NAND Error Correction Codes Introduction
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Recommendation Itu-R Bt.2016*
Recommendation ITU-R BT.2016 (04/2012) Error-correction, data framing, modulation and emission methods for terrestrial multimedia broadcasting for mobile reception using handheld receivers in VHF/UHF bands BT Series Broadcasting service (television) ii Rec. ITU-R BT.2016 Foreword The role of the Radiocommunication Sector is to ensure the rational, equitable, efficient and economical use of the radio-frequency spectrum by all radiocommunication services, including satellite services, and carry out studies without limit of frequency range on the basis of which Recommendations are adopted. The regulatory and policy functions of the Radiocommunication Sector are performed by World and Regional Radiocommunication Conferences and Radiocommunication Assemblies supported by Study Groups. Policy on Intellectual Property Right (IPR) ITU-R policy on IPR is described in the Common Patent Policy for ITU-T/ITU-R/ISO/IEC referenced in Annex 1 of Resolution ITU-R 1. Forms to be used for the submission of patent statements and licensing declarations by patent holders are available from http://www.itu.int/ITU-R/go/patents/en where the Guidelines for Implementation of the Common Patent Policy for ITU-T/ITU-R/ISO/IEC and the ITU-R patent information database can also be found. Series of ITU-R Recommendations (Also available online at http://www.itu.int/publ/R-REC/en) Series Title BO Satellite delivery BR Recording for production, archival and play-out; film for television BS Broadcasting service (sound) BT Broadcasting service (television) F Fixed service M Mobile, radiodetermination, amateur and related satellite services P Radiowave propagation RA Radio astronomy RS Remote sensing systems S Fixed-satellite service SA Space applications and meteorology SF Frequency sharing and coordination between fixed-satellite and fixed service systems SM Spectrum management SNG Satellite news gathering TF Time signals and frequency standards emissions V Vocabulary and related subjects Note: This ITU-R Recommendation was approved in English under the procedure detailed in Resolution ITU-R 1. -
ACRONYMS and ABBREVIATIONS BLUETOOTH SPECIFICATION Version 1.1 Page 914 of 1084
Appendix III ACRONYMS AND ABBREVIATIONS BLUETOOTH SPECIFICATION Version 1.1 page 914 of 1084 Appendix III - Acronyms 914 22 February 2001 BLUETOOTH SPECIFICATION Version 1.1 page 915 of 1084 Appendix III - Acronyms List of Acronyms and Abbreviations Acronym or Writing out in full Which means abbreviation ACK Acknowledge ACL link Asynchronous Connection-Less Provides a packet-switched con- link nection.(Master to any slave) ACO Authenticated Ciphering Offset AM_ADDR Active Member Address AR_ADDR Access Request Address ARQ Automatic Repeat reQuest B BB BaseBand BCH Bose, Chaudhuri & Hocquenghem Type of code The persons who discovered these codes in 1959 (H) and 1960 (B&C) BD_ADDR Bluetooth Device Address BER Bit Error Rate BT Bandwidth Time BT Bluetooth C CAC Channel Access Code CC Call Control CL Connectionless CODEC COder DECoder COF Ciphering Offset CRC Cyclic Redundancy Check CVSD Continuous Variable Slope Delta Modulation D DAC Device Access Code DCE Data Communication Equipment 22 February 2001 915 BLUETOOTH SPECIFICATION Version 1.1 page 916 of 1084 Appendix III - Acronyms Acronym or Writing out in full Which means abbreviation DCE Data Circuit-Terminating Equip- In serial communications, DCE ment refers to a device between the communication endpoints whose sole task is to facilitate the commu- nications process; typically a modem DCI Default Check Initialization DH Data-High Rate Data packet type for high rate data DIAC Dedicated Inquiry Access Code DM Data - Medium Rate Data packet type for medium rate data DTE Data Terminal Equipment In serial communications, DTE refers to a device at the endpoint of the communications path; typi- cally a computer or terminal. -
Implementation and Evaluation of Polar Codes in 5G
Implementation and evaluation of Polar Codes in 5G Implementation och evaluering av Polar Codes för 5G Tobias Rosenqvist Joël Sloof Faculty of Health, Science and Technology Computer Science 15 hp Supervisor: Stefan Alfredsson Examiner: Kerstin Andersson Date: 2019-06-12 Serial number: N/A Implementation and evaluation of Polar Codes in 5G Tobias Rosenqvist, Joël Sloof © 2019 The author(s) and Karlstad University This report is submitted in partial fulfillment of the requirements for the Bachelor’s degree in Computer Science. All material in this report which is not our own work has been identified and no material is included for which a degree has previously been conferred. Tobias Rosenqvist Joël Sloof Approved, Date of defense Advisor: Stefan Alfredsson Examiner: Kerstin Andersson iii Abstract In today’s society the ability to communicate with one another has grown, were a lot of focus is aimed towards speed in the telecommunication industry. For transmissions to become even faster, there are many ways to enhance transmission speeds of which error correction is one. Padding messages such that they are protected from noise, while using as few bits as possible and ensuring safe transmit is handled by error correction codes. Short codes with low complexity is a solution to faster transmission speeds. An error correction code which has gained a lot of attention since its first appearance in 2009 is Polar Codes. Polar Codes was chosen as the 3GPP standard for 5G control channel. The goal of the thesis is to develop and implement Polar Codes and rate matching according to the 3GPP standard 38.212. -
