Shannon and Poisson. sergio verdú

Size: px
Start display at page:

Download "Shannon and Poisson. sergio verdú"

Transcription

1 Shannon and Poisson sergio verdú

2

3 P λ (k) = e λλk k! deaths from horse kicks in the Prussian cavalry. photons arriving at photodetector packets arriving at a router DNA mutations

4 Poisson entropy

5 law of small numbers Harremoes, Johnson and Kontoyiannis (2007) Thinning and the law of small numbers

6 Poisson as maximum entropy distribution Johnson (2008) Log-concavity and the maximum entropy property of the Poisson distribution

7 Poisson entropy

8 lossless compression of one Poisson sample Verdú and Han (1997) The role of the asymptotic equipartition property in noiseless source coding

9 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR

10 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR Rubin (1974) Information rates and datacompression schemes for Poisson processes Gallager (1976) Basic limits on protocol information in data communication networks Sato and Kawabata (1987) Information rates for Poisson point processes

11 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR Verdú (1996) The exponential distribution in information theory

12 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR R (d) = λ [ 1 log 2 λd ] + bits/arrival Verdú (1996) The exponential distribution in information theory

13 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR Bedekar (2001) On the Information about message arrival times required for in-order decoding Coleman, Kiyavash, Subramanian (2008) Rate distortion function of a Poisson process with a queueing distortion measure

14 lossy compression of Poisson processes COMPRESSOR DECOMPRESSOR R (d) = λ [ 1 log 2 λd ] + bits/arrival Bedekar (2001) On the Information about message arrival times required for in-order decoding Coleman, Kiyavash, Subramanian (2008) Rate distortion function of a Poisson process with a queueing distortion measure

15 Shannon capacity of a queue ENCODER QUEUE DECODER

16 Shannon capacity of a queue ENCODER QUEUE DECODER Anantharam and Verdú (1996) Bits through queues

17 Shannon capacity of a queue ENCODER QUEUE DECODER Anantharam and Verdú (1996) Bits through queues

18 Shannon capacity of a queue ENCODER QUEUE DECODER Anantharam and Verdú (1996) Bits through queues

19 Shannon capacity of a queue ENCODER QUEUE DECODER Anantharam and Verdú (1996) Bits through queues

20 error exponent of the exponential server Arikan (2002) On the reliability function of the exponential timing channel

21 Shannon capacity of a discrete-time geometric-server queue Wagner and Anantharam (2005) Zero-rate reliability of the exponential timing channel

22 Shannon capacity of a discrete-time geometric-server queue Thomas (1997) On the Shannon capacity of discrete-time queues Bedekar and Azizoglu (1998) The informationtheoretic capacity of discrete-time queues Prabhakar and Gallager (2005) Entropy and the timing capacity of discrete queues

23 Shannon capacity of the direct-detection Poisson photon-counting channel Kabanov (1978) Capacity of a channel of a Poisson type Davis (1980) Capacity and cutoff rate for Poisson-type channels Wyner (1988) Capacity and error exponent for the direct detection photon channel

24 error exponent of the direct-detection Poisson photon-counting channel Wyner (1988) Capacity and error exponent for the direct detection photon channel

25 error exponent of the direct-detection Poisson photon-counting channel with feedback Lapidoth (1993) On the reliability function of the ideal Poisson channel with noiseless feedback

26 capacity of the direct-detection Poisson photon-counting channel with fading Chakraborthy and Narayan (2007) The Poisson fading channel

27 pulse-amplitude modulation Poisson photon-counting channel Lapidoth and Moser (2003) Asymptotic capacity of the discrete-time Poisson channel

28 mutual information and estimation Guo, Shamai, Verdú (2008) Mutual information and conditional mean estimation in Poisson Channels

29 mutual information and estimation Guo, Shamai, Verdú (2008) Mutual information and conditional mean estimation in Poisson Channels

30 filtering with Poisson observations

31 mutual information and causal filtering Kabanov (1978) Capacity of a channel of a Poisson type Liptser and Shiryaev (1978) Statistics of Random Processes

32 causal and noncausal filtering Guo, Shamai, Verdú (2008) Mutual information and conditional mean estimation in Poisson Channels

33 Life is good for only two things: discovering mathematics and teaching mathematics Simeon Denis Poisson

34 Life is good for only two things: discovering mathematics and teaching mathematics Simeon Denis Poisson I can think of other things Anthony Ephremides

35

36

An Alternative Proof of the Rate-Distortion Function of Poisson Processes with a Queueing Distortion Measure

An Alternative Proof of the Rate-Distortion Function of Poisson Processes with a Queueing Distortion Measure An Alternative Proof of the Rate-Distortion Function of Poisson Processes with a Queueing Distortion Measure Todd P. Coleman, Negar Kiyavash, and Vijay G. Subramanian ECE Dept & Coordinated Science Laboratory,

