Data Rate Theorem for Stabilization over Time-Varying Feedback Channels
|
|
- Abigayle Moore
- 6 years ago
- Views:
Transcription
1 Data Rate Theorem for Stabilization over Time-Varying Feedback Channels Workshop on Frontiers in Distributed Communication, Sensing and Control Massimo Franceschetti, UCSD (joint work with P. Minero, S. Dey and G. Nair) California Institute for Telecommunications and Information Technology
2 Motivation
3 Abstraction DYNAMICAL SYSTEM OBSERVER/ ESTIMATOR CHANNEL CONTROLLER/ ACTUATOR Quality channel Time
4 Packet loss point of view
5 Rate process: Rate Process Packet loss point of view R k = { w.p. 1 p 0 w.p. p 0 Time There is a critical dropout probability p for estimation (Sinopoli et al 2004) and control (Schenato et al 2007, Gupta et al 2007): p < 1 max i λ i 2.
6 Rate limited feedback point of view
7 Rate limited feedback point of view Rate process: R k = R Rate Process R Time Data rate theorem (Tatikonda-Mitter 2002): R > u m u log 2 λ u
8 Can we merge the two approaches?
9 Can we merge the two approaches? Stochastic time-varying rate limited feedback: rate process {R k } i.i.d. according to a random variable R. Rate Process R 1 R 3 R 2 Special cases: R is deterministic, R is 0 or Time
10 Problem formulation R k x k+1 = Ax k + Bu k + v k y k = Cx k + w k y k OBSERVER/ ESTIMATOR C O D E R D E C O D E R CONTROLLER/ ACTUATOR u k A set of unstable eigenmodes λ 1 1,, λ n 1. Problem: conditions on R to achieve second moment stability: sup k E [ x k 2] <.
11 Problem formulation Distributions of x 0 and system disturbances (v and w): Unbounded support A bounded moment > 2 (e.g. Gaussian distribution)
12 Problem formulation Distributions of x 0 and system disturbances (v and w): Unbounded support A bounded moment > 2 (e.g. Gaussian distribution) Rate process: At time k, coder and decoder have knowledge of {R i } k i=0. The case of R k = 0 can be thought as a packet loss with acknowledgment at the transmitter.
13 Problem formulation Distributions of x 0 and system disturbances (v and w): Unbounded support A bounded moment > 2 (e.g. Gaussian distribution) Rate process: At time k, coder and decoder have knowledge of {R i } k i=0. The case of R k = 0 can be thought as a packet loss with acknowledgment at the transmitter. Coder has access to more information than the decoder (Classical information structure).
14 Related works Decoding Disturbances Time-varying errors rate Tatikonda-Mitter No Bounded No (Tran AC 02) Nair-Evans No Unbounded No (SIAM J.C. 04) Yuksel-Basar Yes Unbounded No (CDC 05) Martins-Dahleh-Elia No Bounded Yes (Tran AC 06) Sahai-Mitter Yes Bounded Yes (Tran IT 06) This work No Unbounded Yes
15 Summary of results Scalar case: Necessary and sufficient condition for second moment stabilizability.
16 Summary of results Scalar case: Necessary and sufficient condition for second moment stabilizability. Vector case: we sandwich the stabilizability region between two polytopes. Necessity: The outer polytope has a special geometric structure. Sufficiency: The inner and outer polytopes coincide in some special cases.
17 Scalar case R k x k+1 = λx k + u k + v k y k = x k + w k y k OBSERVER/ ESTIMATOR C O D E R D E C O D E R CONTROLLER/ ACTUATOR u k Unstable system, so λ 1. Theorem 1. Necessary and sufficient condition for stabilization in the mean square sense is that [ ] λ 2 E < R
18 h Scalar case: E λ 2 i 2 2R At each time step, uncertainty volume λ 2, 1 2 2R If the ratio, on average, is 1, then the information sent cannot compensate the system dynamics. < 1
19 h Scalar case: E λ 2 i 2 2R At each time step, uncertainty volume λ 2, 1 2 2R If the ratio, on average, is 1, then the information sent cannot compensate the system dynamics. < 1 Deterministic rate: we recover the data rate theorem R > log 2 λ.
