Green Codes : Energy-efficient short-range communication
|
|
- Kathryn Brown
- 5 years ago
- Views:
Transcription
1 Green Codes : Energy-efficient short-range communication Pulkit Grover Department of Electrical Engineering and Computer Sciences University of California at Berkeley Joint work with Prof. Anant Sahai
2 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors 2
3 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity 2
4 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity - significant power consumed in computations 2
5 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity - significant power consumed in computations total power for communicating 2
6 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity Small battery operated wireless sensors - significant power consumed in computations total power for communicating 2
7 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity - significant power consumed in computations Small battery operated wireless sensors - energy at a premium. total power for communicating 2
8 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity - significant power consumed in computations total power for communicating Small battery operated wireless sensors - energy at a premium. - flexibility in rate. 2
9 Motivation : Understand processing power consumed in communicating Fixed Rate Fixed message size processor with heat sink small sensors Moore s law : decreasing implementation complexity - significant power consumed in computations total power for communicating Small battery operated wireless sensors - energy at a premium. - flexibility in rate. total energy per bit 2
10 Promise of Shannon Theory Fixed Rate: Shannon waterfall!$!$"#.*/0 $! 1!0), 0"02!%!%"#!&!&"#!(!("# :6;*<,<08-56=>?==?* *60758,-95..!#!#"# R = 1/3!'!!"# $ $"# % %"# & &"# )*+,- 3
11 Promise of Shannon Theory Fixed Rate: Shannon waterfall Fixed message size : Verdu On channel capacity per unit cost.*/0 $! 1!0), 0"02!$!$"#!%!%"#!&!&"#!(!("# :6;*<,<08-56=>?==?* *60758,-95..!#!#"# R = 1/3!'!!"# $ $"# % %"# & &"# )*+,- 3
12 Promise of Shannon Theory Fixed Rate: Shannon waterfall Fixed message size : Verdu On channel capacity per unit cost.*/0 $! 1!0), 0"02!$!$"#!%!%"#!&!&"#!(!("# :6;*<,<08-56=>?==?* *60758,-95..!#!#"# R = 1/3!'!!"# $ $"# % %"# & &"# )*+,- Long distance communication - processing power transmit power -- Shannon theory works! Short distance communication - Processing power can be substantial [Agarwal 98, Kravertz et al 98, Goldsmith et al 02, Cui et al 05] 3
13 Information theory + processing power =? Fixed rate!$!$"#.*/0 $! 1!0), 0"02!%!%"#!&!&"#!( :6;*<,<08-56=>?==?* *60758, =?!("#!#!#"#!'!!"# $ $"# % %"# & &"# )*+,- processor with heat sink 4
14 Information theory + processing power =? Fixed rate!$!$"#.*/0 $! 1!0), 0"02!%!%"#!&!&"#!( :6;*<,<08-56=>?==?* *60758, =?!("#!#!#"#!'!!"# $ $"# % %"# & &"# )*+,- Fixed message size processor with heat sink + =? 4 small sensors
15 Talk Outline Motivation: Power consumption - Fixed rate and fixed message size problems. Decoding power using decoding complexity. Complexity-performance tradeoffs. - our bounds for iterative decoding. Fixed rate -- lower bounds on total power. Fixed message size (Green codes) -- lower bounds on min energy. How tight are our bounds : Related coding-theoretic literature 5
16 Modeling processing power through decoding complexity Encoder Decoder 6
17 Modeling processing power through decoding complexity Encoder Decoder power consumed in decoding: model using the decoding complexity - decoding complexity : number of operations performed at the decoder - constant amount of energy per operation. 6
18 Modeling processing power through decoding complexity Encoder Decoder power consumed in decoding: model using the decoding complexity - decoding complexity : number of operations performed at the decoder - constant amount of energy per operation. the common currency: power 6
19 Talk Outline Motivation: Power consumption - Fixed rate and fixed message size problems. Decoding power using decoding complexity. Complexity-performance tradeoffs. - our bounds for iterative decoding. Fixed rate -- lower bounds on total power. Fixed message size (Green codes) -- lower bounds on min energy. How tight are our bounds : Related coding-theoretic literature 7
20 Understanding decoding complexity : complexity - performance tradeoffs complexity-performance tradeoffs : - Required complexity to attain error probability P e and rate R. - Lower bounds : Abstract away from details of code structure. - Upper bounds : code constructions. e.g. block codes : P e exp( me r (R)) e.g. convolution codes : - error exponents with constraint length [Viterbi 67] - cut-off rate for sequential decoding [Jacobs and Berlekamp 67] 8
21 Understanding decoding complexity : complexity - performance tradeoffs complexity-performance tradeoffs : - Required complexity to attain error probability P e and rate R. - Lower bounds : Abstract away from details of code structure. - Upper bounds : code constructions. e.g. block codes : P e exp( me r (R)) e.g. convolution codes : - error exponents with constraint length [Viterbi 67] - cut-off rate for sequential decoding [Jacobs and Berlekamp 67] Want a similar analysis for iterative decoding. 8
