1,..., û k m,...}, and that we model the output ŷ k as ŷ k = g 0 u k + g 1û k g m û k m.
21 We can now ask what value of u k causes ŷ k to be, at time k, as close as possible to the observed output y k. We measure how close ŷ k is to y k by the following one-step objective function: V 1 (ŷ k, u k ) = [y k ŷ k ] 2. We also require that u {+1, 1}. k
22 The solution to this constrained optimisation problem is readily seen to be: { } 1 û k = sign [y k g 1 û k 1... g m û k m ]. (2) g 0 The above is the DFE.
23 Generalise to the following two-stage objective function: where V 2 (ŷ k, ŷ k+1, u k, u k+1 ) = [y k ŷ k ] 2 + [y k+1 ŷ k+1 ] 2, (3) ŷ k = g 0 u k + g 1û k g m û k m, (4) ŷ k+1 = g 0 u k+1 + g 1u k + g 2û k g m û k m+1, (5) and where the past estimates {û k 1, û k 2,...} are again assumed fixed and known.
24 The solution to the above problem can be readily computed by simple evaluation of V 2 for all possible constrained inputs; that is, for {u k, u k+1 } { { 1, 1}, { 1, 1}, {1, 1}, {1, 1} }. (6)
25 We could then fix the estimate of u k (denoted û k ) as the first element of the solution to this optimisation problem. We might then proceed to measure y k+2 and re-estimate u k+1, plus obtain a fresh estimate of u k+2 by minimising: where V 2 (ŷ k+1, ŷ k+2, u k+1, u k+2 ) = [y k+1 ŷ k+1 ] 2 + [y k+2 ŷ k+2 ] 2, ŷ k+1 = g 0 u k+1 + g 1û k g m û k m+1, ŷ k+2 = g 0 u k+2 + g 1u k+1 + g 2û k g m û k m+2,
26 By the above procedure, we are already generating constrained estimates via a moving horizon estimator [MHE] subject to the constraint u {+1, 1}. k
27 The corresponding simulation results, for noise variance σ 2 = 0.2, are shown in Figure 9.
28 Figure uk, ûk g replacements Figure: Data u k (circle-solid line) and estimate û k (triangle-solid line) using the moving horizon two-step estimator. Noise variance: σ 2 = 0.2. k
29 Connections Between Constrained Control and Estimation The brief introduction to constrained control and estimation given above will have, no doubt, left the reader with the impression that these two problems are, at least, very similar. Indeed, both have been cast as finite horizon constrained optimisation problems. We will see later that these problems lead to the same underlying question, the only difference being a rather minor issue associated with the boundary conditions.
30 Actually, we will show that a strong connection between constrained control and estimation problems is revealed when looked upon via a Lagrangian duality perspective. This will be the topic of the Lecture 2 of Friday.
RADIO SYSTEMS ETIN15. Lecture no: Equalization. Ove Edfors, Department of Electrical and Information Technology
RADIO SYSTEMS ETIN15 Lecture no: 8 Equalization Ove Edfors, Department of Electrical and Information Technology Ove.Edfors@eit.lth.se Contents Inter-symbol interference Linear equalizers Decision-feedback
More informationGEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems
GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems Aids Allowed: 2 8 1/2 X11 crib sheets, calculator DATE: Tuesday
More informationAn Adaptive Blind Channel Shortening Algorithm for MCM Systems
Hacettepe University Department of Electrical and Electronics Engineering An Adaptive Blind Channel Shortening Algorithm for MCM Systems Blind, Adaptive Channel Shortening Equalizer Algorithm which provides
More informationOn-off Control: Audio Applications
On-off Control: Audio Applications Graham C. Goodwin Day 4: Lecture 3 16th September 2004 International Summer School Grenoble, France 1 Background In this lecture we address the issue of control when
More informationDetermining the Optimal Decision Delay Parameter for a Linear Equalizer
International Journal of Automation and Computing 1 (2005) 20-24 Determining the Optimal Decision Delay Parameter for a Linear Equalizer Eng Siong Chng School of Computer Engineering, Nanyang Technological
More informationLeast Squares Regression
E0 70 Machine Learning Lecture 4 Jan 7, 03) Least Squares Regression Lecturer: Shivani Agarwal Disclaimer: These notes are a brief summary of the topics covered in the lecture. They are not a substitute
More informationValue-Ordering and Discrepancies. Ciaran McCreesh and Patrick Prosser
Value-Ordering and Discrepancies Maintaining Arc Consistency (MAC) Achieve (generalised) arc consistency (AC3, etc). If we have a domain wipeout, backtrack. If all domains have one value, we re done. Pick
More informationSimilarities of PMD and DMD for 10Gbps Equalization
Similarities of PMD and DMD for 10Gbps Equalization Moe Win Jack Winters win/jhw@research.att.com AT&T Labs-Research (Some viewgraphs and results curtesy of Julien Porrier) Outline Polarization Mode Dispersion
More informationSingle-Carrier Block Transmission With Frequency-Domain Equalisation