More information

A Simple Memoryless Proof of the Capacity of the Exponential Server Timing Channel

A Simple Memoryless Proof of the Capacity of the Exponential Server Timing Channel A Simple Memoryless Proof of the Capacity of the Exponential Server iming Channel odd P. Coleman ECE Department Coordinated Science Laboratory University of Illinois colemant@illinois.edu Abstract his

More information

The Rate-Distortion Function of a Poisson Process with a Queueing Distortion Measure

The Rate-Distortion Function of a Poisson Process with a Queueing Distortion Measure The Rate-Distortion Function of a Poisson Process with a Queueing Distortion Measure Todd P. Coleman Negar Kiyavash Vijay G. Subramanian Abstract This paper characterizes the rate distortion function of

More information

THE capacity of the exponential server timing channel

THE capacity of the exponential server timing channel IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 6, JUNE 2006 2697 Capacity of Queues Via Point-Process Channels Rajesh Sundaresan, Senior Member, IEEE, and Sergio Verdú, Fellow, IEEE Abstract A conceptually

More information

The Poisson Channel with Side Information

The Poisson Channel with Side Information The Poisson Channel with Side Information Shraga Bross School of Enginerring Bar-Ilan University, Israel brosss@macs.biu.ac.il Amos Lapidoth Ligong Wang Signal and Information Processing Laboratory ETH

More information

SOME fundamental relationships between input output mutual

SOME fundamental relationships between input output mutual IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 54, NO. 5, MAY 2008 1837 Mutual Information and Conditional Mean Estimation in Poisson Channels Dongning Guo, Member, IEEE, Shlomo Shamai (Shitz), Fellow,

More information

Memory in Classical Information Theory: A Brief History

Memory in Classical Information Theory: A Brief History Beyond iid in information theory 8- January, 203 Memory in Classical Information Theory: A Brief History sergio verdu princeton university entropy rate Shannon, 948 entropy rate: memoryless process H(X)

More information

Performance-based Security for Encoding of Information Signals. FA ( ) Paul Cuff (Princeton University)

Performance-based Security for Encoding of Information Signals. FA ( ) Paul Cuff (Princeton University) Performance-based Security for Encoding of Information Signals FA9550-15-1-0180 (2015-2018) Paul Cuff (Princeton University) Contributors Two students finished PhD Tiance Wang (Goldman Sachs) Eva Song

More information

Capacity and Optimal Power Allocation of Poisson Optical Communication Channels

Capacity and Optimal Power Allocation of Poisson Optical Communication Channels , October 19-21, 2011, San Francisco, USA Capacity and Optimal ower Allocation of oisson Optical Communication Channels Samah A M Ghanem, Member, IAENG, Munnujahan Ara, Member, IAENG Abstract in this paper,

More information

Interactions of Information Theory and Estimation in Single- and Multi-user Communications

Interactions of Information Theory and Estimation in Single- and Multi-user Communications Interactions of Information Theory and Estimation in Single- and Multi-user Communications Dongning Guo Department of Electrical Engineering Princeton University March 8, 2004 p 1 Dongning Guo Communications

More information

Estimating a linear process using phone calls

Estimating a linear process using phone calls Estimating a linear process using phone calls Mohammad Javad Khojasteh, Massimo Franceschetti, Gireeja Ranade Abstract We consider the problem of estimating an undisturbed, scalar, linear process over

More information

A new converse in rate-distortion theory

A new converse in rate-distortion theory A new converse in rate-distortion theory Victoria Kostina, Sergio Verdú Dept. of Electrical Engineering, Princeton University, NJ, 08544, USA Abstract This paper shows new finite-blocklength converse bounds

More information

Lecture 1. Introduction

Lecture 1. Introduction Lecture 1. Introduction What is the course about? Logistics Questionnaire Dr. Yao Xie, ECE587, Information Theory, Duke University What is information? Dr. Yao Xie, ECE587, Information Theory, Duke University

More information

Digital Communications III (ECE 154C) Introduction to Coding and Information Theory

Digital Communications III (ECE 154C) Introduction to Coding and Information Theory Digital Communications III (ECE 154C) Introduction to Coding and Information Theory Tara Javidi These lecture notes were originally developed by late Prof. J. K. Wolf. UC San Diego Spring 2014 1 / 8 I

More information

SEVERAL results in classical queueing theory state that

SEVERAL results in classical queueing theory state that IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 49, NO 2, FEBRUARY 2003 357 Entropy the Timing Capacity of Discrete Queues Balaji Prabhakar, Member, IEEE, Robert Gallager, Life Fellow, IEEE Abstract Queueing