22 9 Iterative decoding : Decoding by passing messages Output nodes Y 1 Y 2 Y 3 Y 4 Y 5 Y 6 Y 7 Y 8 Y 9 Decoder implementation graph
23 9 Iterative decoding : Decoding by passing messages Output nodes Y 1 Information nodes B 1 Y 2 B 2 Y 3 Y 4 Y 5 B 3 B 4 B 5 Y 6 Y 7 B 6 B 7 Y 8 Y 9 Decoder implementation graph
24 9 Iterative decoding : Decoding by passing messages Output nodes Y 1 Helper nodes Information nodes B 1 Y 2 B 2 Y 3 B 3 Y 4 B 4 Y 5 B 5 Y 6 B 6 Y 7 Y 8 B 7 Y 9 Decoder implementation graph
25 9 Iterative decoding : Decoding by passing messages Output nodes Y 1 Helper nodes Information nodes B 1 Y 2 B 2 Y 3 B 3 Y 4 B 4 Y 5 B 5 Y 6 B 6 Y 7 Y 8 B 7 Y 9 Decoder implementation graph
26 9 Iterative decoding : Decoding by passing messages Output nodes Y 1 Helper nodes Information nodes B 1 Y 2 B 2 Y 3 B 3 Y 4 B 4 Y 5 B 5 Y 6 B 6 Y 7 Y 8 B 7 Y 9 Decoder implementation graph
27 9 Iterative decoding : Decoding by passing messages Output nodes Helper nodes Information nodes Y 1 Y 2 Y 3 Y 4 Y 5 Y 6 Y 7 Y 8 B 1 B 2 B 3 B 4 B 5 B 6 B 7 Each node consumes Joules of energy per iteration. After l iterations, the energy consumed is γ l # of nodes Each node is connected to at most other nodes -- an implementation constraint. γ α Y 9 Decoder implementation graph
28 9 Iterative decoding : Decoding by passing messages Output nodes Helper nodes Information nodes Y 1 Y 2 Y 3 Y 4 B 1 B 2 B 3 B 4 Each node consumes Joules of energy per iteration. After l iterations, the energy consumed is γ l # of nodes γ Y 5 Y 6 Y 7 B 5 B 6 B 7 Each node is connected to at most other nodes -- an implementation constraint. α Y 8 Y 9 Suffices now to find l Decoder implementation graph
29 Lower bound on l : Key Idea B i a 10
30 Lower bound on l : Key Idea B i a 10
31 Lower bound on l : Key Idea B i a 10
32 Lower bound on l : Key Idea B i a l < a l+1 10
33 Lower bound on l : Key Idea B i a l < a l+1 Channel needs to behave atypically only in the decoding neighborhood to cause an error 10
34 Lower bound on decoding complexity Result [Sahai, Grover, Submitted to IT Trans. 07] In the limit of small P e l 1 log(α) log ( log 1 P e (C R) 2 ) C = Channel capacity R = Rate P e = error probability α = maximum node degree 11
35 Lower bound on decoding complexity l 1 log(α) log ( log 1 P e (C R) 2 ) A general lower bound - applies to all (possible) codes with decoding based on passing messages. - applies regardless of the presence of cycles. - applies to all decoding algorithms based on passing messages. 12
36 Talk Outline Motivation: Power consumption - Fixed rate and fixed message size problems. Decoding power using decoding complexity. Complexity-performance tradeoffs. - our bounds for iterative decoding. Fixed rate -- lower bounds on total power. Fixed message size (Green codes) -- lower bounds on min energy. How tight are our bounds : Related coding-theoretic literature 13
37 Fixed Rate: Total power consumption k Encoder m m Decoder k # of nodes P total = P T + γ l m P T + γ l ( P T + γ log(α) log log 1 P e (C(P T ) R) 2 ) Minimize P total by optimizing over P T l = Number of iterations g = Energy consumed per node per iteration P T = Transmit power m = block-length 14
38 P total P T + Fixed Rate: Total Power Curves γ log(α) log ( log 1 P e (C(P T ) R) 2 )!# R = 1/3.!% /*0 "! 1.),.2!'!"(!$# 34566*6.758,-95//.! " # $ % & )*+,-. 15
39 P total P T + Fixed Rate: Total Power Curves γ log(α) log ( log 1 P e (C(P T ) R) 2 )!# R = 1/3.!% /*0 "! 1.),.2!'!"( 758,-:/;<, =>-?,!$# 34566*6.758,-95//.! " # $ % & )*+,-. 15
40 P total P T + Fixed Rate: Total Power Curves γ log(α) log ( log 1 P e (C(P T ) R) 2 )!# R = 1/3.!% /*0 "! 8-56:B;8.A*+,- 758,-:/;<, =>-?,!$# 34566*6.758,-95//.! " # $ % & )*+,-. 15
41 Fixed Rate: Summary Total power increases unboundedly as P e 0 Optimal transmit power strictly larger than the Shannon limit (transmit power - decoding power tradeoff) 16
42 Talk Outline Motivation: Power consumption - Fixed rate and fixed message size problems. Decoding power using decoding complexity. Complexity-performance tradeoffs. - our bounds for iterative decoding. Fixed rate -- lower bounds on total power. Fixed message size (Green codes) -- lower bounds on min energy. How tight are our bounds : Related coding-theoretic literature 17
43 Fixed message size : Green Codes Minimum energy per-bit k Encoder m m Decoder k E total = mp total = m P T + γ l # of nodes 18
44 Fixed message size : Green Codes Minimum energy per-bit k Encoder m m Decoder k E total = mp total = m P T + γ l # of nodes E per bit = E total k = P T # of nodes + γ l R k 18
45 Fixed message size : Green Codes Minimum energy per-bit k Encoder m m Decoder k E total = mp total = m P T + γ l # of nodes E per bit = E total k = P T # of nodes + γ l R k P T R + γ l max{k, m} k 18
46 Fixed message size: Minimum energy per bit curves!!(!&)!&( 45.!) 6"7, "8!$)!$(!')!'( 9:;++5+"42<23!()!((!"!#$%" &" $" '" ( *+,-./"0,-"123 19