ELEC6014 RCNSs: Additional Topic Notes Single-Carrier Block Transmission With Frequency-Domain Equalisation Professor Sheng Chen School of Electronics and Computer Science University of Southampton Southampton
More informationOne Lesson of Information Theory
Institut für One Lesson of Information Theory Prof. Dr.-Ing. Volker Kühn Institute of Communications Engineering University of Rostock, Germany Email: volker.kuehn@uni-rostock.de http://www.int.uni-rostock.de/
More information4 An Introduction to Channel Coding and Decoding over BSC
4 An Introduction to Channel Coding and Decoding over BSC 4.1. Recall that channel coding introduces, in a controlled manner, some redundancy in the (binary information sequence that can be used at the
More informationRevision of Lecture 4
Revision of Lecture 4 We have discussed all basic components of MODEM Pulse shaping Tx/Rx filter pair Modulator/demodulator Bits map symbols Discussions assume ideal channel, and for dispersive channel
More informationData Detection for Controlled ISI. h(nt) = 1 for n=0,1 and zero otherwise.
Data Detection for Controlled ISI *Symbol by symbol suboptimum detection For the duobinary signal pulse h(nt) = 1 for n=0,1 and zero otherwise. The samples at the output of the receiving filter(demodulator)
More information18.2 Continuous Alphabet (discrete-time, memoryless) Channel
0-704: Information Processing and Learning Spring 0 Lecture 8: Gaussian channel, Parallel channels and Rate-distortion theory Lecturer: Aarti Singh Scribe: Danai Koutra Disclaimer: These notes have not
More information6196 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 9, SEPTEMBER 2011
6196 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 9, SEPTEMBER 2011 On the Structure of Real-Time Encoding and Decoding Functions in a Multiterminal Communication System Ashutosh Nayyar, Student
More informationACOMMUNICATION situation where a single transmitter
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 9, SEPTEMBER 2004 1875 Sum Capacity of Gaussian Vector Broadcast Channels Wei Yu, Member, IEEE, and John M. Cioffi, Fellow, IEEE Abstract This paper
More informationADAPTIVE FILTER ALGORITHMS. Prepared by Deepa.T, Asst.Prof. /TCE
ADAPTIVE FILTER ALGORITHMS Prepared by Deepa.T, Asst.Prof. /TCE Equalization Techniques Fig.3 Classification of equalizers Equalizer Techniques Linear transversal equalizer (LTE, made up of tapped delay
More informationMMSE DECISION FEEDBACK EQUALIZER FROM CHANNEL ESTIMATE
MMSE DECISION FEEDBACK EQUALIZER FROM CHANNEL ESTIMATE M. Magarini, A. Spalvieri, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano (Italy),
More informationEE5713 : Advanced Digital Communications
EE5713 : Advanced Digital Communications Week 12, 13: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Equalization (On Board) 20-May-15 Muhammad
More informationLinear Optimum Filtering: Statement
Ch2: Wiener Filters Optimal filters for stationary stochastic models are reviewed and derived in this presentation. Contents: Linear optimal filtering Principle of orthogonality Minimum mean squared error
More informationThis examination consists of 11 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS
THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 6 December 2006 This examination consists of
More informationApproximate ML Decision Feedback Block. Equalizer for Doubly Selective Fading Channels
Approximate ML Decision Feedback Block 1 Equalizer for Doubly Selective Fading Channels Lingyang Song, Rodrigo C. de Lamare, Are Hjørungnes, and Alister G. Burr arxiv:1112.0725v1 [cs.it] 4 Dec 2011 Abstract
More informationCS711008Z Algorithm Design and Analysis
CS711008Z Algorithm Design and Analysis Lecture 8 Linear programming: interior point method Dongbo Bu Institute of Computing Technology Chinese Academy of Sciences, Beijing, China 1 / 31 Outline Brief
More informationCommunication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi
Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 41 Pulse Code Modulation (PCM) So, if you remember we have been talking
More informationCS 6901 (Applied Algorithms) Lecture 3
CS 6901 (Applied Algorithms) Lecture 3 Antonina Kolokolova September 16, 2014 1 Representative problems: brief overview In this lecture we will look at several problems which, although look somewhat similar
More informationChannel Estimation with Low-Precision Analog-to-Digital Conversion
Channel Estimation with Low-Precision Analog-to-Digital Conversion Onkar Dabeer School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai India Email: onkar@tcs.tifr.res.in
More informationLeast Squares with Examples in Signal Processing 1. 2 Overdetermined equations. 1 Notation. The sum of squares of x is denoted by x 2 2, i.e.