20 h Scalar case: E λ 2 i 2 2R At each time step, uncertainty volume λ 2, 1 2 2R If the ratio, on average, is 1, then the information sent cannot compensate the system dynamics. < 1 Deterministic rate: we recover the data rate theorem R > log 2 λ. Packet loss: we recover the critical dropout probability [ ] λ 2 E 2 2R = p λ 2 + (1 p) λ r < 1 p < 1 λ 2, as r
21 Scalar case: proofs Necessity: recursion Thus, Using the entropy power inequality we establish the following [ ] λ E[x 2 2 k] E E[x 2 k 1] + const., sup k 2 2R [ ] λ E[x 2 2 k] < E < R Sufficiency: Design an adaptive quantizer, avoid saturation, high resolution through successive refinements.
22 Successive refinements Divide time into cycles of fixed duration τ (of our choice) Observe the system at the beginning of each cycle and send an initial estimate During the remaining of a cycle, refine the initial estimate. Quantize Quantize 1 2 τ 1 τ τ + 1 2τ 1 Refinements Refinements Number of bits per cycle is a random variable, dependent on the rate process
23 Successive refinements Quantizer can be generated recursively from a 2-bit quantizer, as follows 2-bit quantizer bit quantizer ρ ρ 4-bit quantizer ρ 2 ρ ρ ρ 2 ρ is a parameter chosen dependent on the distribution of the disturbances.
24 Successive refinements: example Suppose we need to quantize a positive real value x. At time k = 1, suppose R 1 = 1. x R With one bit of information, the decoder knows that x > 0
25 Successive refinements: example At time k = 2, suppose R 2 = 2: coder and decoder partition the real axis according to a 3-bit quantizer. x ρ ρ R However, coder and decoded only label the partitions in the positive real line (2 bits suffice). After receiving 01, the decoder knows that x [ 1 2, 1). The initial estimate x [0, ) has been refined. The scheme works as if we had known ahead of time that R 1 + R 2 = 3.
26 Sufficiency: analysis Quantize Quantize 1 2 τ 1 τ τ + 1 2τ 1 Refinements Refinements We find a recursion for E[x 2 kτ ] E[x 2 kτ] const Thus, for τ large enough ( E [ λ 2 2 2R ]) τ E[x 2 (k 1)τ ] + const. [ ] λ 2 E 2 2R < 1 const ( E [ ]) λ 2 τ < R
27 Vector case R k x k+1 = Ax k + Bu k + v k y k = Cx k + w k y k OBSERVER/ ESTIMATOR C O D E R D E C O D E R CONTROLLER/ ACTUATOR u k n unstable sub-mode associated to eigenvalues λ 1 1, λ 2 1,, λ n 1. Sub-mode i has multiplicity m i. (A,B) is reachable and (C,A) observable.
28 Vector case: necessity Theorem 2. Necessary condition for stabilizability in the mean square sense is that (log 2 λ 1,...,log 2 λ n ) R n + are inside the region determined by the following set of inequalities n i s i log 2 λ i < s i 2 i=1 [ log 2 E ] 2 P 2 i s R i where s = [s 1,, s n ] T 0, s i {0,...,m i }, i = 1,, n.,
29 Vector case: necessity Example 1. Suppose so m 1 = 2 and m 2 = 1. A = log λ 2 < 1 2 log 2 E λ λ λ 2 ˆ2 2R, Not stabilizable 2 log λ 1 + log λ 2 < 3 2 log 2 E h2 2 3 Ri log 2 λ log λ 1 < log 2 E ˆ2 R log 2 λ 1
30 Special case: deterministic rate We recover the necessity of the data rate theorem 2log λ 1 + log λ 2 < R. Polyhedron reduces to a hyperplane R Not stabilizable log 2 λ log 2 λ 1 R 2
31 Special case: packet erasure We recover the necessity on the critical dropout probability p < 1 max i λ i 2. Polyhedron reduces to a hypercube log 2 1 p Not stabilizable log 2 λ log 2 λ log 2 1 p
32 Vector case: sufficiency From the scalar case result we can achieve the two red points Not stabilizable 0.12 log 2 λ log 2 λ 1
33 Vector case: sufficiency We can then time share between the two red points Not stabilizable 0.12 log 2 λ Stabilizable region log 2 λ 1 Can we do better?
34 Sufficiency: rate allocation Theorem 3. Sufficient condition for stabilizability in the mean square sense is that (log 2 λ 1,...,log 2 λ n ) R n + are inside the convex hull of the regions determined by the following n inequalities [ λ i 2 ] E < 1, i = 1,...,n, 2 2 α i(r) R where the rate allocation vector α(r) := [α 1 (R),...,α n (R)] T satisfies α i (r) [0, 1] r m i α i (r) N n i=1 α i(r) 1 for all possible values r that R can take (excluding 0).