47 Fixed message size: Minimum energy per bit curves!!(!&)!&( 45.!) 6"7, "8!$)!$(!')!'(!()!(( 9:;++5+"42<23 &" $" '" ( *+,-./"0,-"123 Black-box bounds : Based on [Massaad, Medard and Zheng] 19
48 Fixed message size: Minimum energy per bit curves!!( 45.!) 6"7, "8!&)!&(!$)!$(!')!'(!()!(( 9:;++5+"42<23 ""C")#& &" $" '" ( *+,-./"0,-"123 Black-box bounds : Based on [Massaad, Medard and Zheng] 19
49 Fixed message size: Optimal rate curves!)!!$!.+/ &! !(!!'!!151!"6!151!")!#!!%!!&!!!"#$!"%!"%$ & * +,- 20
50 Fixed message size: Summary Minimum energy per bit increases to infinity as P e 0 - compare with a constant, ln(4), in classical information theory. Optimizing rate converges to 1. - zero in classical information theory. 21
51 Talk Outline Motivation: Power consumption - Fixed rate and fixed message size problems. Decoding power using decoding complexity. Complexity-performance tradeoffs. - our bounds for iterative decoding. Fixed rate -- lower bounds on total power. Fixed message size (Green codes) -- lower bounds on min energy. How tight are our bounds : Related coding-theoretic literature 22
52 Lower bounds on complexity: how tight are they? l 1 log(α) log ( log 1 P e (C R) 2 ) y y = x Optimal behavior with respect to P e y = f! x " - regular LDPC s achieve this! [Lentmaier et al] gap = C R what about behavior with? x 23
53 Complexity behavior with gap = C R [Gallager, Burshtein et al, Sason-Urbanke] Lower bounds on density for LDPCs. [Pfister-Sason, Hsu-Anastastopoulos] Upper bounds. ( ) 1 Khandekar-McEliece conjecture: l Ω C R [Sason, Weichman] For LDPCs, IRAs, ARAs, if there are a nonzero fraction of degree 2 nodes, and the graph is a tree, the conjecture holds. ( ) 1 - but with degree-2 nodes, l log - and it seems that degree-2 nodes are needed to approach capacity. - from energy perspective, is it worth approaching capacity? P e 24
54 Thank you Full paper on arxiv - The price of certainty: Waterslide curves and the gap to capacity. Anant Sahai and Pulkit Grover. 25
Time Division Multiplexing for Green Broadcasting
Time Division Multiplexing for Green Broadcasting Pulkit Grover UC Berkeley with Anant Sahai There are handouts for this talk. Please take one! Short-distance green communication C = W 2 log (1 + SNR)
More informationTowards a communication-theoretic understanding of system-level power consumption
Towards a communication-theoretic understanding of system-level power consumption 1 Pulkit Grover, Kristen Ann Woyach and Anant Sahai University of California, Berkeley, Berkeley, CA-9470 Abstract Traditional
More informationMULTILEVEL CODING (MLC) with multistage decoding
350 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 52, NO. 3, MARCH 2004 Power- and Bandwidth-Efficient Communications Using LDPC Codes Piraporn Limpaphayom, Student Member, IEEE, and Kim A. Winick, Senior
More informationJoint Relaying and Network Coding in Wireless Networks
Joint Relaying and Network Coding in Wireless Networks Sachin Katti Ivana Marić Andrea Goldsmith Dina Katabi Muriel Médard MIT Stanford Stanford MIT MIT Abstract Relaying is a fundamental building block
More informationCT-516 Advanced Digital Communications
CT-516 Advanced Digital Communications Yash Vasavada Winter 2017 DA-IICT Lecture 17 Channel Coding and Power/Bandwidth Tradeoff 20 th April 2017 Power and Bandwidth Tradeoff (for achieving a particular
More informationIEEE C /02R1. IEEE Mobile Broadband Wireless Access <http://grouper.ieee.org/groups/802/mbwa>
23--29 IEEE C82.2-3/2R Project Title Date Submitted IEEE 82.2 Mobile Broadband Wireless Access Soft Iterative Decoding for Mobile Wireless Communications 23--29
More informationCapacity-Approaching Bandwidth-Efficient Coded Modulation Schemes Based on Low-Density Parity-Check Codes
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO. 9, SEPTEMBER 2003 2141 Capacity-Approaching Bandwidth-Efficient Coded Modulation Schemes Based on Low-Density Parity-Check Codes Jilei Hou, Student
More informationEncoding of Control Information and Data for Downlink Broadcast of Short Packets
Encoding of Control Information and Data for Downlin Broadcast of Short Pacets Kasper Fløe Trillingsgaard and Petar Popovsi Department of Electronic Systems, Aalborg University 9220 Aalborg, Denmar Abstract
More informationBackground Dirty Paper Coding Codeword Binning Code construction Remaining problems. Information Hiding. Phil Regalia
Information Hiding Phil Regalia Department of Electrical Engineering and Computer Science Catholic University of America Washington, DC 20064 regalia@cua.edu Baltimore IEEE Signal Processing Society Chapter,
More informationDigital Television Lecture 5
Digital Television Lecture 5 Forward Error Correction (FEC) Åbo Akademi University Domkyrkotorget 5 Åbo 8.4. Error Correction in Transmissions Need for error correction in transmissions Loss of data during
More informationwireless transmission of short packets
wireless transmission of short packets Petar Popovski Aalborg University, Denmark AAU, June 2016 P. Popovski (Aalborg Uni) short packets AAU, Jun. 2016 1 / 19 short data packets gaining in importance with