More information

Quantum rate distortion, reverse Shannon theorems, and source-channel separation

Quantum rate distortion, reverse Shannon theorems, and source-channel separation Quantum rate distortion, reverse Shannon theorems, and source-channel separation ilanjana Datta, Min-Hsiu Hsieh, Mark Wilde (1) University of Cambridge,U.K. (2) McGill University, Montreal, Canada Classical

More information

Delay, feedback, and the price of ignorance

Delay, feedback, and the price of ignorance Delay, feedback, and the price of ignorance Anant Sahai based in part on joint work with students: Tunc Simsek Cheng Chang Wireless Foundations Department of Electrical Engineering and Computer Sciences

More information

Information Theory and Coding Techniques: Chapter 1.1. What is Information Theory? Why you should take this course?

Information Theory and Coding Techniques: Chapter 1.1. What is Information Theory? Why you should take this course? Information Theory and Coding Techniques: Chapter 1.1 What is Information Theory? Why you should take this course? 1 What is Information Theory? Information Theory answers two fundamental questions in

More information

Competition and Cooperation in Multiuser Communication Environments

Competition and Cooperation in Multiuser Communication Environments Competition and Cooperation in Multiuser Communication Environments Wei Yu Electrical Engineering Department Stanford University April, 2002 Wei Yu, Stanford University Introduction A multiuser communication

More information

Capacity of Memoryless Channels and Block-Fading Channels With Designable Cardinality-Constrained Channel State Feedback

Capacity of Memoryless Channels and Block-Fading Channels With Designable Cardinality-Constrained Channel State Feedback 2038 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 9, SEPTEMBER 2004 Capacity of Memoryless Channels and Block-Fading Channels With Designable Cardinality-Constrained Channel State Feedback Vincent

More information

Probability Models in Electrical and Computer Engineering Mathematical models as tools in analysis and design Deterministic models Probability models

Probability Models in Electrical and Computer Engineering Mathematical models as tools in analysis and design Deterministic models Probability models Probability Models in Electrical and Computer Engineering Mathematical models as tools in analysis and design Deterministic models Probability models Statistical regularity Properties of relative frequency

More information

much more on minimax (order bounds) cf. lecture by Iain Johnstone

much more on minimax (order bounds) cf. lecture by Iain Johnstone much more on minimax (order bounds) cf. lecture by Iain Johnstone http://www-stat.stanford.edu/~imj/wald/wald1web.pdf today s lecture parametric estimation, Fisher information, Cramer-Rao lower bound:

More information

Capacity Bounds on Timing Channels with Bounded Service Times

Capacity Bounds on Timing Channels with Bounded Service Times Capacity Bounds on Timing Channels with Bounded Service Times S. Sellke, C.-C. Wang, N. B. Shroff, and S. Bagchi School of Electrical and Computer Engineering Purdue University, West Lafayette, IN 47907

More information

SHARED INFORMATION. Prakash Narayan with. Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe

SHARED INFORMATION. Prakash Narayan with. Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe SHARED INFORMATION Prakash Narayan with Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe 2/40 Acknowledgement Praneeth Boda Himanshu Tyagi Shun Watanabe 3/40 Outline Two-terminal model: Mutual

More information

Reliable Computation over Multiple-Access Channels

Reliable Computation over Multiple-Access Channels Reliable Computation over Multiple-Access Channels Bobak Nazer and Michael Gastpar Dept. of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA, 94720-1770 {bobak,

More information

Lecture 1: Introduction, Entropy and ML estimation

Lecture 1: Introduction, Entropy and ML estimation 0-704: Information Processing and Learning Spring 202 Lecture : Introduction, Entropy and ML estimation Lecturer: Aarti Singh Scribes: Min Xu Disclaimer: These notes have not been subjected to the usual

More information

Digital communication system. Shannon s separation principle

Digital communication system. Shannon s separation principle Digital communication system Representation of the source signal by a stream of (binary) symbols Adaptation to the properties of the transmission channel information source source coder channel coder modulation

More information

Computing and Communications 2. Information Theory -Entropy

Computing and Communications 2. Information Theory -Entropy 1896 1920 1987 2006 Computing and Communications 2. Information Theory -Entropy Ying Cui Department of Electronic Engineering Shanghai Jiao Tong University, China 2017, Autumn 1 Outline Entropy Joint entropy

More information

Arimoto Channel Coding Converse and Rényi Divergence

Arimoto Channel Coding Converse and Rényi Divergence Arimoto Channel Coding Converse and Rényi Divergence Yury Polyanskiy and Sergio Verdú Abstract Arimoto proved a non-asymptotic upper bound on the probability of successful decoding achievable by any code

More information

The Information Lost in Erasures Sergio Verdú, Fellow, IEEE, and Tsachy Weissman, Senior Member, IEEE

