Low-Density Parity-Check Codes : Unequal Error Protection And
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Notes for Lecture 2
Notes on Complexity Theory Last updated: December, 2011 Lecture 2 Jonathan Katz 1 Review The running time of a Turing machine M on input x is the number of \steps" M takes before it halts. Machine M is said to run in time T (¢) if for every input x the running time of M(x) is at most T (jxj). (In particular, this means it halts on all inputs.) The space used by M on input x is the number of cells written to by M on all its work tapes1 (a cell that is written to multiple times is only counted once); M is said to use space T (¢) if for every input x the space used during the computation of M(x) is at most T (jxj). We remark that these time and space measures are worst-case notions; i.e., even if M runs in time T (n) for only a fraction of the inputs of length n (and uses less time for all other inputs of length n), the running time of M is still T . (Average-case notions of complexity have also been considered, but are somewhat more di±cult to reason about. We may cover this later in the semester; or see [1, Chap. 18].) Recall that a Turing machine M computes a function f : f0; 1g¤ ! f0; 1g¤ if M(x) = f(x) for all x. We will focus most of our attention on boolean functions, a context in which it is more convenient to phrase computation in terms of languages. A language is simply a subset of f0; 1g¤. -
Ts 136 212 V11.1.0 (2013-02)
ETSI TS 136 212 V11.1.0 (2013-02) Technical Specification LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding (3GPP TS 36.212 version 11.1.0 Release 11) 3GPP TS 36.212 version 11.1.0 Release 11 1 ETSI TS 136 212 V11.1.0 (2013-02) Reference RTS/TSGR-0136212vb10 Keywords LTE ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice Individual copies of the present document can be downloaded from: http://www.etsi.org The present document may be made available in more than one electronic version or in print. In any case of existing or perceived difference in contents between such versions, the reference version is the Portable Document Format (PDF). In case of dispute, the reference shall be the printing on ETSI printers of the PDF version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http://portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced except as authorized by written permission. -
Tm Synchronization and Channel Coding—Summary of Concept and Rationale
Report Concerning Space Data System Standards TM SYNCHRONIZATION AND CHANNEL CODING— SUMMARY OF CONCEPT AND RATIONALE INFORMATIONAL REPORT CCSDS 130.1-G-3 GREEN BOOK June 2020 Report Concerning Space Data System Standards TM SYNCHRONIZATION AND CHANNEL CODING— SUMMARY OF CONCEPT AND RATIONALE INFORMATIONAL REPORT CCSDS 130.1-G-3 GREEN BOOK June 2020 TM SYNCHRONIZATION AND CHANNEL CODING—SUMMARY OF CONCEPT AND RATIONALE AUTHORITY Issue: Informational Report, Issue 3 Date: June 2020 Location: Washington, DC, USA This document has been approved for publication by the Management Council of the Consultative Committee for Space Data Systems (CCSDS) and reflects the consensus of technical panel experts from CCSDS Member Agencies. The procedure for review and authorization of CCSDS Reports is detailed in Organization and Processes for the Consultative Committee for Space Data Systems (CCSDS A02.1-Y-4). This document is published and maintained by: CCSDS Secretariat National Aeronautics and Space Administration Washington, DC, USA Email: [email protected] CCSDS 130.1-G-3 Page i June 2020 TM SYNCHRONIZATION AND CHANNEL CODING—SUMMARY OF CONCEPT AND RATIONALE FOREWORD This document is a CCSDS Report that contains background and explanatory material to support the CCSDS Recommended Standard, TM Synchronization and Channel Coding (reference [3]). Through the process of normal evolution, it is expected that expansion, deletion, or modification of this document may occur. This Report is therefore subject to CCSDS document management and change control procedures, which are defined in Organization and Processes for the Consultative Committee for Space Data Systems (CCSDS A02.1-Y-4). Current versions of CCSDS documents are maintained at the CCSDS Web site: http://www.ccsds.org/ Questions relating to the contents or status of this document should be sent to the CCSDS Secretariat at the email address indicated on page i. -
Ioag Infusion Plans and Roadmaps
IOAG ROADMAP DRAFT IOAG INFUSION PLANS AND ROADMAPS (this version contains all IOAG agency(s) input) (Draft IOAG-8 Version – July 2005) (Draft IOAG-8 Version, Revision 1 – August 2005) IOAG ROADMAP This document is uncontrolled when printed. Please check the IOAG web site at http://www.ioag.org prior to use to ensure this is the latest version. IOAG ROADMAP TABLE OF CONTENTS 1 – INTRODUCTION .................................................................................................................1 2 – STRUCTURE AND MANAGEMENT OF THE PRESENT DOCUMENT...........................1 2-1 STRUCTURE ..................................................................................................................1 2-2 DOCUMENT MANAGEMENT..........................................................................................2 3 – IOAG STRATEGY PLAN....................................................................................................2 3-1 IOAG OBJECTIVES ........................................................................................................2 3-2 IOAG AREAS OF INTEREST ..........................................................................................3 4 – IOAG INTEROPERABILITY ITEMS...................................................................................6 5 – AGENCIES’ INFUSION PLANS AND ROADMAPS..........................................................7 ANNEX-1 – ASI INFUSION PLAN AND ROADMAP ...........................................................1-1 ANNEX-2 – CNES INFUSION PLAN AND ROADMAP -