Least Squares with Eamples in Signal Processing Ivan Selesnick March 7, 3 NYU-Poly These notes address (approimate) solutions to linear equations by least squares We deal with the easy case wherein the
More informationOne-Bit LDPC Message Passing Decoding Based on Maximization of Mutual Information
One-Bit LDPC Message Passing Decoding Based on Maximization of Mutual Information ZOU Sheng and Brian M. Kurkoski kurkoski@ice.uec.ac.jp University of Electro-Communications Tokyo, Japan University of
More informationSoft-Output Decision-Feedback Equalization with a Priori Information
Soft-Output Decision-Feedback Equalization with a Priori Information Renato R. opes and John R. Barry School of Electrical and Computer Engineering Georgia Institute of Technology, Atlanta, Georgia 333-5
More informationDirect-Sequence Spread-Spectrum
Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.
More informationEs e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0
Problem 6.15 : he received signal-plus-noise vector at the output of the matched filter may be represented as (see (5-2-63) for example) : r n = E s e j(θn φ) + N n where θ n =0,π/2,π,3π/2 for QPSK, and
More informationShallow Water Fluctuations and Communications
Shallow Water Fluctuations and Communications H.C. Song Marine Physical Laboratory Scripps Institution of oceanography La Jolla, CA 92093-0238 phone: (858) 534-0954 fax: (858) 534-7641 email: hcsong@mpl.ucsd.edu
More informationTurbo Codes are Low Density Parity Check Codes
Turbo Codes are Low Density Parity Check Codes David J. C. MacKay July 5, 00 Draft 0., not for distribution! (First draft written July 5, 998) Abstract Turbo codes and Gallager codes (also known as low
More informationApproximately achieving the feedback interference channel capacity with point-to-point codes
Approximately achieving the feedback interference channel capacity with point-to-point codes Joyson Sebastian*, Can Karakus*, Suhas Diggavi* Abstract Superposition codes with rate-splitting have been used
More informationCross Directional Control
Cross Directional Control Graham C. Goodwin Day 4: Lecture 4 16th September 2004 International Summer School Grenoble, France 1. Introduction In this lecture we describe a practical application of receding
More informationEE4601 Communication Systems
EE4601 Communication Systems Week 13 Linear Zero Forcing Equalization 0 c 2012, Georgia Institute of Technology (lect13 1) Equalization The cascade of the transmit filter g(t), channel c(t), receiver filter
More informationLeast Squares Regression
CIS 50: Machine Learning Spring 08: Lecture 4 Least Squares Regression Lecturer: Shivani Agarwal Disclaimer: These notes are designed to be a supplement to the lecture. They may or may not cover all the
More informationRevision of Lecture 4
Revision of Lecture 4 We have completed studying digital sources from information theory viewpoint We have learnt all fundamental principles for source coding, provided by information theory Practical
More information4.0 Update Algorithms For Linear Closed-Loop Systems
4. Update Algorithms For Linear Closed-Loop Systems A controller design methodology has been developed that combines an adaptive finite impulse response (FIR) filter with feedback. FIR filters are used
More informationLQR, Kalman Filter, and LQG. Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin
LQR, Kalman Filter, and LQG Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin May 2015 Linear Quadratic Regulator (LQR) Consider a linear system
More information(Refer Slide Time: )
Digital Signal Processing Prof. S. C. Dutta Roy Department of Electrical Engineering Indian Institute of Technology, Delhi FIR Lattice Synthesis Lecture - 32 This is the 32nd lecture and our topic for
More informationBLIND DECONVOLUTION ALGORITHMS FOR MIMO-FIR SYSTEMS DRIVEN BY FOURTH-ORDER COLORED SIGNALS