Applicability of the Julia Programming Language to Forward Error-Correction Coding in Digital Communications Systems
Applicability of the Julia Programming Language to Forward Error-Correction Coding in Digital Communications Systems A project presented to Department of Computer and Information Sciences State University of New York Polytechnic Institute At Utica/Rome Utica, New York In partial fulfillment Of the requirements of the Master of Science Degree by Ryan Quinn May 2018 Declaration I declare that this project is my own work and has not been submitted in any form for another degree of diploma at any university of other institute of tertiary education. Information derived from published and unpublished work of others has been acknowledged in the text and a list of references given. _ Ryan Quinn 2 | P a g e SUNY POLYTECHNIC INSTITUTE DEPARTMENT OF COMPUTER AND INFORMATION SCIENCES Approved and recommended for acceptance as a project in partial fulfillment of the requirements for the degree of Master of Science in computer and information science _ DATE _ Dr. Bruno R. Andriamanalimanana Advisor _ Dr. Saumendra Sengupta _ Dr. Scott Spetka 3 | P a g e 1 ABSTRACT Traditionally SDR has been implemented in C and C++ for execution speed and processor efficiency. Interpreted and high-level languages were considered too slow to handle the challenges of digital signal processing (DSP). The Julia programming language is a new language developed for scientific and mathematical purposes that is supposed to write like Python or MATLAB and execute like C or FORTRAN. Given the touted strengths of the Julia language, it bore investigating as to whether it was suitable for DSP. This project specifically addresses the applicability of Julia to forward error correction (FEC), a highly mathematical topic to which Julia should be well suited. -
16.548 Notes 15: Concatenated Codes, Turbo Codes and Iterative Processing Outline
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 " Theory of Concatenated codes ! Turbo codes " Encoding " Decoding " Performance analysis " Applications ! Other applications of iterative processing " Joint equalization/FEC " Joint multiuser detection/FEC Shannon Capacity Theorem Capacity as a function of Code rate Motivation: Performance of Turbo Codes. Theoretical Limit! ! Comparison: " Rate 1/2 Codes. " K=5 turbo code. " K=14 convolutional code. ! Plot is from: L. Perez, “Turbo Codes”, chapter 8 of Trellis Coding by C. Schlegel. IEEE Press, 1997. Gain of almost 2 dB! Power Efficiency of Existing Standards Error Correction Coding ! Channel coding adds structured redundancy to a transmission. mxChannel Encoder " The input message m is composed of K symbols. " The output code word x is composed of N symbols. " Since N > K there is redundancy in the output. " The code rate is r = K/N. ! Coding can be used to: " Detect errors: ARQ " Correct errors: FEC Traditional Coding Techniques The Turbo-Principle/Iterative Decoding ! Turbo codes get their name because the decoder uses feedback, like a turbo engine. Theory of Iterative Coding Theory of Iterative Coding(2) Theory of Iterative Coding(3) Theory of Iterative Coding (4) Log Likelihood Algebra New Operator Modulo 2 Addition Example: Product Code Iterative Product Decoding RSC vs NSC Recursive/Systematic Convolutional Coding Concatenated Coding -
10. on Extending Bch Codes
University of Plymouth PEARL https://pearl.plymouth.ac.uk 04 University of Plymouth Research Theses 01 Research Theses Main Collection 2011 Algebraic Codes For Error Correction In Digital Communication Systems Jibril, Mubarak http://hdl.handle.net/10026.1/1188 University of Plymouth All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. Copyright Statement This copy of the thesis has been supplied on the condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and information derived from it may be published without the author’s prior consent. Copyright © Mubarak Jibril, December 2011 Algebraic Codes For Error Correction In Digital Communication Systems by Mubarak Jibril A thesis submitted to the University of Plymouth in partial fulfilment for the degree of Doctor of Philosophy School of Computing and Mathematics Faculty of Technology Under the supervision of Dr. M. Z. Ahmed, Prof. M. Tomlinson and Dr. C. Tjhai December 2011 ABSTRACT Algebraic Codes For Error Correction In Digital Communication Systems Mubarak Jibril C. Shannon presented theoretical conditions under which communication was pos- sible error-free in the presence of noise. Subsequently the notion of using error correcting codes to mitigate the effects of noise in digital transmission was intro- duced by R. -
Decoding Beyond the Designed Distance for Certain Algebraic Codes
