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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 -
Source Coding: Part I of Fundamentals of Source and Video Coding
Foundations and Trends R in sample Vol. 1, No 1 (2011) 1–217 c 2011 Thomas Wiegand and Heiko Schwarz DOI: xxxxxx Source Coding: Part I of Fundamentals of Source and Video Coding Thomas Wiegand1 and Heiko Schwarz2 1 Berlin Institute of Technology and Fraunhofer Institute for Telecommunica- tions — Heinrich Hertz Institute, Germany, [email protected] 2 Fraunhofer Institute for Telecommunications — Heinrich Hertz Institute, Germany, [email protected] Abstract Digital media technologies have become an integral part of the way we create, communicate, and consume information. At the core of these technologies are source coding methods that are described in this text. Based on the fundamentals of information and rate distortion theory, the most relevant techniques used in source coding algorithms are de- scribed: entropy coding, quantization as well as predictive and trans- form coding. The emphasis is put onto algorithms that are also used in video coding, which will be described in the other text of this two-part monograph. To our families Contents 1 Introduction 1 1.1 The Communication Problem 3 1.2 Scope and Overview of the Text 4 1.3 The Source Coding Principle 5 2 Random Processes 7 2.1 Probability 8 2.2 Random Variables 9 2.2.1 Continuous Random Variables 10 2.2.2 Discrete Random Variables 11 2.2.3 Expectation 13 2.3 Random Processes 14 2.3.1 Markov Processes 16 2.3.2 Gaussian Processes 18 2.3.3 Gauss-Markov Processes 18 2.4 Summary of Random Processes 19 i ii Contents 3 Lossless Source Coding 20 3.1 Classification -
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. -
Tm Synchronization and Channel Coding
Recommendation for Space Data System Standards TM SYNCHRONIZATION AND CHANNEL CODING RECOMMENDED STANDARD CCSDS 131.0-B-3 BLUE BOOK September 2017 Recommendation for Space Data System Standards TM SYNCHRONIZATION AND CHANNEL CODING RECOMMENDED STANDARD CCSDS 131.0-B-3 BLUE BOOK September 2017 CCSDS RECOMMENDED STANDARD FOR TM SYNCHRONIZATION AND CHANNEL CODING AUTHORITY Issue: Recommended Standard, Issue 3 Date: September 2017 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 represents the consensus technical agreement of the participating CCSDS Member Agencies. The procedure for review and authorization of CCSDS documents is detailed in Organization and Processes for the Consultative Committee for Space Data Systems (CCSDS A02.1-Y-4), and the record of Agency participation in the authorization of this document can be obtained from the CCSDS Secretariat at the e-mail address below. This document is published and maintained by: CCSDS Secretariat National Aeronautics and Space Administration Washington, DC, USA E-mail: [email protected] CCSDS 131.0-B-3 Page i September 2017 CCSDS RECOMMENDED STANDARD FOR TM SYNCHRONIZATION AND CHANNEL CODING STATEMENT OF INTENT The Consultative Committee for Space Data Systems (CCSDS) is an organization officially established by the management of its members. The Committee meets periodically to address data systems problems that are common to all participants, and to formulate sound technical solutions to these problems. Inasmuch as participation in the CCSDS is completely voluntary, the results of Committee actions are termed Recommended Standards and are not considered binding on any Agency. -
Configurable and Scalable Turbo Decoder for 4G Wireless Decoder
Configurable and Scalable Turbo Decoder for 4G Wireless Receivers Yang Sun Rice University, USA Joseph R. Cavallaro Rice University, USA Yuming Zhu Texas Instruments, USA Manish Goel Texas Instruments, USA ABSTRACT The increasing requirements of high data rates and quality of service (QoS) in fourth-generation (4G) wireless communication require the implementation of practical capacity approaching codes. In this chapter, the application of Turbo coding schemes that have recently been adopted in the IEEE 802.16e WiMax standard and 3GPP Long Term Evolution (LTE) standard are reviewed. In order to process several 4G wireless standards with a common hardware module, a reconfigurable and scalable Turbo decoder architecture is presented. A parallel Turbo decoding scheme with scalable parallelism tailored to the target throughput is applied to support high data rates in 4G applications. High-level decoding parallelism is achieved by employing contention-free interleavers. A multi-banked memory structure and routing network among memories and MAP decoders are designed to operate at full speed