BLIND DECONVOLUTION ALGORITHMS FOR MIMO-FIR SYSTEMS DRIVEN BY FOURTH-ORDER COLORED SIGNALS M. Kawamoto 1,2, Y. Inouye 1, A. Mansour 2, and R.-W. Liu 3 1. Department of Electronic and Control Systems Engineering,
More informationCoding for Digital Communication and Beyond Fall 2013 Anant Sahai MT 1
EECS 121 Coding for Digital Communication and Beyond Fall 2013 Anant Sahai MT 1 PRINT your student ID: PRINT AND SIGN your name:, (last) (first) (signature) PRINT your Unix account login: ee121- Prob.
More informationMMSE Decision Feedback Equalization of Pulse Position Modulated Signals
SE Decision Feedback Equalization of Pulse Position odulated Signals AG Klein and CR Johnson, Jr School of Electrical and Computer Engineering Cornell University, Ithaca, NY 4853 email: agk5@cornelledu
More informationECE 564/645 - Digital Communications, Spring 2018 Homework #2 Due: March 19 (In Lecture)
ECE 564/645 - Digital Communications, Spring 018 Homework # Due: March 19 (In Lecture) 1. Consider a binary communication system over a 1-dimensional vector channel where message m 1 is sent by signaling
More informationInformation Structures, the Witsenhausen Counterexample, and Communicating Using Actions
Information Structures, the Witsenhausen Counterexample, and Communicating Using Actions Pulkit Grover, Carnegie Mellon University Abstract The concept of information-structures in decentralized control
More informationDigital Baseband Systems. Reference: Digital Communications John G. Proakis
Digital Baseband Systems Reference: Digital Communications John G. Proais Baseband Pulse Transmission Baseband digital signals - signals whose spectrum extend down to or near zero frequency. Model of the
More informationFlat Rayleigh fading. Assume a single tap model with G 0,m = G m. Assume G m is circ. symmetric Gaussian with E[ G m 2 ]=1.
Flat Rayleigh fading Assume a single tap model with G 0,m = G m. Assume G m is circ. symmetric Gaussian with E[ G m 2 ]=1. The magnitude is Rayleigh with f Gm ( g ) =2 g exp{ g 2 } ; g 0 f( g ) g R(G m
More informationWeiyao Lin. Shanghai Jiao Tong University. Chapter 5: Digital Transmission through Baseband slchannels Textbook: Ch
Principles of Communications Weiyao Lin Shanghai Jiao Tong University Chapter 5: Digital Transmission through Baseband slchannels Textbook: Ch 10.1-10.5 2009/2010 Meixia Tao @ SJTU 1 Topics to be Covered
More informationShannon meets Wiener II: On MMSE estimation in successive decoding schemes
Shannon meets Wiener II: On MMSE estimation in successive decoding schemes G. David Forney, Jr. MIT Cambridge, MA 0239 USA forneyd@comcast.net Abstract We continue to discuss why MMSE estimation arises
More informationIN this paper, we show that the scalar Gaussian multiple-access
768 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 5, MAY 2004 On the Duality of Gaussian Multiple-Access and Broadcast Channels Nihar Jindal, Student Member, IEEE, Sriram Vishwanath, and Andrea
More informationLecture 7. Union bound for reducing M-ary to binary hypothesis testing
Lecture 7 Agenda for the lecture M-ary hypothesis testing and the MAP rule Union bound for reducing M-ary to binary hypothesis testing Introduction of the channel coding problem 7.1 M-ary hypothesis testing
More informationPerformance of Optimal Digital Page Detection in a Two-Dimensional ISI/AWGN Channel
c IEEE 1996 Presented at Asilomar Conf. on Signals, Systems and Comp., Nov. 1996 Performance of Optimal Digital Page Detection in a Two-Dimensional ISI/AWGN Channel Keith M. Chugg Department of Electrical
More informationNotes on Discriminant Functions and Optimal Classification
Notes on Discriminant Functions and Optimal Classification Padhraic Smyth, Department of Computer Science University of California, Irvine c 2017 1 Discriminant Functions Consider a classification problem
More informationDigital Communications