INFORMATION AND CONTROL 35, 209--228 (1977) Decoding beyond the Designed Distance for Certain Algebraic Codes DAVID MANDELBAUM P.O. Box 645, Eatontown, New Jersey 07724 It is shown how decoding beyond the designed distance can be accomplished for a certain decoding algorithm. This method can be used for subcodes of generalized Goppa codes and generalized Reed-Solomon codes. 1. INTRODUCTION A decoding algorithm for generalized Goppa codes is presented here. This method directly uses the Euclidean algorithm, it is shown how subcodes of these generalized codes can be decoded for errors beyond the designed distance. It is proved that all coset leaders can be decoded by this method. Decoding of errors beyond the BCH bound may involve solution of nonlinear equations. The method used is similar to the use of continued fractions for decoding within the BCH bound (Mandelbaum, 1976). The Euclidean algorithm is also used by Sugiyama et aL (1975) in a different manner for decoding Goppa codes within the BCH bound. Section 2 defines the generalized codes and shows the relationship with the Mattson-Solomon polynomial. Section 3 defines the decoding algorithm and gives, as a detailed example, the decoding of the (15, 5) BCH code for a quadruple error. Application to the Golay code is pointed out. This method can also be used for decoding the Generalized Reed-Solomon codes constructed by Delsarte (1975). Section 4 presents a method of obtaining the original information and syndrome in an iterated manner, which results in reduced computation for codewords with no errors. Other methods for decoding beyond the BCH bound for BCH codes are given by Berlekamp (1968) and Harmann (1972). -
Chapter 6 Modeling and Simulations of Reed-Solomon Encoder and Decoder
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE The Implementation of a Reed Solomon Code Encoder /Decoder A graduate project submitted in partial fulfillment of the requirements For the degree of Master of Science in Electrical Engineering By Qiang Zhang May 2014 Copyright by Qiang Zhang 2014 ii The graduate project of Qiang Zhang is approved: Dr. Ali Amini Date Dr. Sharlene Katz Date Dr. Nagi El Naga, Chair Date California State University, Northridge iii Acknowledgements I would like to thank my project professor and committee: Dr. Nagi El Naga, Dr. Ali Amini, and Dr. Sharlene Katz for their time and help. I would also like to thank my parents for their love and care. I could not have finished this project without them, because they give me the huge encouragement during last two years. iv Dedication To my parents v Table of Contents Copyright Page.................................................................................................................... ii Signature Page ................................................................................................................... iii Acknowledgement ............................................................................................................. iv Dedication ............................................................................................................................v List of Figures .................................................................................................................. viii Abstract ................................................................................................................................x -
Improving Wireless Link Throughput Via Interleaved FEC
Improving Wireless Link Throughput via Interleaved FEC Ling-Jyh Chen, Tony Sun, M. Y. Sanadidi, Mario Gerla UCLA Computer Science Department, Los Angeles, CA 90095, USA {cclljj, tonysun, medy, gerla}@cs.ucla.edu Abstract Traditionally, one of three techniques is used for er- ror-control: 1) Retransmission which uses acknowl- Wireless communication is inherently vulnerable to edgements, and time-outs; 2) Redundancy, and 3) Inter- errors from the dynamic wireless environment. Link leaving [6] [13]. Retransmitting lost packets is an obvi- layer packets discarded due to these errors impose a ous means by which error may be repaired, and it is serious limitation on the maximum achievable through- typically performed at either the link layer with Auto- put in the wireless channel. To enhance the overall matic Repeat reQuest (ARQ) or at the end-to-end layers throughput of wireless communication, it is necessary to with protocols like TCP. When data is exchanged have a link layer transmission scheme that is robust to through a relatively error-free communication medium, the errors intrinsic to the wireless channel. To this end, retransmission is clearly a good tactic. However, re- we present Interleaved-Forward Error Correction (I- transmission is not the best strategy in an error-prone FEC), a clever link layer coding scheme that protects communication channel, where high latency and band- link layer data against random and busty errors. We width overhead are believed to make retransmission a examine the level of data protection provided by I-FEC less than ideal error controlling technique. against other popular schemes. We also simulate I-FEC Redundancy is the means by which repair data is in Bluetooth, and compare the TCP throughput result added along with the original data, such that corrupted with Bluetooth’s integrated FEC coding feature. -