35 Sufficiency: rate allocation Example 2. Suppose the rate process is given by R = { 6 w.p. 1 p 0 w.p. p, and, as in the previous example, A = λ λ λ 2. There are four (dominant) possible allocations α(r) = [α 1 (R),α 2 (R)] T : [α 1 (6), α 2 (6)] T { [6 ] T 6, 0, [ 4 6, 2 ] T [ 2, 6 6, 4 ] T [, 0, 6 ] } T, 6 6
36 Sufficiency: example Not stabilizable ˆ4 6, 2 T 6 log 2 λ T ˆ0, 6 ˆ2 6, 4 T Stabilizable region 0.02 ˆ6 6, 0 T log 2 λ 1 One point on the dominant face of the pentagon is optimal
37 Sufficiency: remarks One point on the dominant face of the pentagon is optimal If R = j r w.p. 1 p 0 w.p. p, scheme asymptotically optimal as r Not stabilizable region 0.12 log 2 λ Stabilizable region log 2 λ 1 Scheme is optimal if pentagon reduces to hypercube or hyperplane
38 Remarks Above theorems provide polytopic inner and outer bounds to stabilizability region Not stabilizable log 2 λ Inner bound Outer bound 0.04 Stabilizable region log 2 λ 1 Improved coding scheme for the case of an erasure channel.
39 Concluding Remarks Summary: Unified approach to information-theoretic and packet loss models. second moment stabilization with stochastic time-varying rates. Scalar case: complete characterization. Vector case: geometric structure, scheme is optimal in some limiting cases. Future directions: Close the gap in the vector case Model decoding errors
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 54, NO. 2, FEBRUARY
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 54, NO 2, FEBRUARY 2009 243 Data Rate Theorem for Stabilization Over Time-Varying Feedback Channels Paolo Minero, Student Member, IEEE, Massimo Franceschetti,
More informationData rate theorem for stabilization over fading channels
Data rate theorem for stabilization over fading channels (Invited Paper) Paolo Minero, Massimo Franceschetti, Subhrakanti Dey and Girish Nair Abstract In this paper, we present a data rate theorem for
More informationTowards control over fading channels
Towards control over fading channels Paolo Minero, Massimo Franceschetti Advanced Network Science University of California San Diego, CA, USA mail: {minero,massimo}@ucsd.edu Invited Paper) Subhrakanti
More informationStabilization Over Markov Feedback Channels: The general case.
Stabilization Over Markov Feedback Channels: The general case. Lorenzo Coviello, Paolo Minero, and Massimo Franceschetti 1 Abstract The problem of mean-square stabilization of a discrete-time linear dynamical
More informationChapter 1 Elements of Information Theory for Networked Control Systems
Chapter 1 Elements of Information Theory for Networked Control Systems Massimo Franceschetti and Paolo Minero 1.1 Introduction Next generation cyber-physical systems [35] will integrate computing, communication,
More informationEstimating 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 informationCommunication constraints and latency in Networked Control Systems
Communication constraints and latency in Networked Control Systems João P. Hespanha Center for Control Engineering and Computation University of California Santa Barbara In collaboration with Antonio Ortega
More informationControl over finite capacity channels: the role of data loss, delay and signal-to-noise limitations
Control over finite capacity channels: the role of data loss, delay and signal-to-noise limitations Plant DEC COD Channel Luca Schenato University of Padova Control Seminars, Berkeley, 2014 University
More informationFeedback Stabilization of Uncertain Systems Using a Stochastic Digital Link
Feedback Stabilization of Uncertain Systems Using a Stochastic Digital Link Nuno C. Martins, Munther A. Dahleh and Nicola Elia Abstract We study the stabilizability of uncertain systems in the presence
More informationTime-triggering versus event-triggering control over communication channels
Time-triggering versus event-triggering control over communication channels Mohammad Javad Khojasteh Pavankumar Tallapragada Jorge Cortés Massimo Franceschetti Abstract Time-triggered and event-triggered
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 informationLinear Encoder-Decoder-Controller Design over Channels with Packet Loss and Quantization Noise
Linear Encoder-Decoder-Controller Design over Channels with Packet Loss and Quantization Noise S. Dey, A. Chiuso and L. Schenato Abstract In this paper we consider the problem of designing coding and decoding
More informationUniversal 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 informationControl Capacity. Gireeja Ranade and Anant Sahai UC Berkeley EECS