More informationPolar Codes for Probabilistic Amplitude Shaping
Polar Codes for Probabilistic Amplitude Shaping Tobias Prinz tobias.prinz@tum.de Second LNT & DLR Summer Workshop on Coding July 26, 2016 Tobias Prinz Polar Codes for Probabilistic Amplitude Shaping 1/16
More informationPower Efficiency of LDPC Codes under Hard and Soft Decision QAM Modulated OFDM
Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 5 (2014), pp. 463-468 Research India Publications http://www.ripublication.com/aeee.htm Power Efficiency of LDPC Codes under
More informationNear-Optimal Radio Use For Wireless Network Synch. Synchronization
Near-Optimal Radio Use For Wireless Network Synchronization LANL, UCLA 10th of July, 2009 Motivation Consider sensor network: tiny, inexpensive embedded computers run complex software sense environmental
More informationFrom Shared Memory to Message Passing
From Shared Memory to Message Passing Stefan Schmid T-Labs / TU Berlin Some parts of the lecture, parts of the Skript and exercises will be based on the lectures of Prof. Roger Wattenhofer at ETH Zurich
More informationDigital Communications I: Modulation and Coding Course. Term Catharina Logothetis Lecture 12
Digital Communications I: Modulation and Coding Course Term 3-8 Catharina Logothetis Lecture Last time, we talked about: How decoding is performed for Convolutional codes? What is a Maximum likelihood
More informationLDPC codes for OFDM over an Inter-symbol Interference Channel
LDPC codes for OFDM over an Inter-symbol Interference Channel Dileep M. K. Bhashyam Andrew Thangaraj Department of Electrical Engineering IIT Madras June 16, 2008 Outline 1 LDPC codes OFDM Prior work Our
More informationFOR THE PAST few years, there has been a great amount
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 53, NO. 4, APRIL 2005 549 Transactions Letters On Implementation of Min-Sum Algorithm and Its Modifications for Decoding Low-Density Parity-Check (LDPC) Codes
More informationProject. Title. Submitted Sources: {se.park,
Project Title Date Submitted Sources: Re: Abstract Purpose Notice Release Patent Policy IEEE 802.20 Working Group on Mobile Broadband Wireless Access LDPC Code
More informationDecoding Turbo Codes and LDPC Codes via Linear Programming
Decoding Turbo Codes and LDPC Codes via Linear Programming Jon Feldman David Karger jonfeld@theorylcsmitedu karger@theorylcsmitedu MIT LCS Martin Wainwright martinw@eecsberkeleyedu UC Berkeley MIT LCS
More informationPunctured vs Rateless Codes for Hybrid ARQ
Punctured vs Rateless Codes for Hybrid ARQ Emina Soljanin Mathematical and Algorithmic Sciences Research, Bell Labs Collaborations with R. Liu, P. Spasojevic, N. Varnica and P. Whiting Tsinghua University
More informationIEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 1, JANUARY
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 1, JANUARY 2004 31 Product Accumulate Codes: A Class of Codes With Near-Capacity Performance and Low Decoding Complexity Jing Li, Member, IEEE, Krishna
More informationA Survey of Advanced FEC Systems
A Survey of Advanced FEC Systems Eric Jacobsen Minister of Algorithms, Intel Labs Communication Technology Laboratory/ Radio Communications Laboratory July 29, 2004 With a lot of material from Bo Xia,
More informationA Bit of network information theory
Š#/,% 0/,94%#(.)15% A Bit of network information theory Suhas Diggavi 1 Email: suhas.diggavi@epfl.ch URL: http://licos.epfl.ch Parts of talk are joint work with S. Avestimehr 2, S. Mohajer 1, C. Tian 3,
More informationPerformance Analysis and Improvements for the Future Aeronautical Mobile Airport Communications System. Candidate: Paola Pulini Advisor: Marco Chiani
Performance Analysis and Improvements for the Future Aeronautical Mobile Airport Communications System (AeroMACS) Candidate: Paola Pulini Advisor: Marco Chiani Outline Introduction and Motivations Thesis
More informationNotes 15: Concatenated Codes, Turbo Codes and Iterative Processing
16.548 Notes 15: Concatenated Codes, Turbo Codes and Iterative Processing Outline! Introduction " Pushing the Bounds on Channel Capacity " Theory of Iterative Decoding " Recursive Convolutional Coding
More informationPractical Cooperative Coding for Half-Duplex Relay Channels
Practical Cooperative Coding for Half-Duplex Relay Channels Noah Jacobsen Alcatel-Lucent 600 Mountain Avenue Murray Hill, NJ 07974 jacobsen@alcatel-lucent.com Abstract Simple variations on rate-compatible
More informationBoosting reliability over AWGN networks with average power constraints and noiseless feedback
Boosting reliability over AWGN networks with average power constraints and noiseless feedback Anant Sahai, Stark C. Draper, and Michael Gastpar Department of EECS, University of California, Berkeley, CA,
More informationChapter 3 Convolutional Codes and Trellis Coded Modulation
Chapter 3 Convolutional Codes and Trellis Coded Modulation 3. Encoder Structure and Trellis Representation 3. Systematic Convolutional Codes 3.3 Viterbi Decoding Algorithm 3.4 BCJR Decoding Algorithm 3.5