The Information Lost in Erasures Sergio Verdú, Fellow, IEEE, and Tsachy Weissman, Senior Member, IEEE 5030 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 54, NO. 11, NOVEMBER 2008 The Information Lost in Erasures Sergio Verdú, Fellow, IEEE, Tsachy Weissman, Senior Member, IEEE Abstract We consider sources

More information

Energy State Amplification in an Energy Harvesting Communication System

Energy State Amplification in an Energy Harvesting Communication System Energy State Amplification in an Energy Harvesting Communication System Omur Ozel Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland College Park, MD 20742 omur@umd.edu

More information

Quantum Sphere-Packing Bounds and Moderate Deviation Analysis for Classical-Quantum Channels

Quantum Sphere-Packing Bounds and Moderate Deviation Analysis for Classical-Quantum Channels Quantum Sphere-Packing Bounds and Moderate Deviation Analysis for Classical-Quantum Channels (, ) Joint work with Min-Hsiu Hsieh and Marco Tomamichel Hao-Chung Cheng University of Technology Sydney National

More information

Variable Rate Channel Capacity. Jie Ren 2013/4/26

Variable Rate Channel Capacity. Jie Ren 2013/4/26 Variable Rate Channel Capacity Jie Ren 2013/4/26 Reference This is a introduc?on of Sergio Verdu and Shlomo Shamai s paper. Sergio Verdu and Shlomo Shamai, Variable- Rate Channel Capacity, IEEE Transac?ons

More information

SHARED INFORMATION. Prakash Narayan with. Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe

SHARED INFORMATION. Prakash Narayan with. Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe SHARED INFORMATION Prakash Narayan with Imre Csiszár, Sirin Nitinawarat, Himanshu Tyagi, Shun Watanabe 2/41 Outline Two-terminal model: Mutual information Operational meaning in: Channel coding: channel

More information

The Poisson Optical Communication Channels: Capacity and Optimal Power Allocation

The Poisson Optical Communication Channels: Capacity and Optimal Power Allocation The oisson Optical Communication : Capacity and Optimal ower Allocation Samah A M Ghanem, Member, IAENG, Munnujahan Ara, Member, IAENG Abstract In this paper, the channel capacity for different models

More information

Information Theory. Coding and Information Theory. Information Theory Textbooks. Entropy

Information Theory. Coding and Information Theory. Information Theory Textbooks. Entropy Coding and Information Theory Chris Williams, School of Informatics, University of Edinburgh Overview What is information theory? Entropy Coding Information Theory Shannon (1948): Information theory is

More information

Information and Entropy

Information and Entropy Information and Entropy Shannon s Separation Principle Source Coding Principles Entropy Variable Length Codes Huffman Codes Joint Sources Arithmetic Codes Adaptive Codes Thomas Wiegand: Digital Image Communication

More information

(Classical) Information Theory III: Noisy channel coding

(Classical) Information Theory III: Noisy channel coding (Classical) Information Theory III: Noisy channel coding Sibasish Ghosh The Institute of Mathematical Sciences CIT Campus, Taramani, Chennai 600 113, India. p. 1 Abstract What is the best possible way

More information

Sparse Regression Codes for Multi-terminal Source and Channel Coding

Sparse Regression Codes for Multi-terminal Source and Channel Coding Sparse Regression Codes for Multi-terminal Source and Channel Coding Ramji Venkataramanan Yale University Sekhar Tatikonda Allerton 2012 1 / 20 Compression with Side-Information X Encoder Rate R Decoder

More information

Lecture 4 Channel Coding

Lecture 4 Channel Coding Capacity and the Weak Converse Lecture 4 Coding I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw October 15, 2014 1 / 16 I-Hsiang Wang NIT Lecture 4 Capacity

More information

Springer Undergraduate Texts in Mathematics and Technology

Springer Undergraduate Texts in Mathematics and Technology Springer Undergraduate Texts in Mathematics and Technology Series editor H. Holden, Norwegian University of Science and Technology, Trondheim, Norway Editorial Board Lisa Goldberg, University of California,

More information

The connection between information theory and networked control

The connection between information theory and networked control The connection between information theory and networked control Anant Sahai based in part on joint work with students: Tunc Simsek, Hari Palaiyanur, and Pulkit Grover Wireless Foundations Department of

More information

Lecture 22: Final Review

Lecture 22: Final Review Lecture 22: Final Review Nuts and bolts Fundamental questions and limits Tools Practical algorithms Future topics Dr Yao Xie, ECE587, Information Theory, Duke University Basics Dr Yao Xie, ECE587, Information

More information

Iterative Quantization. Using Codes On Graphs

Iterative Quantization. Using Codes On Graphs Iterative Quantization Using Codes On Graphs Emin Martinian and Jonathan S. Yedidia 2 Massachusetts Institute of Technology 2 Mitsubishi Electric Research Labs Lossy Data Compression: Encoding: Map source