with parallel interleavers. A new on-line address generation technique is introduced to support multiple Turbo interleaving patterns, which avoids the interleaver address memory that is typically necessary in the traditional designs. Design trade-offs in terms of area and power efficiency are analyzed for different parallelism and clock frequency goals. KEYWORDS Error Correction Codes, Turbo Codes, MAP Algorithm, BCJR Algorithm, Turbo Decoder, Interleaver, Wireless PHY, 4G Receiver, VLSI Architecture INTRODUCTION The approaching fourth-generation (4G) wireless systems are promising to support very high data rates from 100 Mbps to 1 Gbps. This consequently leads to orders of complexity increases in a 4G wireless receiver. -
BER Comparison Between Convolutional, Turbo, LDPC, and Polar Codes
BER Comparison Between Convolutional, Turbo, LDPC, and Polar Codes Bashar Tahir∗, Stefan Schwarzy, and Markus Ruppz Institute of Telecommunications Technische Universitat¨ (TU) Wien Vienna, Austria Email: f∗bashar.tahir, ystefan.schwarz, [email protected] Abstract—Channel coding is a fundamental building block in A major breakthrough happened in 1993 when turbo codes any communications system. High performance codes, with low were introduced [5]. They represent a class of codes that complexity encoding and decoding are a must-have for future can perform very close to the capacity limit. In its common wireless systems, with requirements ranging from the operation in highly reliable scenarios, utilizing short information messages form, turbo encoding is done using two recursive convolutional and low code rates, to high throughput scenarios, working with encoders. The input stream is passed to the first encoder, and a long messages, and high code rates. permuted version is passed to the second one. At the receiving We investigate in this paper the performance of Convolutional, side, two decoders are used, each one decodes the streams Turbo, Low-Density Parity-Check (LDPC), and Polar codes, in of the corresponding encoder. By exchanging probabilistic terms of the Bit-Error-Ratio (BER) for different information block lengths and code rates, spanning the multiple scenarios information, the two decoders can iteratively help each other of reliability and high throughput. We further investigate their in a manner similar to a turbo engine. convergence behavior with respect to the number of iterations Another class of capacity-approaching codes, are the LDPC (turbo and LDPC), and list size (polar), as well as how their per- codes. -
Services Catalog
Deep Space Network Deep Space Network Services Catalog Document Owner: Approved by: Signature Provided 02/03/15 Signature Provided 01/22/15 Jeff Berner Date Alaudin M. Bhanji Date DSN Project Chief Engineer DSN Project Manager Prepared by: Approved by: Signature Provided 01/16/15 Signature Provided 01/16/15 Timothy Pham Date Charles Scott Date DSN Chief Systems Engineer DSN Commitment Office Manager Signature Provided 02/20/15 DSN Document Release Date DSN No. 820-100, Rev. F Issue Date: February 24, 2015 JPL D-19002 Jet Propulsion Laboratory California Institute of Technology Users must ensure that they are using the current version in PDMS: https://pdms.jpl.nasa.gov Copyright 2015 California Institute of Technology. U.S. Government sponsorship acknowledged 820-100, Rev. F Review Acknowledgment By signing below, the signatories acknowledge that they have reviewed this document and provided comments, if any, to the signatories on the Cover Page. Signature Provided 02/19/15 Signature Provided 02/17/15 Ana Guerrero Date Pat Beyer Date DSN TTC &Data Delivery Office Manager DSN Service Management Office Manager Signature Provided 02/17/15 Signature Provided 02/23/15 Peter Hames Date James A. O’dea Date DSN Antenna Front-End, Facilities and TTC System Engineer Infrastructure Office Manager Signature Provided 02/16/15 Signature Provided 02/17/15 Tim Cornish Date Alina Bedrossian Date Uplink System Engineer Science System Engineer Signature Provided 02/17/15 Signature Provided 02/17/15 Leslie Deutsch Date Cathy Cagle Date Architecture Strategic Planning and Mission Support Definition and System Engineering Manager Commitments Manager ii 820-100, Rev. -
Dynamic Code Selection Method for Content Transfer in Deep Space Network