More informationOn the Feasibility and Performance of CDMA with Interference Cancellation
On the Feasibility and Performance of CDMA with Interference Cancellation Jack Keil Wolf QUALCOMM Incorporated Plenary Session ISSSTA 2006 August 29, 2006 Acknowledgements This presentation is based upon
More informationVLSI Design for High-Speed Sparse Parity-Check Matrix Decoders
VLSI Design for High-Speed Sparse Parity-Check Matrix Decoders Mohammad M. Mansour Department of Electrical and Computer Engineering American University of Beirut Beirut, Lebanon 7 22 Email: mmansour@aub.edu.lb
More informationAnytime coding on the infinite bandwidth AWGN channel: A sequential semi-orthogonal optimal code. Anant Sahai
Anytime coding on the infinite bandwidth AWGN channel: A sequential semi-orthogonal optimal code Anant Sahai sahai@eecs.berkeley.edu Abstract It is well known that orthogonal coding can be used to approach
More informationBandwidth Scaling in Ultra Wideband Communication 1
Bandwidth Scaling in Ultra Wideband Communication 1 Dana Porrat dporrat@wireless.stanford.edu David Tse dtse@eecs.berkeley.edu Department of Electrical Engineering and Computer Sciences University of California,
More informationDecoding of Block Turbo Codes
Decoding of Block Turbo Codes Mathematical Methods for Cryptography Dedicated to Celebrate Prof. Tor Helleseth s 70 th Birthday September 4-8, 2017 Kyeongcheol Yang Pohang University of Science and Technology
More informationTransmit Power Allocation for BER Performance Improvement in Multicarrier Systems
Transmit Power Allocation for Performance Improvement in Systems Chang Soon Par O and wang Bo (Ed) Lee School of Electrical Engineering and Computer Science, Seoul National University parcs@mobile.snu.ac.r,
More informationBursty Transmission and Glue Pouring: On Wireless Channels with Overhead Costs
588 I TRANSACTIONS ON WIRLSS COMMUNICATIONS, VOL. 7, NO. 2, DCMBR 2008 Bursty Transmission and Glue Pouring: On Wireless Channels with Overhead Costs Pamela Youssef-Massaad, Lizhong Zheng, and Muriel Médard
More informationIN A direct-sequence code-division multiple-access (DS-
2636 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO. 6, NOVEMBER 2005 Optimal Bandwidth Allocation to Coding and Spreading in DS-CDMA Systems Using LMMSE Front-End Detector Manish Agarwal, Kunal
More informationM2M massive wireless access: challenges, research issues, and ways forward
M2M massive wireless access: challenges, research issues, and ways forward Petar Popovski Aalborg University Andrea Zanella, Michele Zorzi André D. F. Santos Uni Padova Alcatel Lucent Nuno Pratas, Cedomir
More informationEDI042 Error Control Coding (Kodningsteknik)
EDI042 Error Control Coding (Kodningsteknik) Chapter 1: Introduction Michael Lentmaier November 3, 2014 Michael Lentmaier, Fall 2014 EDI042 Error Control Coding: Chapter 1 1 / 26 Course overview I Lectures:
More informationJoint work with Dragana Bajović and Dušan Jakovetić. DLR/TUM Workshop, Munich,
Slotted ALOHA in Small Cell Networks: How to Design Codes on Random Geometric Graphs? Dejan Vukobratović Associate Professor, DEET-UNS University of Novi Sad, Serbia Joint work with Dragana Bajović and
More informationConvolutional Coding Using Booth Algorithm For Application in Wireless Communication
Available online at www.interscience.in Convolutional Coding Using Booth Algorithm For Application in Wireless Communication Sishir Kalita, Parismita Gogoi & Kandarpa Kumar Sarma Department of Electronics
More informationOutline. Communications Engineering 1
Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband channels Signal space representation Optimal
More informationFrom Fountain to BATS: Realization of Network Coding
From Fountain to BATS: Realization of Network Coding Shenghao Yang Jan 26, 2015 Shenzhen Shenghao Yang Jan 26, 2015 1 / 35 Outline 1 Outline 2 Single-Hop: Fountain Codes LT Codes Raptor codes: achieving
More informationIncremental Redundancy and Feedback at Finite Blocklengths
Incremental Redundancy and Feedbac at Finite Bloclengths Richard Wesel, Kasra Vailinia, Adam Williamson Munich Worshop on Coding and Modulation, July 30-31, 2015 1 Lower Bound on Benefit of Feedbac 0.7
More informationn Based on the decision rule Po- Ning Chapter Po- Ning Chapter
n Soft decision decoding (can be analyzed via an equivalent binary-input additive white Gaussian noise channel) o The error rate of Ungerboeck codes (particularly at high SNR) is dominated by the two codewords
More informationPerformance and Complexity Tradeoffs of Space-Time Modulation and Coding Schemes
Performance and Complexity Tradeoffs of Space-Time Modulation and Coding Schemes The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation
More informationLDPC Decoding: VLSI Architectures and Implementations
LDPC Decoding: VLSI Architectures and Implementations Module : LDPC Decoding Ned Varnica varnica@gmail.com Marvell Semiconductor Inc Overview Error Correction Codes (ECC) Intro to Low-density parity-check
More informationWireless Network Information Flow