More information

Coding for Computing. ASPITRG, Drexel University. Jie Ren 2012/11/14

Coding for Computing. ASPITRG, Drexel University. Jie Ren 2012/11/14 Coding for Computing ASPITRG, Drexel University Jie Ren 2012/11/14 Outline Background and definitions Main Results Examples and Analysis Proofs Background and definitions Problem Setup Graph Entropy Conditional

More information

Probability Theory and Simulation Methods. April 6th, Lecture 19: Special distributions

Probability Theory and Simulation Methods. April 6th, Lecture 19: Special distributions April 6th, 2018 Lecture 19: Special distributions Week 1 Chapter 1: Axioms of probability Week 2 Chapter 3: Conditional probability and independence Week 4 Chapters 4, 6: Random variables Week 9 Chapter

More information

Lecture 4 Noisy Channel Coding

Lecture 4 Noisy Channel Coding Lecture 4 Noisy Channel Coding I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw October 9, 2015 1 / 56 I-Hsiang Wang IT Lecture 4 The Channel Coding Problem

More information

Information Theoretic Limits of Randomness Generation

Information Theoretic Limits of Randomness Generation Information Theoretic Limits of Randomness Generation Abbas El Gamal Stanford University Shannon Centennial, University of Michigan, September 2016 Information theory The fundamental problem of communication

More information

Lecture 5 Channel Coding over Continuous Channels

Lecture 5 Channel Coding over Continuous Channels Lecture 5 Channel Coding over Continuous Channels I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw November 14, 2014 1 / 34 I-Hsiang Wang NIT Lecture 5 From

More information

1590 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 6, JUNE Source Coding, Large Deviations, and Approximate Pattern Matching

1590 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 6, JUNE Source Coding, Large Deviations, and Approximate Pattern Matching 1590 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 6, JUNE 2002 Source Coding, Large Deviations, and Approximate Pattern Matching Amir Dembo and Ioannis Kontoyiannis, Member, IEEE Invited Paper

More information

Fixed-Length-Parsing Universal Compression with Side Information

Fixed-Length-Parsing Universal Compression with Side Information Fixed-ength-Parsing Universal Compression with Side Information Yeohee Im and Sergio Verdú Dept. of Electrical Eng., Princeton University, NJ 08544 Email: yeoheei,verdu@princeton.edu Abstract This paper

More information

EE376A: Homework #3 Due by 11:59pm Saturday, February 10th, 2018

EE376A: Homework #3 Due by 11:59pm Saturday, February 10th, 2018 Please submit the solutions on Gradescope. EE376A: Homework #3 Due by 11:59pm Saturday, February 10th, 2018 1. Optimal codeword lengths. Although the codeword lengths of an optimal variable length code

More information

Lattices for Distributed Source Coding: Jointly Gaussian Sources and Reconstruction of a Linear Function

Lattices for Distributed Source Coding: Jointly Gaussian Sources and Reconstruction of a Linear Function Lattices for Distributed Source Coding: Jointly Gaussian Sources and Reconstruction of a Linear Function Dinesh Krithivasan and S. Sandeep Pradhan Department of Electrical Engineering and Computer Science,

More information

WE consider a memoryless discrete-time channel whose

WE consider a memoryless discrete-time channel whose IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 55, NO. 1, JANUARY 2009 303 On the Capacity of the Discrete-Time Poisson Channel Amos Lapidoth, Fellow, IEEE, and Stefan M. Moser, Member, IEEE Abstract The

More information

arxiv: v1 [cs.it] 5 Sep 2008

arxiv: v1 [cs.it] 5 Sep 2008 1 arxiv:0809.1043v1 [cs.it] 5 Sep 2008 On Unique Decodability Marco Dalai, Riccardo Leonardi Abstract In this paper we propose a revisitation of the topic of unique decodability and of some fundamental

More information

Chapter 9 Fundamental Limits in Information Theory

Chapter 9 Fundamental Limits in Information Theory Chapter 9 Fundamental Limits in Information Theory Information Theory is the fundamental theory behind information manipulation, including data compression and data transmission. 9.1 Introduction o For

More information

The Timing Capacity of Single-Server Queues with Multiple Flows

The Timing Capacity of Single-Server Queues with Multiple Flows The Timing Capacity of Single-Server Queues with Multiple Flows Xin Liu and R. Srikant Coordinated Science Laboratory University of Illinois at Urbana Champaign March 14, 2003 Timing Channel Information

More information

Transmitting k samples over the Gaussian channel: energy-distortion tradeoff

Transmitting k samples over the Gaussian channel: energy-distortion tradeoff Transmitting samples over the Gaussian channel: energy-distortion tradeoff Victoria Kostina Yury Polyansiy Sergio Verdú California Institute of Technology Massachusetts Institute of Technology Princeton