Control Capacity Gireeja Ranade and Anant Sahai UC Berkeley EECS gireeja@eecs.berkeley.edu, sahai@eecs.berkeley.edu Abstract This paper presents a notion of control capacity that gives a fundamental limit
More information2. PROBLEM SETUP. <. The system gains {A t } t 0
Rate-limited control of systems with uncertain gain Victoria Kostina, Yuval Peres, Miklós Z. Rácz, Gireeja Ranade California Institute of Technology, Pasadena, C Microsoft Research, Redmond, W vkostina@caltech.edu,
More informationNetworked Control Systems: Estimation and Control over Lossy Networks
Noname manuscript No. (will be inserted by the editor) Networked Control Systems: Estimation and Control over Lossy Networks João P. Hespanha Alexandre R. Mesquita the date of receipt and acceptance should
More informationEncoder Decoder Design for Feedback Control over the Binary Symmetric Channel
Encoder Decoder Design for Feedback Control over the Binary Symmetric Channel Lei Bao, Mikael Skoglund and Karl Henrik Johansson School of Electrical Engineering, Royal Institute of Technology, Stockholm,
More informationStochastic Stability and Ergodicity of Linear Gaussian Systems Controlled over Discrete Channels
Stochastic Stability and Ergodicity of Linear Gaussian Systems Controlled over Discrete Channels Serdar Yüksel Abstract We present necessary and sufficient conditions for stochastic stabilizability of
More informationStationarity and Ergodicity of Stochastic Non-Linear Systems Controlled over Communication Channels
Stationarity and Ergodicity of Stochastic Non-Linear Systems Controlled over Communication Channels Serdar Yüksel Abstract This paper is concerned with the following problem: Given a stochastic non-linear
More informationNONLINEAR CONTROL with LIMITED INFORMATION. Daniel Liberzon
NONLINEAR CONTROL with LIMITED INFORMATION Daniel Liberzon Coordinated Science Laboratory and Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign Plenary talk, 2 nd Indian Control
More informationCapacity-achieving Feedback Scheme for Flat Fading Channels with Channel State Information
Capacity-achieving Feedback Scheme for Flat Fading Channels with Channel State Information Jialing Liu liujl@iastate.edu Sekhar Tatikonda sekhar.tatikonda@yale.edu Nicola Elia nelia@iastate.edu Dept. of
More informationStochastic Stabilization of a Noisy Linear System with a Fixed-Rate Adaptive Quantizer
2009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 10-12, 2009 ThA06.6 Stochastic Stabilization of a Noisy Linear System with a Fixed-Rate Adaptive Quantizer Serdar Yüksel
More informationControl Over Noisy Channels
IEEE RANSACIONS ON AUOMAIC CONROL, VOL??, NO??, MONH?? 004 Control Over Noisy Channels Sekhar atikonda, Member, IEEE, and Sanjoy Mitter, Fellow, IEEE, Abstract Communication is an important component of
More informationLQG CONTROL WITH MISSING OBSERVATION AND CONTROL PACKETS. Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Shankar Sastry
LQG CONTROL WITH MISSING OBSERVATION AND CONTROL PACKETS Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Shankar Sastry Department of Electrical Engineering and Computer Sciences
More informationOn the Effect of Quantization on Performance at High Rates
Proceedings of the 006 American Control Conference Minneapolis, Minnesota, USA, June 4-6, 006 WeB0. On the Effect of Quantization on Performance at High Rates Vijay Gupta, Amir F. Dana, Richard M Murray
More informationTowards the Control of Linear Systems with Minimum Bit-Rate
Towards the Control of Linear Systems with Minimum Bit-Rate João Hespanha hespanha@ece.ucsb.edu Antonio Ortega ortega@sipi.usc.edu Lavanya Vasudevan vasudeva@usc.edu Dept. Electrical & Computer Engineering,
More informationClosed-Loop Stabilization Over Gaussian Interference Channel
reprints of the 8th IFAC World Congress Milano Italy August 28 - September 2, 20 Closed-Loop Stabilization Over Gaussian Interference Channel Ali A. Zaidi, Tobias J. Oechtering and Mikael Skoglund School
More informationFeedback Stabilization over Signal-to-Noise Ratio Constrained Channels
Feedback Stabilization over Signal-to-Noise Ratio Constrained Channels Julio H. Braslavsky, Rick H. Middleton and Jim S. Freudenberg Abstract There has recently been significant interest in feedback stabilization
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 informationRECENT advances in technology have led to increased activity