Š#/,% 0/,94%#(.)15% Wireless Network Information Flow Suhas iggavi School of Computer and Communication Sciences, Laboratory for Information and Communication Systems (LICOS), EPFL Email: suhas.diggavi@epfl.ch
More informationECE 8771, Information Theory & Coding for Digital Communications Summer 2010 Syllabus & Outline (Draft 1 - May 12, 2010)
ECE 8771, Information Theory & Coding for Digital Communications Summer 2010 Syllabus & Outline (Draft 1 - May 12, 2010) Instructor: Kevin Buckley, Tolentine 433a, 610-519-5658 (W), 610-519-4436 (F), buckley@ece.vill.edu,
More informationDegrees of Freedom in Adaptive Modulation: A Unified View
Degrees of Freedom in Adaptive Modulation: A Unified View Seong Taek Chung and Andrea Goldsmith Stanford University Wireless System Laboratory David Packard Building Stanford, CA, U.S.A. taek,andrea @systems.stanford.edu
More informationShort-Blocklength Non-Binary LDPC Codes with Feedback-Dependent Incremental Transmissions
Short-Blocklength Non-Binary LDPC Codes with Feedback-Dependent Incremental Transmissions Kasra Vakilinia, Tsung-Yi Chen*, Sudarsan V. S. Ranganathan, Adam R. Williamson, Dariush Divsalar**, and Richard
More informationSC-LDPC Codes over the Block-Fading Channel: Robustness to a Synchronisation Offset
SC-LDPC Codes over the Block-Fading Channel: Robustness to a Synchronisation Offset Andriyanova, Iryna; ul Hassan, Najeeb; Lentmaier, Michael; Fettweis, Gerhard Published in: [Host publication title missing]
More informationOptimal Coded Information Network Design and Management via Improved Characterizations of the Binary Entropy Function
Optimal Coded Information Network Design and Management via Improved Characterizations of the Binary Entropy Function John MacLaren Walsh & Steven Weber Department of Electrical and Computer Engineering
More informationDiversity Gain Region for MIMO Fading Multiple Access Channels
Diversity Gain Region for MIMO Fading Multiple Access Channels Lihua Weng, Sandeep Pradhan and Achilleas Anastasopoulos Electrical Engineering and Computer Science Dept. University of Michigan, Ann Arbor,
More informationOptimization Techniques for Alphabet-Constrained Signal Design
Optimization Techniques for Alphabet-Constrained Signal Design Mojtaba Soltanalian Department of Electrical Engineering California Institute of Technology Stanford EE- ISL Mar. 2015 Optimization Techniques
More informationVector-LDPC Codes for Mobile Broadband Communications
Vector-LDPC Codes for Mobile Broadband Communications Whitepaper November 23 Flarion Technologies, Inc. Bedminster One 35 Route 22/26 South Bedminster, NJ 792 Tel: + 98-947-7 Fax: + 98-947-25 www.flarion.com
More informationColor of Interference and Joint Encoding and Medium Access in Large Wireless Networks
Color of Interference and Joint Encoding and Medium Access in Large Wireless Networks Nithin Sugavanam, C. Emre Koksal, Atilla Eryilmaz Department of Electrical and Computer Engineering The Ohio State
More informationPerformance of Single-tone and Two-tone Frequency-shift Keying for Ultrawideband
erformance of Single-tone and Two-tone Frequency-shift Keying for Ultrawideband Cheng Luo Muriel Médard Electrical Engineering Electrical Engineering and Computer Science, and Computer Science, Massachusetts
More informationComputing and Communications 2. Information Theory -Channel Capacity
1896 1920 1987 2006 Computing and Communications 2. Information Theory -Channel Capacity Ying Cui Department of Electronic Engineering Shanghai Jiao Tong University, China 2017, Autumn 1 Outline Communication
More informationarxiv: v2 [cs.it] 29 Mar 2014
1 Spectral Efficiency and Outage Performance for Hybrid D2D-Infrastructure Uplink Cooperation Ahmad Abu Al Haija and Mai Vu Abstract arxiv:1312.2169v2 [cs.it] 29 Mar 2014 We propose a time-division uplink
More informationPerformance of Combined Error Correction and Error Detection for very Short Block Length Codes
Performance of Combined Error Correction and Error Detection for very Short Block Length Codes Matthias Breuninger and Joachim Speidel Institute of Telecommunications, University of Stuttgart Pfaffenwaldring
More information6. FUNDAMENTALS OF CHANNEL CODER
82 6. FUNDAMENTALS OF CHANNEL CODER 6.1 INTRODUCTION The digital information can be transmitted over the channel using different signaling schemes. The type of the signal scheme chosen mainly depends on
More informationQ-ary LDPC Decoders with Reduced Complexity
Q-ary LDPC Decoders with Reduced Complexity X. H. Shen & F. C. M. Lau Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong Email: shenxh@eie.polyu.edu.hk
More informationOptimized Codes for the Binary Coded Side-Information Problem
Optimized Codes for the Binary Coded Side-Information Problem Anne Savard, Claudio Weidmann ETIS / ENSEA - Université de Cergy-Pontoise - CNRS UMR 8051 F-95000 Cergy-Pontoise Cedex, France Outline 1 Introduction
More informationDegrees of Freedom of the MIMO X Channel
Degrees of Freedom of the MIMO X Channel Syed A. Jafar Electrical Engineering and Computer Science University of California Irvine Irvine California 9697 USA Email: syed@uci.edu Shlomo Shamai (Shitz) Department