More information

ELEMENTS O F INFORMATION THEORY

ELEMENTS O F INFORMATION THEORY ELEMENTS O F INFORMATION THEORY THOMAS M. COVER JOY A. THOMAS Preface to the Second Edition Preface to the First Edition Acknowledgments for the Second Edition Acknowledgments for the First Edition x

More information

SHANNON made the following well-known remarks in

SHANNON made the following well-known remarks in IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 2, FEBRUARY 2004 245 Geometric Programming Duals of Channel Capacity and Rate Distortion Mung Chiang, Member, IEEE, and Stephen Boyd, Fellow, IEEE

More information

Compression and Coding

Compression and Coding Compression and Coding Theory and Applications Part 1: Fundamentals Gloria Menegaz 1 Transmitter (Encoder) What is the problem? Receiver (Decoder) Transformation information unit Channel Ordering (significance)

More information

Capacity and Reliability Function for Small Peak Signal Constraints

Capacity and Reliability Function for Small Peak Signal Constraints 828 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 4, APRIL 2002 Capacity and Reliability Function for Small Peak Signal Constraints Bruce Hajek, Fellow, IEEE, and Vijay G. Subramanian, Member,

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Lesson 7 Delta Modulation and DPCM Instructional Objectives At the end of this lesson, the students should be able to: 1. Describe a lossy predictive coding scheme.

More information

Channel Dependent Adaptive Modulation and Coding Without Channel State Information at the Transmitter

Channel Dependent Adaptive Modulation and Coding Without Channel State Information at the Transmitter Channel Dependent Adaptive Modulation and Coding Without Channel State Information at the Transmitter Bradford D. Boyle, John MacLaren Walsh, and Steven Weber Modeling & Analysis of Networks Laboratory

More information

ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS

ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS EC 32 (CR) Total No. of Questions :09] [Total No. of Pages : 02 III/IV B.Tech. DEGREE EXAMINATIONS, APRIL/MAY- 207 Second Semester ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS Time: Three Hours

More information

The necessity and sufficiency of anytime capacity for control over a noisy communication link: Parts I and II

The necessity and sufficiency of anytime capacity for control over a noisy communication link: Parts I and II The necessity and sufficiency of anytime capacity for control over a noisy communication link: Parts I and II Anant Sahai, Sanjoy Mitter sahai@eecs.berkeley.edu, mitter@mit.edu Abstract We review how Shannon

More information

Azadeh Faridi Doctor of Philosophy, 2007

Azadeh Faridi Doctor of Philosophy, 2007 ABSTRACT Title of dissertation: CROSS-LAYER DISTORTION CONTROL FOR DELAY SENSITIVE SOURCES Azadeh Faridi Doctor of Philosophy, 2007 Dissertation directed by: Professor Anthony Ephremides Department of

More information

On the variable-delay reliability function of discrete memoryless channels with access to noisy feedback

On the variable-delay reliability function of discrete memoryless channels with access to noisy feedback ITW2004, San Antonio, Texas, October 24 29, 2004 On the variable-delay reliability function of discrete memoryless channels with access to noisy feedback Anant Sahai and Tunç Şimşek Electrical Engineering

More information

Advanced Topics in Information Theory

Advanced Topics in Information Theory Advanced Topics in Information Theory Lecture Notes Stefan M. Moser c Copyright Stefan M. Moser Signal and Information Processing Lab ETH Zürich Zurich, Switzerland Institute of Communications Engineering

More information

Performance of Polar Codes for Channel and Source Coding

Performance of Polar Codes for Channel and Source Coding Performance of Polar Codes for Channel and Source Coding Nadine Hussami AUB, Lebanon, Email: njh03@aub.edu.lb Satish Babu Korada and üdiger Urbanke EPFL, Switzerland, Email: {satish.korada,ruediger.urbanke}@epfl.ch

More information

Multiaccess Channels with State Known to One Encoder: A Case of Degraded Message Sets

Multiaccess Channels with State Known to One Encoder: A Case of Degraded Message Sets Multiaccess Channels with State Known to One Encoder: A Case of Degraded Message Sets Shivaprasad Kotagiri and J. Nicholas Laneman Department of Electrical Engineering University of Notre Dame Notre Dame,

More information

On the Throughput, Capacity and Stability Regions of Random Multiple Access over Standard Multi-Packet Reception Channels

On the Throughput, Capacity and Stability Regions of Random Multiple Access over Standard Multi-Packet Reception Channels On the Throughput, Capacity and Stability Regions of Random Multiple Access over Standard Multi-Packet Reception Channels Jie Luo, Anthony Ephremides ECE Dept. Univ. of Maryland College Park, MD 20742