MHOMS: High Speed ACM Modem for Satellite Applications1
MHOMS : high speed ACM modem for satellite applications Sergio Benedetto, Claude Berrou, Catherine Douillard, Roberto Garello, Domenico Giancristofaro, Alberto Ginesi, Luca Giugno, Marco Luise, G. Montorsi To cite this version: Sergio Benedetto, Claude Berrou, Catherine Douillard, Roberto Garello, Domenico Giancristofaro, et al.. MHOMS : high speed ACM modem for satellite applications. IEEE Wireless Communications, In- stitute of Electrical and Electronics Engineers, 2005, 12 (2), pp.66 - 77. 10.1109/MWC.2005.1421930. hal-02137104 HAL Id: hal-02137104 https://hal.archives-ouvertes.fr/hal-02137104 Submitted on 22 May 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. MHOMS: High Speed ACM Modem for Satellite Applications1 S. Benedetto(3), C. Berrou(5), C. Douillard(5), R. Garello(3), D. Giancristofaro(1), A. Ginesi(2), L. Giugno(4), M. Luise(4), G. Montorsi(3), (1) Alenia Spazio (2) European Space Agency (3) Politecnico di Torino (4) Università di Pisa (5) ENST Bretagne Table of Contents 1 Introduction...................................................................................................................................................................... -
The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. -
When Subgraph Isomorphism Is Really Hard, and Why This Matters for Graph Databases Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble
When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble To cite this version: Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble. When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases. Journal of Artificial Intelligence Research, Association for the Advancement of Artificial Intelligence, 2018, 61, pp.723 - 759. 10.1613/jair.5768. hal-01741928 HAL Id: hal-01741928 https://hal.archives-ouvertes.fr/hal-01741928 Submitted on 26 Mar 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Journal of Artificial Intelligence Research 61 (2018) 723-759 Submitted 11/17; published 03/18 When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases Ciaran McCreesh [email protected] Patrick Prosser [email protected] University of Glasgow, Glasgow, Scotland Christine Solnon [email protected] INSA-Lyon, LIRIS, UMR5205, F-69621, France James Trimble [email protected] University of Glasgow, Glasgow, Scotland Abstract The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. -
Hamming Code - Wikipedia, the Free Encyclopedia Hamming Code Hamming Code
Hamming code - Wikipedia, the free encyclopedia Hamming code Hamming code From Wikipedia, the free encyclopedia In telecommunication, a Hamming code is a linear error-correcting code named after its inventor, Richard Hamming. Hamming codes can detect and correct single-bit errors, and can detect (but not correct) double-bit errors. In contrast, the simple parity code cannot detect errors where two bits are transposed, nor can it correct the errors it can find. Contents [hide] • 1 History • 2 Codes predating Hamming ♦ 2.1 Parity ♦ 2.2 Two-out-of-five code ♦ 2.3 Repetition • 3 Hamming codes • 4 Example using the (11,7) Hamming code • 5 Hamming code (7,4) ♦ 5.1 Hamming matrices ♦ 5.2 Channel coding ♦ 5.3 Parity check ♦ 5.4 Error correction • 6 Hamming codes with additional parity • 7 See also • 8 References • 9 External links History Hamming worked at Bell Labs in the 1940s on the Bell Model V computer, an electromechanical relay-based machine with cycle times in seconds. Input was fed in on punch cards, which would invariably have read errors. During weekdays, special code would find errors and flash lights so the operators could correct the problem. During after-hours periods and on weekends, when there were no operators, the machine simply moved on to the next job. Hamming worked on weekends, and grew increasingly frustrated with having to restart his programs from scratch due to the unreliability of the card reader. Over the next few years he worked on the problem of error-correction, developing an increasingly powerful array of algorithms. -
Convolutional Codes in Vss
CONVOLUTIONAL CODES IN VSS . .. By: Dr. Kurt R. Matis Director of Systems Research SIMULATION OF CONVOLUTIONAL CODES IN VSS This document describes the modeling and simulation of short constraint- length convolutional codes used in conjunction with Viterbi decoding in the Visual System Simulator (VSS). After a brief review of the history and application of convolutional codes, a detailed description of VSS models for encoding/decoding of these codes is presented. Step-by-step examples illustrate how to construct simulations and analyze results. Convolutional Code Basics This note begins with some background information on the use of convolutional codes. The development of convolutional codes is discussed along with a history of important applications. This information is meant to provide a perspective on the selection of the convolutional code models that are provided in VSS. Transmission efficiency and reliability can be improved by encoding information digits in a way that creates an interdependence between symbols which are transmitted over a channel. At the receiving end, the interdependence can be exploited to detect or even correct transmission errors, provided erroneous symbols are not received too frequently. Such coding is called error-control coding and is shown in the configuration of Figure 1. Received Source Encoded Symbols Decoded Symbols Symbols ˆ Symbols {bk } {ai} Channel {bk} Transmitter s(t) Channel r(t) Receiver Channel {âi} Encoder Decoder {rk} Figure 1. System Employing Error-Control Coding Visual System Simulator 1 CONVOLUTIONAL CODES IN VSS Simulation of Convolutional Codes in VSS Encoders for error control are usually called channel encoders to differentiate them from various encoders used for other purposes within digital communication systems. -