More informationRecent Progress in Mobile Transmission
Recent Progress in Mobile Transmission Joachim Hagenauer Institute for Communications Engineering () Munich University of Technology (TUM) D-80290 München, Germany State University of Telecommunications
More informationOn the Practicality of Low-Density Parity-Check Codes
On the Practicality of Low-Density Parity-Check Codes Alex C. Snoeren MIT Lab for Computer Science Cambridge, MA 0138 snoeren@lcs.mit.edu June 7, 001 Abstract Recent advances in coding theory have produced
More informationTransport Capacity and Spectral Efficiency of Large Wireless CDMA Ad Hoc Networks
Transport Capacity and Spectral Efficiency of Large Wireless CDMA Ad Hoc Networks Yi Sun Department of Electrical Engineering The City College of City University of New York Acknowledgement: supported
More informationUNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences
UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences Jan Rabaey EECS 141 Spring 2010 LDPC Decoder Project Phase 3 Due Noon, Wednesday, May 5th, 2010
More informationMultitree Decoding and Multitree-Aided LDPC Decoding
Multitree Decoding and Multitree-Aided LDPC Decoding Maja Ostojic and Hans-Andrea Loeliger Dept. of Information Technology and Electrical Engineering ETH Zurich, Switzerland Email: {ostojic,loeliger}@isi.ee.ethz.ch
More informationPERFORMANCE OF DISTRIBUTED UTILITY-BASED POWER CONTROL FOR WIRELESS AD HOC NETWORKS
PERFORMANCE OF DISTRIBUTED UTILITY-BASED POWER CONTROL FOR WIRELESS AD HOC NETWORKS Jianwei Huang, Randall Berry, Michael L. Honig Department of Electrical and Computer Engineering Northwestern University
More informationError Performance of Channel Coding in Random-Access Communication
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 58, NO. 6, JUNE 2012 3961 Error Performance of Channel Coding in Random-Access Communication Zheng Wang, Student Member, IEEE, andjieluo, Member, IEEE Abstract
More informationThe Multi-way Relay Channel
The Multi-way Relay Channel Deniz Gündüz, Aylin Yener, Andrea Goldsmith, H. Vincent Poor Department of Electrical Engineering, Stanford University, Stanford, CA Department of Electrical Engineering, Princeton
More informationSimulink Modeling of Convolutional Encoders
Simulink Modeling of Convolutional Encoders * Ahiara Wilson C and ** Iroegbu Chbuisi, *Department of Computer Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria **Department
More informationRouting in Massively Dense Static Sensor Networks
Routing in Massively Dense Static Sensor Networks Eitan ALTMAN, Pierre BERNHARD, Alonso SILVA* July 15, 2008 Altman, Bernhard, Silva* Routing in Massively Dense Static Sensor Networks 1/27 Table of Contents
More informationAN INTRODUCTION TO ERROR CORRECTING CODES Part 2
AN INTRODUCTION TO ERROR CORRECTING CODES Part Jack Keil Wolf ECE 54 C Spring BINARY CONVOLUTIONAL CODES A binary convolutional code is a set of infinite length binary sequences which satisfy a certain
More informationGoa, India, October Question: 4/15 SOURCE 1 : IBM. G.gen: Low-density parity-check codes for DSL transmission.
ITU - Telecommunication Standardization Sector STUDY GROUP 15 Temporary Document BI-095 Original: English Goa, India, 3 7 October 000 Question: 4/15 SOURCE 1 : IBM TITLE: G.gen: Low-density parity-check
More informationInformation flow over wireless networks: a deterministic approach
Information flow over wireless networks: a deterministic approach alman Avestimehr In collaboration with uhas iggavi (EPFL) and avid Tse (UC Berkeley) Overview Point-to-point channel Information theory
More informationHype, Myths, Fundamental Limits and New Directions in Wireless Systems
Hype, Myths, Fundamental Limits and New Directions in Wireless Systems Reinaldo A. Valenzuela, Director, Wireless Communications Research Dept., Bell Laboratories Rutgers, December, 2007 Need to greatly
More informationCoding for Super Dense Networks 1. JAIST SAST 2015 Nomi, November 2015
Coding for Super Dense Networks 1 Mohammad Nur Hasan and Khoirul Anwar School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) Email : {hasan-mn, anwar-k}@jaist.ac.jp
More informationSENSOR PLACEMENT FOR MAXIMIZING LIFETIME PER UNIT COST IN WIRELESS SENSOR NETWORKS
SENSOR PACEMENT FOR MAXIMIZING IFETIME PER UNIT COST IN WIREESS SENSOR NETWORKS Yunxia Chen, Chen-Nee Chuah, and Qing Zhao Department of Electrical and Computer Engineering University of California, Davis,
More informationLecture 4: Wireless Physical Layer: Channel Coding. Mythili Vutukuru CS 653 Spring 2014 Jan 16, Thursday
Lecture 4: Wireless Physical Layer: Channel Coding Mythili Vutukuru CS 653 Spring 2014 Jan 16, Thursday Channel Coding Modulated waveforms disrupted by signal propagation through wireless channel leads
More informationPerformance Optimization of Hybrid Combination of LDPC and RS Codes Using Image Transmission System Over Fading Channels
European Journal of Scientific Research ISSN 1450-216X Vol.35 No.1 (2009), pp 34-42 EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/ejsr.htm Performance Optimization of Hybrid Combination