More information

Towards a Theory of Information Flow in the Finitary Process Soup

Towards a Theory of Information Flow in the Finitary Process Soup Towards a Theory of in the Finitary Process Department of Computer Science and Complexity Sciences Center University of California at Davis June 1, 2010 Goals Analyze model of evolutionary self-organization

More information

On the Capacity of Diffusion-Based Molecular Timing Channels With Diversity

On the Capacity of Diffusion-Based Molecular Timing Channels With Diversity On the Capacity of Diffusion-Based Molecular Timing Channels With Diversity Nariman Farsad, Yonathan Murin, Milind Rao, and Andrea Goldsmith Electrical Engineering, Stanford University, USA Abstract This

More information

The Method of Types and Its Application to Information Hiding

The Method of Types and Its Application to Information Hiding The Method of Types and Its Application to Information Hiding Pierre Moulin University of Illinois at Urbana-Champaign www.ifp.uiuc.edu/ moulin/talks/eusipco05-slides.pdf EUSIPCO Antalya, September 7,

More information

Common Information. Abbas El Gamal. Stanford University. Viterbi Lecture, USC, April 2014

Common Information. Abbas El Gamal. Stanford University. Viterbi Lecture, USC, April 2014 Common Information Abbas El Gamal Stanford University Viterbi Lecture, USC, April 2014 Andrew Viterbi s Fabulous Formula, IEEE Spectrum, 2010 El Gamal (Stanford University) Disclaimer Viterbi Lecture 2

More information

A General Formula for Compound Channel Capacity

A General Formula for Compound Channel Capacity A General Formula for Compound Channel Capacity Sergey Loyka, Charalambos D. Charalambous University of Ottawa, University of Cyprus ETH Zurich (May 2015), ISIT-15 1/32 Outline 1 Introduction 2 Channel

More information

Channels with cost constraints: strong converse and dispersion

Channels with cost constraints: strong converse and dispersion Channels with cost constraints: strong converse and dispersion Victoria Kostina, Sergio Verdú Dept. of Electrical Engineering, Princeton University, NJ 08544, USA Abstract This paper shows the strong converse

More information

Capacity of the Discrete Memoryless Energy Harvesting Channel with Side Information

Capacity of the Discrete Memoryless Energy Harvesting Channel with Side Information 204 IEEE International Symposium on Information Theory Capacity of the Discrete Memoryless Energy Harvesting Channel with Side Information Omur Ozel, Kaya Tutuncuoglu 2, Sennur Ulukus, and Aylin Yener

More information

ABSTRACT ENERGY HARVESTING COMMUNICATION SYSTEMS. Omur Ozel, Doctor of Philosophy, Department of Electrical and Computer Engineering

ABSTRACT ENERGY HARVESTING COMMUNICATION SYSTEMS. Omur Ozel, Doctor of Philosophy, Department of Electrical and Computer Engineering ABSTRACT Title of dissertation: CODING AND SCHEDULING IN ENERGY HARVESTING COMMUNICATION SYSTEMS Omur Ozel, Doctor of Philosophy, 2014 Dissertation directed by: Professor Şennur Ulukuş Department of Electrical

More information

Information Theory and Coding Techniques

Information Theory and Coding Techniques Information Theory and Coding Techniques Lecture 1.2: Introduction and Course Outlines Information Theory 1 Information Theory and Coding Techniques Prof. Ja-Ling Wu Department of Computer Science and

More information

Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels

Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels 1 Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels Ain-ul-Aisha, Lifeng Lai, Yingbin Liang and Shlomo Shamai (Shitz Abstract In this paper, we analyze the sum-rate capacity of two-user Poisson

More information

Universal Anytime Codes: An approach to uncertain channels in control

Universal Anytime Codes: An approach to uncertain channels in control Universal Anytime Codes: An approach to uncertain channels in control paper by Stark Draper and Anant Sahai presented by Sekhar Tatikonda Wireless Foundations Department of Electrical Engineering and Computer

More information

Design of Optimal Quantizers for Distributed Source Coding

Design of Optimal Quantizers for Distributed Source Coding Design of Optimal Quantizers for Distributed Source Coding David Rebollo-Monedero, Rui Zhang and Bernd Girod Information Systems Laboratory, Electrical Eng. Dept. Stanford University, Stanford, CA 94305

More information

Lecture 11: Continuous-valued signals and differential entropy

Lecture 11: Continuous-valued signals and differential entropy Lecture 11: Continuous-valued signals and differential entropy Biology 429 Carl Bergstrom September 20, 2008 Sources: Parts of today s lecture follow Chapter 8 from Cover and Thomas (2007). Some components