University of Mississippi eGrove Electronic Theses and Dissertations Graduate School 2019 Dynamic Code Selection Method for Content Transfer in Deep Space Network Rojina Adhikary University of Mississippi Follow this and additional works at: https://egrove.olemiss.edu/etd Part of the Electrical and Computer Engineering Commons Recommended Citation Adhikary, Rojina, "Dynamic Code Selection Method for Content Transfer in Deep Space Network" (2019). Electronic Theses and Dissertations. 1528. https://egrove.olemiss.edu/etd/1528 This Dissertation is brought to you for free and open access by the Graduate School at eGrove. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of eGrove. For more information, please contact [email protected]. DYNAMIC CODE SELECTION METHOD FOR CONTENT TRANSFER IN DEEP SPACE NETWORK A Dissertation presented in partial fulfillment of requirements for the degree of Doctor of Philosophy in the Department of Electrical Engineering The University of Mississippi by Rojina Adhikary May 2019 Copyright Rojina Adhikary 2019 ALL RIGHTS RESERVED ABSTRACT Space communications feature large round-trip time delays (for example, be- tween 6.5 and 44 minutes for Mars to Earth and return, depending on the actual distance between the two planets) and highly variable data error rates, for example, bit error rate (BER) of 10−5 is very common and even higher BERs on the order of 10−1 is observed in the deep-space environment. We develop a new content transfer protocol based on RaptorQ codes and turbo codes together with a real-time channel prediction model to maximize file transfer from space vehicles to the Earth stations. -
CCSDS Glossary Export 8Nov11
Space Assigned Number Authority(SANA) Registry: CCSDS Glossary 11/8/11 1:18 PM SPACE ASSIGNED NUMBER AUTHORITY(SANA) REGISTRY CCSDS GLOSSARY XML version of this registry Created 2010-11-03 Registration Policy TBD Review authority Signature md5 Status Candidate Keyword Definition Synonym Related Status Reference (N)-association A cooperative relationship among (N)-entity-invocations. [A30X0G3] (N)-entity An active element within an (N)-subsystem embodying a set of [A30X0G3] capabilities defined for the (N)-layer that corresponds to a specific (N)- entity-type (without any extra capabilities being used). (N)-entity-invocation A specific utilization of part or all of the capabilities of a given (N)-entity [A30X0G3] (without any extra capabilities being used) []. (N)-entity-type A description of a class of (N)-entities in terms of a set of capabilities [A30X0G3] defined for the (N)-layer []. (N)-function A part of the activity of (N)-entities. [A30X0G3] (N)-layer A subdivision of the OSI architecture, constituted by subsystems of the [A30X0G3] http://sanaregistry.org/r/glossary/glossary.html Page 1 of 193 Space Assigned Number Authority(SANA) Registry: CCSDS Glossary 11/8/11 1:18 PM (N)-layer A subdivision of the OSI architecture, constituted by subsystems of the [A30X0G3] same rank (N) []. (N)-layer management Functions related to the management of the (N)-layer partly performed [A30X0G3] in the (N)-layer itself according to the (N)-protocol of the layer (activities such as activation and error control) and partly performed as a subset of systems management. (N)-layer operation The monitoring and control of a single instance of communication. -
Dynamic Data Distribution
An overview of Compression Compression becomes necessary in multimedia because it requires large amounts of storage space and bandwidth CpSc 863: Multimedia Systems Types of Compression and Applications Lossless compression = data is not altered or lost in the process Lossy = some info is lost but can be reasonably Data Compression reproduced using the data. James Wang 2 Binary Image Compression Binary Image Compression RLE (Run Length Encoding) Disadvantage of RLE scheme: Also called Packed Bits encoding When groups of adjacent pixels change rapidly, E.g. aaaaaaaaaaaaaaaaaaaa111110000000 the run length will be shorter. It could take more bits for the code to represent the run length that Can be coded as: the uncompressed data negative Byte1 Byte 2 Byte3 Byte4 Byte5 Byte6 compression. 20 a 05 1 07 0 It is a generalization of zero suppression, which This is a one dimensional scheme. Some assumes that just one symbol appears schemes will also use a flag to separate the data particularly often in sequences. bytes http://astronomy.swin.edu.au/~pbourke/dataformats/rle/ http://datacompression.info/RLE.shtml 3 http://www.data-compression.info/Algorithms/RLE/ 4 Basics of Information Theory According to Shannon, the entropy of an information source S is defined as: 1 H(S) pi log2 i pi Lossless Compression Algorithms where p is the probability that symbol S in S will occur. (Entropy Encoding) i i 1 log 2 indicates the amount of information Adapted from: pi http://www.cs.cf.ac.uk/Dave/Multimedia/node207.html contained in Si, i.e., the number of bits needed to code Si.