Digital Communications Chapter 9 Digital Communications Through Band-Limited Channels Po-Ning Chen, Professor Institute of Communications Engineering National Chiao-Tung University, Taiwan Digital Communications:
More informationConstrained Detection for Multiple-Input Multiple-Output Channels
Constrained Detection for Multiple-Input Multiple-Output Channels Tao Cui, Chintha Tellambura and Yue Wu Department of Electrical and Computer Engineering University of Alberta Edmonton, AB, Canada T6G
More informationBASICS OF DETECTION AND ESTIMATION THEORY
BASICS OF DETECTION AND ESTIMATION THEORY 83050E/158 In this chapter we discuss how the transmitted symbols are detected optimally from a noisy received signal (observation). Based on these results, optimal
More informationAppendix B Information theory from first principles
Appendix B Information theory from first principles This appendix discusses the information theory behind the capacity expressions used in the book. Section 8.3.4 is the only part of the book that supposes
More informationLecture 6 Channel Coding over Continuous Channels
Lecture 6 Channel Coding over Continuous Channels I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw November 9, 015 1 / 59 I-Hsiang Wang IT Lecture 6 We have
More informationAnalog Electronics 2 ICS905
Analog Electronics 2 ICS905 G. Rodriguez-Guisantes Dépt. COMELEC http://perso.telecom-paristech.fr/ rodrigez/ens/cycle_master/ November 2016 2/ 67 Schedule Radio channel characteristics ; Analysis and
More informationIssues with sampling time and jitter in Annex 93A. Adam Healey IEEE P802.3bj Task Force May 2013
Issues with sampling time and jitter in Annex 93A Adam Healey IEEE P802.3bj Task Force May 2013 Part 1: Jitter (comment #157) 2 Treatment of jitter in COM Draft 2.0 h (0) (t s ) slope h(0) (t s ) 1 UI
More informationProblem Set 7 Due March, 22
EE16: Probability and Random Processes SP 07 Problem Set 7 Due March, Lecturer: Jean C. Walrand GSI: Daniel Preda, Assane Gueye Problem 7.1. Let u and v be independent, standard normal random variables
More informationMMSE Equalizer Design
MMSE Equalizer Design Phil Schniter March 6, 2008 [k] a[m] P a [k] g[k] m[k] h[k] + ṽ[k] q[k] y [k] P y[m] For a trivial channel (i.e., h[k] = δ[k]), e kno that the use of square-root raisedcosine (SRRC)
More informationIntroduction to Machine Learning Prof. Sudeshna Sarkar Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur
Introduction to Machine Learning Prof. Sudeshna Sarkar Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Module 2 Lecture 05 Linear Regression Good morning, welcome
More informationAdvanced 3G and 4G Wireless Communication Prof. Aditya K. Jagannatham Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3G and 4G Wireless Communication Prof. Aditya K. Jagannatham Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 12 Doppler Spectrum and Jakes Model Welcome to
More informationLECTURE 5 Noise and ISI
MIT 6.02 DRAFT Lecture Notes Spring 2010 (Last update: February 25, 2010) Comments, questions or bug reports? Please contact 6.02-staff@mit.edu LECTURE 5 Noise and ISI If there is intersymbol interference
More informationAdaptive MMSE Equalizer with Optimum Tap-length and Decision Delay
Adaptive MMSE Equalizer with Optimum Tap-length and Decision Delay Yu Gong, Xia Hong and Khalid F. Abu-Salim School of Systems Engineering The University of Reading, Reading RG6 6AY, UK E-mail: {y.gong,x.hong,k.f.abusalem}@reading.ac.uk
More informationCapacity of a Two-way Function Multicast Channel
Capacity of a Two-way Function Multicast Channel 1 Seiyun Shin, Student Member, IEEE and Changho Suh, Member, IEEE Abstract We explore the role of interaction for the problem of reliable computation over
More informationITCT Lecture IV.3: Markov Processes and Sources with Memory
ITCT Lecture IV.3: Markov Processes and Sources with Memory 4. Markov Processes Thus far, we have been occupied with memoryless sources and channels. We must now turn our attention to sources with memory.