More information

Entropies & Information Theory

Entropies & Information Theory Entropies & Information Theory LECTURE I Nilanjana Datta University of Cambridge,U.K. See lecture notes on: http://www.qi.damtp.cam.ac.uk/node/223 Quantum Information Theory Born out of Classical Information

More information

Rényi Information Dimension: Fundamental Limits of Almost Lossless Analog Compression

Rényi Information Dimension: Fundamental Limits of Almost Lossless Analog Compression IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 8, AUGUST 2010 3721 Rényi Information Dimension: Fundamental Limits of Almost Lossless Analog Compression Yihong Wu, Student Member, IEEE, Sergio Verdú,

More information

The Impact of Dark Current on the Wideband Poisson Channel

The Impact of Dark Current on the Wideband Poisson Channel I International Symposium on Information Theory The Impact of Dark Current on the Wideband Poisson Channel Ligong Wang and Gregory W. Wornell Department of lectrical ngineering and Computer Science Massachusetts

More information

Duality Between Channel Capacity and Rate Distortion With Two-Sided State Information

Duality Between Channel Capacity and Rate Distortion With Two-Sided State Information IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 6, JUNE 2002 1629 Duality Between Channel Capacity Rate Distortion With Two-Sided State Information Thomas M. Cover, Fellow, IEEE, Mung Chiang, Student

More information

PCM Reference Chapter 12.1, Communication Systems, Carlson. PCM.1

PCM Reference Chapter 12.1, Communication Systems, Carlson. PCM.1 PCM Reference Chapter 1.1, Communication Systems, Carlson. PCM.1 Pulse-code modulation (PCM) Pulse modulations use discrete time samples of analog signals the transmission is composed of analog information

More information

Minimum Energy Per Bit for Secret Key Acquisition Over Multipath Wireless Channels

Minimum Energy Per Bit for Secret Key Acquisition Over Multipath Wireless Channels Minimum Energy Per Bit for Secret Key Acquisition Over Multipath Wireless Channels Tzu-Han Chou Email: tchou@wisc.edu Akbar M. Sayeed Email: akbar@engr.wisc.edu Stark C. Draper Email: sdraper@ece.wisc.edu

More information

Covert Communications on Poisson Packet Channels

Covert Communications on Poisson Packet Channels Covert Communications on Poisson Packet Channels Ramin Soltani, Dennis Goeckel, Don Towsley, and Amir Houmansadr Electrical and Computer Engineering Department, University of Massachusetts, Amherst, {soltani,

More information

Communication by Regression: Achieving Shannon Capacity

Communication by Regression: Achieving Shannon Capacity Communication by Regression: Practical Achievement of Shannon Capacity Department of Statistics Yale University Workshop Infusing Statistics and Engineering Harvard University, June 5-6, 2011 Practical

More information

Lecture 15: Conditional and Joint Typicaility

Lecture 15: Conditional and Joint Typicaility EE376A Information Theory Lecture 1-02/26/2015 Lecture 15: Conditional and Joint Typicaility Lecturer: Kartik Venkat Scribe: Max Zimet, Brian Wai, Sepehr Nezami 1 Notation We always write a sequence of

More information

ELEC546 Review of Information Theory

ELEC546 Review of Information Theory ELEC546 Review of Information Theory Vincent Lau 1/1/004 1 Review of Information Theory Entropy: Measure of uncertainty of a random variable X. The entropy of X, H(X), is given by: If X is a discrete random

More information

MUTUAL information between two random

MUTUAL information between two random 3248 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 57, NO 6, JUNE 2011 Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing Haim H Permuter, Member, IEEE,

More information

Subset Source Coding

Subset Source Coding Fifty-third Annual Allerton Conference Allerton House, UIUC, Illinois, USA September 29 - October 2, 205 Subset Source Coding Ebrahim MolavianJazi and Aylin Yener Wireless Communications and Networking

More information

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE General e Image Coder Structure Motion Video x(s 1,s 2,t) or x(s 1,s 2 ) Natural Image Sampling A form of data compression; usually lossless, but can be lossy Redundancy Removal Lossless compression: predictive

More information

Characterization of Information Channels for Asymptotic Mean Stationarity and Stochastic Stability of Nonstationary/Unstable Linear Systems

Characterization of Information Channels for Asymptotic Mean Stationarity and Stochastic Stability of Nonstationary/Unstable Linear Systems 6332 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 58, NO 10, OCTOBER 2012 Characterization of Information Channels for Asymptotic Mean Stationarity and Stochastic Stability of Nonstationary/Unstable Linear

More information

G 8243 Entropy and Information in Probability

G 8243 Entropy and Information in Probability G 8243 Entropy and Information in Probability I. Kontoyiannis Columbia University Spring 2009 What is Information Theory? Born in 1948, it is the mathematical foundation of communication theory; it quantifies

More information