More informationOn Joint Decoding and Random CDMA Demodulation
On Joint and Random CDMA Demodulation Christian Schlegel, Dmitri Truhachev, and Łukasz Krzymień Department of Electrical and Computer Engineering University of Alberta Edmonton, AB, CANADA email:{schlegel,dmitrytr,lukaszk}@ece.ualberta.ca
More informationTwo Models for Noisy Feedback in MIMO Channels
Two Models for Noisy Feedback in MIMO Channels Vaneet Aggarwal Princeton University Princeton, NJ 08544 vaggarwa@princeton.edu Gajanana Krishna Stanford University Stanford, CA 94305 gkrishna@stanford.edu
More informationOverview of Message Passing Algorithms for Cooperative Localization in UWB wireless networks. Samuel Van de Velde
Overview of Message Passing Algorithms for Cooperative Localization in UWB wireless networks Samuel Van de Velde Samuel.VandeVelde@telin.ugent.be Promotor: Heidi Steendam Co-promotor Marc Moeneclaey, Henk
More informationVariations on the Index Coding Problem: Pliable Index Coding and Caching
Variations on the Index Coding Problem: Pliable Index Coding and Caching T. Liu K. Wan D. Tuninetti University of Illinois at Chicago Shannon s Centennial, Chicago, September 23rd 2016 D. Tuninetti (UIC)
More informationIterative Joint Source/Channel Decoding for JPEG2000
Iterative Joint Source/Channel Decoding for JPEG Lingling Pu, Zhenyu Wu, Ali Bilgin, Michael W. Marcellin, and Bane Vasic Dept. of Electrical and Computer Engineering The University of Arizona, Tucson,
More informationPerformance Evaluation of Low Density Parity Check codes with Hard and Soft decision Decoding
Performance Evaluation of Low Density Parity Check codes with Hard and Soft decision Decoding Shalini Bahel, Jasdeep Singh Abstract The Low Density Parity Check (LDPC) codes have received a considerable
More informationJoint Optimization of Relay Strategies and Resource Allocations in Cooperative Cellular Networks
Joint Optimization of Relay Strategies and Resource Allocations in Cooperative Cellular Networks Truman Ng, Wei Yu Electrical and Computer Engineering Department University of Toronto Jianzhong (Charlie)
More informationGenetic Algorithm Based Charge Optimization of Lithium-Ion Batteries in Small Satellites. Saurabh Jain Dan Simon
Genetic Algorithm Based Charge Optimization of Lithium-Ion Batteries in Small Satellites Saurabh Jain Dan Simon Outline Problem Identification Solution approaches Our strategy Problem representation Modified
More informationCommunications Overhead as the Cost of Constraints
Communications Overhead as the Cost of Constraints J. Nicholas Laneman and Brian. Dunn Department of Electrical Engineering University of Notre Dame Email: {jnl,bdunn}@nd.edu Abstract This paper speculates
More informationDynamic Network Energy Management via Proximal Message Passing
Dynamic Network Energy Management via Proximal Message Passing Matt Kraning, Eric Chu, Javad Lavaei, and Stephen Boyd Google, 2/20/2013 1 Outline Introduction Model Device examples Algorithm Numerical
More informationWireless Multicasting with Channel Uncertainty
Wireless Multicasting with Channel Uncertainty Jie Luo ECE Dept., Colorado State Univ. Fort Collins, Colorado 80523 e-mail: rockey@eng.colostate.edu Anthony Ephremides ECE Dept., Univ. of Maryland College
More informationMAS.160 / MAS.510 / MAS.511 Signals, Systems and Information for Media Technology Fall 2007
MIT OpenCourseWare http://ocw.mit.edu MAS.160 / MAS.510 / MAS.511 Signals, Systems and Information for Media Technology Fall 2007 For information about citing these materials or our Terms of Use, visit:
More informationPower Control and Utility Optimization in Wireless Communication Systems
Power Control and Utility Optimization in Wireless Communication Systems Dimitrie C. Popescu and Anthony T. Chronopoulos Electrical Engineering Dept. Computer Science Dept. University of Texas at San Antonio
More informationDirty Paper Coding vs. TDMA for MIMO Broadcast Channels
1 Dirty Paper Coding vs. TDMA for MIMO Broadcast Channels Nihar Jindal & Andrea Goldsmith Dept. of Electrical Engineering, Stanford University njindal, andrea@systems.stanford.edu Submitted to IEEE Trans.
More information3432 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 53, NO. 10, OCTOBER 2007
3432 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 53, NO 10, OCTOBER 2007 Resource Allocation for Wireless Fading Relay Channels: Max-Min Solution Yingbin Liang, Member, IEEE, Venugopal V Veeravalli, Fellow,
More informationPattern Avoidance in Poset Permutations
Pattern Avoidance in Poset Permutations Sam Hopkins and Morgan Weiler Massachusetts Institute of Technology and University of California, Berkeley Permutation Patterns, Paris; July 5th, 2013 1 Definitions
More informationRandom access on graphs: Capture-or tree evaluation
Random access on graphs: Capture-or tree evaluation Čedomir Stefanović, cs@es.aau.dk joint work with Petar Popovski, AAU 1 Preliminaries N users Each user wants to send a packet over shared medium Eual
More information