More informationBlind Channel Equalization in Impulse Noise
Blind Channel Equalization in Impulse Noise Rubaiyat Yasmin and Tetsuya Shimamura Graduate School of Science and Engineering, Saitama University 255 Shimo-okubo, Sakura-ku, Saitama 338-8570, Japan yasmin@sie.ics.saitama-u.ac.jp
More informationMachine Learning, Fall 2009: Midterm
10-601 Machine Learning, Fall 009: Midterm Monday, November nd hours 1. Personal info: Name: Andrew account: E-mail address:. You are permitted two pages of notes and a calculator. Please turn off all
More informationIntroduction to Bayesian Learning. Machine Learning Fall 2018
Introduction to Bayesian Learning Machine Learning Fall 2018 1 What we have seen so far What does it mean to learn? Mistake-driven learning Learning by counting (and bounding) number of mistakes PAC learnability
More informationCapacity Penalty due to Ideal Zero-Forcing Decision-Feedback Equalization
Capacity Penalty due to Ideal Zero-Forcing Decision-Feedback Equalization John R. Barry, Edward A. Lee, and David. Messerschmitt John R. Barry, School of Electrical Engineering, eorgia Institute of Technology,
More informationComputation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems
Computation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems Department of Electrical Engineering, College of Engineering, Basrah University Basrah Iraq,
More informationLECTURE 5 Noise and ISI
MIT 6.02 DRAFT Lecture Notes Spring 2010 (Last update: February 22, 2010) Comments, questions or bug reports? Please contact 6.02-staff@mit.edu LECTURE 5 Noise and ISI Sometimes theory tells you: Stop
More informationIEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 2, FEBRUARY Uplink Downlink Duality Via Minimax Duality. Wei Yu, Member, IEEE (1) (2)
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 2, FEBRUARY 2006 361 Uplink Downlink Duality Via Minimax Duality Wei Yu, Member, IEEE Abstract The sum capacity of a Gaussian vector broadcast channel
More informationLecture 10: Broadcast Channel and Superposition Coding
Lecture 10: Broadcast Channel and Superposition Coding Scribed by: Zhe Yao 1 Broadcast channel M 0M 1M P{y 1 y x} M M 01 1 M M 0 The capacity of the broadcast channel depends only on the marginal conditional
More informationApplication of Matched Filter
Application of Matched Filter Lecture No. 12 Dr. Aoife Moloney School of Electronics and Communications Dublin Institute of Technology Overview This lecture will look at the following: Matched filter vs
More informationECE531: Principles of Detection and Estimation Course Introduction
ECE531: Principles of Detection and Estimation Course Introduction D. Richard Brown III WPI 22-January-2009 WPI D. Richard Brown III 22-January-2009 1 / 37 Lecture 1 Major Topics 1. Web page. 2. Syllabus
More informationMultiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks. Ji an Luo
Multiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks Ji an Luo 2008.6.6 Outline Background Problem Statement Main Results Simulation Study Conclusion Background Wireless
More informationInformation Theory. Lecture 10. Network Information Theory (CT15); a focus on channel capacity results
Information Theory Lecture 10 Network Information Theory (CT15); a focus on channel capacity results The (two-user) multiple access channel (15.3) The (two-user) broadcast channel (15.6) The relay channel
More informationDigital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10
Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,
More informationStatistical and Adaptive Signal Processing
r Statistical and Adaptive Signal Processing Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing Dimitris G. Manolakis Massachusetts Institute of Technology Lincoln Laboratory
More informationNetwork Control: A Rate-Distortion Perspective
Network Control: A Rate-Distortion Perspective Jubin Jose and Sriram Vishwanath Dept. of Electrical and Computer Engineering The University of Texas at Austin {jubin, sriram}@austin.utexas.edu arxiv:8.44v2
More informationLine Codes and Pulse Shaping Review. Intersymbol interference (ISI) Pulse shaping to reduce ISI Embracing ISI
Line Codes and Pulse Shaping Review Line codes Pulse width and polarity Power spectral density Intersymbol interference (ISI) Pulse shaping to reduce ISI Embracing ISI Line Code Examples (review) on-off