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 49, NO 9, SEPTEMBER 2004 1549 Stochastic Linear Control Over a Communication Channel Sekhar Tatikonda, Member, IEEE, Anant Sahai, Member, IEEE, and Sanjoy Mitter,
More informationarxiv: v1 [cs.sy] 5 May 2018
Event-triggering stabilization of real and complex linear systems with disturbances over digital channels Mohammad Javad Khojasteh, Mojtaba Hedayatpour, Jorge Cortés, Massimo Franceschetti arxiv:85969v
More informationUNIVERSITY of CALIFORNIA Santa Barbara. Communication scheduling methods for estimation over networks
UNIVERSITY of CALIFORNIA Santa Barbara Communication scheduling methods for estimation over networks A Dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy
More informationRandom-Time, State-Dependent Stochastic Drift for Markov Chains and Application to Stochastic Stabilization Over Erasure Channels
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 58, NO 1, JANUARY 2013 47 Random-Time, State-Dependent Stochastic Drift for Markov Chains and Application to Stochastic Stabilization Over Erasure Channels Serdar
More informationControl of Tele-Operation Systems Subject to Capacity Limited Channels and Uncertainty
Control of Tele-Operation Systems Subject to Capacity Limited s and Uncertainty Alireza Farhadi School of Information Technology and Engineering, University of Ottawa Ottawa, Canada e-mail: afarhadi@site.uottawa.ca
More informationCapacity Region of the Permutation Channel
Capacity Region of the Permutation Channel John MacLaren Walsh and Steven Weber Abstract We discuss the capacity region of a degraded broadcast channel (DBC) formed from a channel that randomly permutes
More informationEstimation over Communication Networks: Performance Bounds and Achievability Results
Estimation over Communication Networks: Performance Bounds and Achievability Results A. F. Dana, V. Gupta, J. P. Hespanha, B. Hassibi and R. M. Murray Abstract This paper considers the problem of estimation
More informationA Mathematical Framework for Robust Control Over Uncertain Communication Channels
A Mathematical Framework for Robust Control Over Uncertain Communication Channels Charalambos D. Charalambous and Alireza Farhadi Abstract In this paper, a mathematical framework for studying robust control
More informationROBUST CONSTRAINED REGULATORS FOR UNCERTAIN LINEAR SYSTEMS
ROBUST CONSTRAINED REGULATORS FOR UNCERTAIN LINEAR SYSTEMS Jean-Claude HENNET Eugênio B. CASTELAN Abstract The purpose of this paper is to combine several control requirements in the same regulator design
More informationEncoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels
Encoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels LEI BAO, MIKAEL SKOGLUND AND KARL HENRIK JOHANSSON IR-EE- 26: Stockholm 26 Signal Processing School of Electrical Engineering
More informationCharacterization 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 informationSequential Coding of Gauss Markov Sources With Packet Erasures and Feedback
Sequential Coding of Gauss Markov Sources With Packet Erasures and Feedback Anatoly Khina, Victoria Kostina, Ashish Khisti, and Babak Hassibi Abstract We consider the problem of sequential transmission
More informationEncoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels
Encoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels Lei Bao, Mikael Skoglund and Karl Henrik Johansson Department of Signals, Sensors and Systems, Royal Institute of Technology,
More informationFeedback Capacity of a Class of Symmetric Finite-State Markov Channels
Feedback Capacity of a Class of Symmetric Finite-State Markov Channels Nevroz Şen, Fady Alajaji and Serdar Yüksel Department of Mathematics and Statistics Queen s University Kingston, ON K7L 3N6, Canada
More informationStabilization over discrete memoryless and wideband channels using nearly memoryless observations
Stabilization over discrete memoryless and wideband channels using nearly memoryless observations Anant Sahai Abstract We study stabilization of a discrete-time scalar unstable plant over a noisy communication
More informationLecture 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 informationTracking and Control of Gauss Markov Processes over Packet-Drop Channels with Acknowledgments
Tracking and Control of Gauss Markov Processes over Packet-Drop Channels with Acknowledgments Anatoly Khina, Victoria Kostina, Ashish Khisti, and Babak Hassibi arxiv:1702.01779v4 [cs.it] 23 May 2018 Abstract
More informationState Estimation Utilizing Multiple Description Coding over Lossy Networks
Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 5 Seville, Spain, December 12-15, 5 MoB6.6 State Estimation Utilizing Multiple Description Coding over
More informationLecture 1: Entropy, convexity, and matrix scaling CSE 599S: Entropy optimality, Winter 2016 Instructor: James R. Lee Last updated: January 24, 2016
Lecture 1: Entropy, convexity, and matrix scaling CSE 599S: Entropy optimality, Winter 2016 Instructor: James R. Lee Last updated: January 24, 2016 1 Entropy Since this course is about entropy maximization,
More informationReference Tracking of Nonlinear Dynamic Systems over AWGN Channel Using Describing Function
1 Reference Tracking of Nonlinear Dynamic Systems over AWGN Channel Using Describing Function Ali Parsa and Alireza Farhadi Abstract This paper presents a new technique for mean square asymptotic reference
More informationOn Time-Varying Bit-Allocation Maintaining Stability and Performance: A Convex Parameterization
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 53, NO 5, JUNE 2008 1147 On Time-Varying Bit-Allocation Maintaining Stability and Performance: A Convex Parameterization Sridevi V Sarma, Member, IEEE, Munther
More informationCoding into a source: an inverse rate-distortion theorem
Coding into a source: an inverse rate-distortion theorem Anant Sahai joint work with: Mukul Agarwal Sanjoy K. Mitter Wireless Foundations Department of Electrical Engineering and Computer Sciences University
More informationControl with side information
Control with side information Gireeja Ranade and Anant Sahai Microsoft Research, Redmond, WA University of California, Berkeley EECS giranade@microsoft.com, sahai@eecs.berkeley.edu Abstract This paper
More informationOptimal LQG Control Across a Packet-Dropping Link
Optimal LQG Control Across a Pacet-Dropping Lin Vijay Gupta, Demetri Spanos, Baba Hassibi, Richard M Murray Division of Engineering and Applied Science California Institute of echnology {vijay,demetri}@cds.caltech.edu,{hassibi,murray}@caltech.edu
More informationCapacity 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 informationFalse Data Injection Attacks in Control Systems
False Data Injection Attacks in Control Systems Yilin Mo, Bruno Sinopoli Department of Electrical and Computer Engineering, Carnegie Mellon University First Workshop on Secure Control Systems Bruno Sinopoli
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 informationCoding and Control over Discrete Noisy Forward and Feedback Channels
Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain, December 12-15, 2005 TuA14.1 Coding and Control over Discrete Noisy Forward and
More informationLecture 1: The Multiple Access Channel. Copyright G. Caire 12
Lecture 1: The Multiple Access Channel Copyright G. Caire 12 Outline Two-user MAC. The Gaussian case. The K-user case. Polymatroid structure and resource allocation problems. Copyright G. Caire 13 Two-user
More informationEVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER Zhen Zhen 1, Jun Young Lee 2, and Abdus Saboor 3 1 Mingde College, Guizhou University, China zhenz2000@21cn.com 2 Department
More informationSpace-Time Coding for Multi-Antenna Systems
Space-Time Coding for Multi-Antenna Systems ECE 559VV Class Project Sreekanth Annapureddy vannapu2@uiuc.edu Dec 3rd 2007 MIMO: Diversity vs Multiplexing Multiplexing Diversity Pictures taken from lectures
More informationDetectability and Output Feedback Stabilizability of Nonlinear Networked Control Systems
Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain, December 12-15, 2005 ThC14.2 Detectability and Output Feedback Stabilizability
More informationJointly Optimal LQG Quantization and Control Policies for Multi-Dimensional Linear Gaussian Sources
Fiftieth Annual Allerton Conference Allerton House, UIUC, Illinois, USA October 1-5, 2012 Jointly Optimal LQG Quantization and Control Policies for Multi-Dimensional Linear Gaussian Sources Serdar Yüksel
More informationRandom Access: An Information-Theoretic Perspective
Random Access: An Information-Theoretic Perspective Paolo Minero, Massimo Franceschetti, and David N. C. Tse Abstract This paper considers a random access system where each sender can be in two modes of
More informationSTATE AND OUTPUT FEEDBACK CONTROL IN MODEL-BASED NETWORKED CONTROL SYSTEMS
SAE AND OUPU FEEDBACK CONROL IN MODEL-BASED NEWORKED CONROL SYSEMS Luis A Montestruque, Panos J Antsalis Abstract In this paper the control of a continuous linear plant where the sensor is connected to
More informationTime Varying Optimal Control with Packet Losses.