Glossary of Complexity Classes
App endix A Glossary of Complexity Classes Summary This glossary includes selfcontained denitions of most complexity classes mentioned in the b o ok Needless to say the glossary oers a very minimal discussion of these classes and the reader is re ferred to the main text for further discussion The items are organized by topics rather than by alphab etic order Sp ecically the glossary is partitioned into two parts dealing separately with complexity classes that are dened in terms of algorithms and their resources ie time and space complexity of Turing machines and complexity classes de ned in terms of nonuniform circuits and referring to their size and depth The algorithmic classes include timecomplexity based classes such as P NP coNP BPP RP coRP PH E EXP and NEXP and the space complexity classes L NL RL and P S P AC E The non k uniform classes include the circuit classes P p oly as well as NC and k AC Denitions and basic results regarding many other complexity classes are available at the constantly evolving Complexity Zoo A Preliminaries Complexity classes are sets of computational problems where each class contains problems that can b e solved with sp ecic computational resources To dene a complexity class one sp ecies a mo del of computation a complexity measure like time or space which is always measured as a function of the input length and a b ound on the complexity of problems in the class We follow the tradition of fo cusing on decision problems but refer to these problems using the terminology of promise problems -
Group, Graphs, Algorithms: the Graph Isomorphism Problem
Proc. Int. Cong. of Math. – 2018 Rio de Janeiro, Vol. 3 (3303–3320) GROUP, GRAPHS, ALGORITHMS: THE GRAPH ISOMORPHISM PROBLEM László Babai Abstract Graph Isomorphism (GI) is one of a small number of natural algorithmic problems with unsettled complexity status in the P / NP theory: not expected to be NP-complete, yet not known to be solvable in polynomial time. Arguably, the GI problem boils down to filling the gap between symmetry and regularity, the former being defined in terms of automorphisms, the latter in terms of equations satisfied by numerical parameters. Recent progress on the complexity of GI relies on a combination of the asymptotic theory of permutation groups and asymptotic properties of highly regular combinato- rial structures called coherent configurations. Group theory provides the tools to infer either global symmetry or global irregularity from local information, eliminating the symmetry/regularity gap in the relevant scenario; the resulting global structure is the subject of combinatorial analysis. These structural studies are melded in a divide- and-conquer algorithmic framework pioneered in the GI context by Eugene M. Luks (1980). 1 Introduction We shall consider finite structures only; so the terms “graph” and “group” will refer to finite graphs and groups, respectively. 1.1 Graphs, isomorphism, NP-intermediate status. A graph is a set (the set of ver- tices) endowed with an irreflexive, symmetric binary relation called adjacency. Isomor- phisms are adjacency-preseving bijections between the sets of vertices. The Graph Iso- morphism (GI) problem asks to determine whether two given graphs are isomorphic. It is known that graphs are universal among explicit finite structures in the sense that the isomorphism problem for explicit structures can be reduced in polynomial time to GI (in the sense of Karp-reductions1) Hedrlı́n and Pultr [1966] and Miller [1979]. -
Short Low-Rate Non-Binary Turbo Codes Gianluigi Liva, Bal´Azs Matuz, Enrico Paolini, Marco Chiani
Short Low-Rate Non-Binary Turbo Codes Gianluigi Liva, Bal´azs Matuz, Enrico Paolini, Marco Chiani Abstract—A serial concatenation of an outer non-binary turbo decoding thresholds lie within 0.5 dB from the Shannon code with different inner binary codes is introduced and an- limit in the coherent case, for a wide range of coding rates 1 alyzed. The turbo code is based on memory- time-variant (1/3 ≤ R ≤ 1/96). Remarkably, a similar result is achieved recursive convolutional codes over high order fields. The resulting codes possess low rates and capacity-approaching performance, in the noncoherent detection framework as well. thus representing an appealing solution for spread spectrum We then focus on the specific case where the inner code is communications. The performance of the scheme is investigated either an order-q Hadamard code or a first order length-q Reed- on the additive white Gaussian noise channel with coherent and Muller (RM) code, due to their simple fast Hadamard trans- noncoherent detection via density evolution analysis. The pro- form (FHT)-based decoding algorithms. The proposed scheme posed codes compare favorably w.r.t. other low rate constructions in terms of complexity/performance trade-off. Low error floors can be thus seen either as (i) a serial concatenation of an outer and performances close to the sphere packing bound are achieved Fq-based turbo code with an inner Hadamard/RM code with down to small block sizes (k = 192 information bits). antipodal signalling, or (ii) as a coded modulation Fq-based turbo/LDPC code with q-ary (bi-) orthogonal modulation.