More informationOn the Shamai-Laroia Approximation for the Information Rate of the ISI Channel
On the Shamai-Laroia Approximation for the Information Rate of the ISI Channel Yair Carmon and Shlomo Shamai (Shitz) Department of Electrical Engineering, Technion - Israel Institute of Technology 2014
More informationBayesian Inference: Principles and Practice 3. Sparse Bayesian Models and the Relevance Vector Machine
Bayesian Inference: Principles and Practice 3. Sparse Bayesian Models and the Relevance Vector Machine Mike Tipping Gaussian prior Marginal prior: single α Independent α Cambridge, UK Lecture 3: Overview
More informationDesigning Information Devices and Systems I Fall 2018 Lecture Notes Note 6
EECS 16A Designing Information Devices and Systems I Fall 2018 Lecture Notes Note 6 6.1 Introduction: Matrix Inversion In the last note, we considered a system of pumps and reservoirs where the water in
More informationRalf Koetter, Andrew C. Singer, and Michael Tüchler
Ralf Koetter, Andrew C. Singer, and Michael Tüchler Capitalizing on the tremendous performance gains of turbo codes and the turbo decoding algorithm, turbo equalization is an iterative equalization and
More informationMachine Learning. A Bayesian and Optimization Perspective. Academic Press, Sergios Theodoridis 1. of Athens, Athens, Greece.
Machine Learning A Bayesian and Optimization Perspective Academic Press, 2015 Sergios Theodoridis 1 1 Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens,
More informationSupport Vector Machines for Classification and Regression. 1 Linearly Separable Data: Hard Margin SVMs
E0 270 Machine Learning Lecture 5 (Jan 22, 203) Support Vector Machines for Classification and Regression Lecturer: Shivani Agarwal Disclaimer: These notes are a brief summary of the topics covered in
More informationDiversity Multiplexing Tradeoff in ISI Channels Leonard H. Grokop, Member, IEEE, and David N. C. Tse, Senior Member, IEEE
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 55, NO 1, JANUARY 2009 109 Diversity Multiplexing Tradeoff in ISI Channels Leonard H Grokop, Member, IEEE, and David N C Tse, Senior Member, IEEE Abstract The
More informationLECTURE 18. Lecture outline Gaussian channels: parallel colored noise inter-symbol interference general case: multiple inputs and outputs
LECTURE 18 Last time: White Gaussian noise Bandlimited WGN Additive White Gaussian Noise (AWGN) channel Capacity of AWGN channel Application: DS-CDMA systems Spreading Coding theorem Lecture outline Gaussian
More informationThis examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS
THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 08 December 2009 This examination consists of
More informationStructure of the Hessian
Structure of the Hessian Graham C. Goodwin September 2004 1. Introduction We saw in earlier lectures that a core ingredient in quadratic constrained optimisation problems is the Hessian matrix H. So far
More informationChannel Coding and Interleaving
Lecture 6 Channel Coding and Interleaving 1 LORA: Future by Lund www.futurebylund.se The network will be free for those who want to try their products, services and solutions in a precommercial stage.
More informationAdvanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 19 Multi-User CDMA Uplink and Asynchronous CDMA
More informationShallow Water Fluctuations and Communications
Shallow Water Fluctuations and Communications H.C. Song Marine Physical Laboratory Scripps Institution of oceanography La Jolla, CA 92093-0238 phone: (858) 534-0954 fax: (858) 534-7641 email: hcsong@mpl.ucsd.edu
More informationMinimum BER Linear Transceivers for Block. Communication Systems. Lecturer: Tom Luo
Minimum BER Linear Transceivers for Block Communication Systems Lecturer: Tom Luo Outline Block-by-block communication Abstract model Applications Current design techniques Minimum BER precoders for zero-forcing
More informationa) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics.
Digital Modulation and Coding Tutorial-1 1. Consider the signal set shown below in Fig.1 a) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics. b) What is the minimum Euclidean
More information