Time Varying Optimal Control with Packet Losses. Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Shankar S. Sastry Department of Electrical Engineering and Computer Sciences University
More informationOPTIMAL CONTROL AND ESTIMATION
OPTIMAL CONTROL AND ESTIMATION Robert F. Stengel Department of Mechanical and Aerospace Engineering Princeton University, Princeton, New Jersey DOVER PUBLICATIONS, INC. New York CONTENTS 1. INTRODUCTION
More informationInformation Recovery from Pairwise Measurements
Information Recovery from Pairwise Measurements A Shannon-Theoretic Approach Yuxin Chen, Changho Suh, Andrea Goldsmith Stanford University KAIST Page 1 Recovering data from correlation measurements A large
More informationMUCH attention has been recently paid to networked control
1392 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 53, NO. 6, JULY 2008 Optimal Sensor Querying: General Markovian and LQG Models With Controlled Observations Wei Wu, Member, IEEE, and Ari Arapostathis,
More informationSignal Reconstruction in the Presence of Finite-Rate. Measurements: Finite-Horizon Control Applications
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL Int. J. Robust Nonlinear Control 2008; 00:1 19 [Version: 2002/11/11 v1.00] Signal Reconstruction in the Presence of Finite-Rate Measurements: Finite-Horizon
More informationThe 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 informationOn the Capacity Region of the Gaussian Z-channel
On the Capacity Region of the Gaussian Z-channel Nan Liu Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland, College Park, MD 74 nkancy@eng.umd.edu ulukus@eng.umd.edu
More informationMultimedia Communications. Scalar Quantization
Multimedia Communications Scalar Quantization Scalar Quantization In many lossy compression applications we want to represent source outputs using a small number of code words. Process of representing
More informationMorning Session Capacity-based Power Control. Department of Electrical and Computer Engineering University of Maryland
Morning Session Capacity-based Power Control Şennur Ulukuş Department of Electrical and Computer Engineering University of Maryland So Far, We Learned... Power control with SIR-based QoS guarantees Suitable
More informationNecessary and sufficient bit rate conditions to stabilize quantized Markov jump linear systems
010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 0, 010 WeA07.1 Necessary and sufficient bit rate conditions to stabilize quantized Markov jump linear systems Qiang
More informationOn the Optimality of Threshold Policies in Event Triggered Estimation with Packet Drops
On the Optimality of Threshold Policies in Event Triggered Estimation with Packet Drops Alex S. Leong, Subhrakanti Dey, and Daniel E. Quevedo Abstract We consider a remote state estimation problem, where
More informationOutput Feedback Control of Linear Systems with Input and Output Quantization
Output Feedback Control of Linear Systems with Input and Output Quantization Daniel Coutinho Minyue Fu Carlos E. de Souza Abstract A considerable amount of research has been done on the use of logarithmic
More informationLecture 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 informationJob Scheduling and Multiple Access. Emre Telatar, EPFL Sibi Raj (EPFL), David Tse (UC Berkeley)
Job Scheduling and Multiple Access Emre Telatar, EPFL Sibi Raj (EPFL), David Tse (UC Berkeley) 1 Multiple Access Setting Characteristics of Multiple Access: Bursty Arrivals Uncoordinated Transmitters Interference
More informationLQG control over communication channels: the role of data losses, delays and SNR limitations
LQG control over communication channels: the role of data losses, delays and SNR limitations lessandro Chiuso, Nicola Laurenti, Luca Schenato, ndrea Zanella Department of Information Engineering University
More informationELEC546 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 informationFeedback Stabilization over a First Order Moving Average Gaussian Noise Channel
Feedback Stabiliation over a First Order Moving Average Gaussian Noise Richard H. Middleton Alejandro J. Rojas James S. Freudenberg Julio H. Braslavsky Abstract Recent developments in information theory
More informationVia Gradenigo 6/b Department of Information Engineering University of Padova Padova, Italy
Via Gradenigo 6/b Department of Information Engineering University of Padova 35121 Padova, Italy Email: schenato@dei.unipd.it November 9, 2007 Professor Roberto Tempo Editor, Technical Notes and Correspondence
More informationSparse Packetized Predictive Control for Networked Control Over Erasure Channels
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 59, NO 7, JULY 2014 1899 Sparse Packetized Predictive Control for Networked Control Over Erasure Channels Masaaki Nagahara, Member,IEEE, Daniel E Quevedo, Member,IEEE,and