Chapter 9: BCH, Reed-Solomon, and Related Codes
Draft of February 23, 2001 Chapter 9: BCH, Reed-Solomon, and Related Codes 9.1 Introduction. In Chapter 7 we gave one useful generalization of the (7, 4) Hamming code of the Introduction: the family of (2m − 1, 2m − m − 1) single error-correcting Hamming Codes. In Chapter 8 we gave a further generalization, to a class of codes capable of correcting a single burst of errors. In this Chapter, however, we will give a far more important and extensive generalization, the multiple-error correcting BCH and Reed-Solomon Codes. To motivate the general definition, recall that the parity-check matrix of a Hamming Code of length n =2m − 1 is given by (see Section 7.4) H =[v0 v1 ··· vn−1 ] , (9.1) m m where (v0, v1,...,vn−1) is some ordering of the 2 − 1 nonzero (column) vectors from Vm = GF (2) . The matrix H has dimensions m × n, which means that it takes m parity-check bits to correct one error. If we wish to correct two errors, it stands to reason that m more parity checks will be required. Thus we might guess that a matrix of the general form v0 v1 ··· vn−1 H2 = , w0 w1 ··· wn−1 where w0, w1,...,wn−1 ∈ Vm, will serve as the parity-check matrix for a two-error-correcting code of length n. Since however the vi’s are distinct, we may view the correspondence vi → wi as a function from Vm into itself, and write H2 as v0 v1 ··· vn−1 H2 = . (9.3) f(v0) f(v1) ··· f(vn−1) But how should the function f be chosen? According to the results of Section 7.3, H2 will define a n two-error-correcting code iff the syndromes of the 1 + n + 2 error pattern of weights 0, 1 and 2 are all distinct. -
Chapter 9: BCH, Reed-Solomon, and Related Codes
Draft of February 21, 1999 Chapter 9: BCH, Reed-Solomon, and Related Codes 9.1 Introduction. In Chapter 7 we gave one useful generalization of the (7, 4)Hamming code of the Introduction: the family of (2m − 1, 2m − m − 1)single error-correcting Hamming Codes. In Chapter 8 we gave a further generalization, to a class of codes capable of correcting a single burst of errors. In this Chapter, however, we will give a far more important and extensive generalization, the multiple-error correcting BCH and Reed-Solomon Codes. To motivate the general definition, recall that the parity-check matrix of a Hamming Code of length n =2m − 1 is given by (see Section 7.4) H =[v0 v1 ··· vn−1 ] , (9.1) m m where (v0, v1,...,vn−1)is some ordering of the 2 − 1 nonzero (column)vectors from Vm = GF (2) . The matrix H has dimensions m × n, which means that it takes m parity-check bits to correct one error. If we wish to correct two errors, it stands to reason that m more parity checks will be required. Thus we might guess that a matrix of the general form v0 v1 ··· vn−1 H2 = , w0 w1 ··· wn−1 where w0, w1,...,wn−1 ∈ Vm, will serve as the parity-check matrix for a two-error-correcting code of length n. Since however the vi’s are distinct, we may view the correspondence vi → wi as a function from Vm into itself, and write H2 as v0 v1 ··· vn−1 H2 = . (9.3) f(v0) f(v1) ··· f(vn−1) But how should the function f be chosen? According to the results of Section 7.3, H2 will define a n two-error-correcting code iff the syndromes of the 1 + n + 2 error pattern of weights 0, 1 and 2 are all distinct. -
LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
electronics Article LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems Chen-Hung Lin 1,2,* , Chen-Xuan Wang 2 and Cheng-Kai Lu 3 1 Biomedical Engineering Research Center, Yuan Ze University, Jungli 32003, Taiwan 2 Department of Electrical Engineering, Yuan Ze University, Jungli 32003, Taiwan; [email protected] 3 Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; [email protected] * Correspondence: [email protected] Abstract: This paper presents a dual-mode low-density parity-check (LDPC) decoding architec- ture that has excellent error-correcting capability and a high parallelism design for fifth-generation (5G) new-radio (NR) applications. We adopted a high parallelism design using a layered decoding schedule to meet the high throughput requirement of 5G NR systems. Although the increase in parallelism can efficiently enhance the throughput, the hardware implementation required to support high parallelism is a significant hardware burden. To efficiently reduce the hardware burden, we used a grouping search rather than a sorter, which was used in the minimum finder with decoding performance loss. Additionally, we proposed a compensation scheme to improve the decoding per- formance loss by revising the probabilistic second minimum of a grouping search. The post-layout implementation of the proposed dual-mode LDPC decoder is based on the Taiwan Semiconduc- Citation: Lin, C.-H.; Wang, C.-X.; Lu, tor Manufacturing Company (TSMC) 40 nm complementary metal-oxide-semiconductor (CMOS) C.-K. LDPC Decoder Design Using Compensation Scheme of Group technology, using a compensation scheme of grouping comparison for 5G communication systems Comparison for 5G Communication with a working frequency of 294.1 MHz.