More informationDecentralized Control across Bit-Limited Communication Channels: An Example
Decentralized Control across Bit-Limited Communication Channels: An Eample Ling Shi, Chih-Kai Ko, Zhipu Jin, Dennice Gayme, Vijay Gupta, Steve Waydo and Richard M. Murray Abstract We formulate a simple
More informationOn Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 2008 On Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements Shaosheng Zhou
More informationNonstochastic Information Theory for Feedback Control
Nonstochastic Information Theory for Feedback Control Girish Nair Department of Electrical and Electronic Engineering University of Melbourne Australia Dutch Institute of Systems and Control Summer School
More informationLinear Algebra Review: Linear Independence. IE418 Integer Programming. Linear Algebra Review: Subspaces. Linear Algebra Review: Affine Independence
Linear Algebra Review: Linear Independence IE418: Integer Programming Department of Industrial and Systems Engineering Lehigh University 21st March 2005 A finite collection of vectors x 1,..., x k R n
More informationFundamental rate delay tradeoffs in multipath routed and network coded networks
Fundamental rate delay tradeoffs in multipath routed and network coded networks John Walsh and Steven Weber Drexel University, Dept of ECE Philadelphia, PA 94 {jwalsh,sweber}@ecedrexeledu IP networks subject
More informationMultiple Description Coding for Closed Loop Systems over Erasure Channels
Downloaded from vbn.aau.dk on: January 24, 2019 Aalborg Universitet Multiple Description Coding for Closed Loop Systems over Erasure Channels Østergaard, Jan; Quevedo, Daniel Published in: IEEE Data Compression
More informationStochastic stabilization of dynamical systems over communication channels arxiv: v1 [math.oc] 9 Jan 2019
Stochastic stabilization of dynamical systems over communication channels arxiv:90.02825v [math.oc] 9 Jan 209 Christoph Kawan Serdar Yüksel Abstract We study stochastic stabilization of non-linear dynamical
More informationDelay-independent stability via a reset loop
Delay-independent stability via a reset loop S. Tarbouriech & L. Zaccarian (LAAS-CNRS) Joint work with F. Perez Rubio & A. Banos (Universidad de Murcia) L2S Paris, 20-22 November 2012 L2S Paris, 20-22
More informationLQG Control For MIMO Systems Over multiple TCP-like Erasure Channels
LQG Control For MIMO Systems Over multiple CP-like Erasure Channels E. Garone, B. Sinopoli, A. Goldsmith and A. Casavola Abstract his paper is concerned with control applications over lossy data networks.
More informationStability Analysis of Finite-Level Quantized Linear Control Systems
Stability nalysis of Finite-Level Quantized Linear Control Systems Carlos E. de Souza Daniel F. Coutinho Minyue Fu bstract In this paper we investigate the stability of discrete-time linear time-invariant
More informationScalar and Vector Quantization. National Chiao Tung University Chun-Jen Tsai 11/06/2014
Scalar and Vector Quantization National Chiao Tung University Chun-Jen Tsai 11/06/014 Basic Concept of Quantization Quantization is the process of representing a large, possibly infinite, set of values
More informationA New Metaconverse and Outer Region for Finite-Blocklength MACs
A New Metaconverse Outer Region for Finite-Blocklength MACs Pierre Moulin Dept of Electrical Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 680 Abstract An outer rate region
More informationSTOCHASTIC STABILITY OF EXTENDED FILTERING FOR NONLINEAR SYSTEMS WITH MEASUREMENT PACKET LOSSES
Proceedings of the IASTED International Conference Modelling, Identification and Control (AsiaMIC 013) April 10-1, 013 Phuet, Thailand STOCHASTIC STABILITY OF EXTENDED FILTERING FOR NONLINEAR SYSTEMS WITH
More informationControl over Gaussian Channels With and Without Source Channel Separation
1 Control over Gaussian Channels With and Without Source Channel Separation Anatoly Khina, Elias Riedel Gårding, Gustav M. Pettersson, Victoria Kostina, and Babak Hassibi Abstract We consider the problem
More informationApplications of Controlled Invariance to the l 1 Optimal Control Problem
Applications of Controlled Invariance to the l 1 Optimal Control Problem Carlos E.T. Dórea and Jean-Claude Hennet LAAS-CNRS 7, Ave. du Colonel Roche, 31077 Toulouse Cédex 4, FRANCE Phone : (+33) 61 33
More informationREAL-TIME STATE ESTIMATION OF LINEAR PROCESSES OVER A SHARED AWGN CHANNEL WITHOUT FEEDBACK
CYBER-PHYSICAL CLOUD COMPUTING LAB UNIVERSITY OF CALIFORNIA, BERKELEY REAL-TIME STATE ESTIMATION OF LINEAR PROCESSES OVER A SHARED AWGN CHANNEL WITHOUT FEEDBACK Ching-Ling Huang and Raja Sengupta Working
More informationECE 4400:693 - Information Theory
ECE 4400:693 - Information Theory Dr. Nghi Tran Lecture 8: Differential Entropy Dr. Nghi Tran (ECE-University of Akron) ECE 4400:693 Lecture 1 / 43 Outline 1 Review: Entropy of discrete RVs 2 Differential
More information