Theory and Modelling of Single-Electronic Systems
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Fountain Codes
6.972 PRINCIPLES OF DIGITAL COMMUNICATION II Fountain Codes Gauri Joshi, Joong Bum Rhim, John Sun, Da Wang December 8, 2010 Contents 1 Digital Fountain Ideal3 2 Preliminaries 4 2.1 Binary Erasure Channel...................................4 2.2 Edge Degree Distributions..................................4 3 Tornado Codes 5 3.1 Code Construction......................................5 3.2 Optimal Degree Sequences..................................7 3.3 Practical Considerations...................................9 3.3.1 Matlab Implementation...............................9 4 LT Codes 9 4.1 Code Construction...................................... 10 4.1.1 Encoding Process................................... 10 4.1.2 Decoding Process................................... 10 4.2 Degree Distribution...................................... 11 4.2.1 The Ideal Soliton Distribution............................ 12 4.2.2 The Robust Soliton Distribution.......................... 13 4.3 Performance Analysis.................................... 14 4.3.1 Degree Distribution................................. 15 4.3.2 Density Evolution.................................. 15 4.4 Implementation........................................ 17 5 Raptor Codes 17 5.1 Code Construction...................................... 18 5.2 Error Analysis......................................... 19 5.3 Practical Considerations................................... 19 5.3.1 Finite-length Raptor Codes............................. 20 5.3.2 Systematic Raptor Codes.............................. 20 5.3.3 -
Network Coding Protocols with Delay and Energy Constraints
Maricica Nistor Network Coding Protocols with Delay and Energy Constraints MAP-tele Doctoral Programme em Telecommunications Departamento de Engenharia Eletrot´ecnica e de Computadores Faculdade de Engenharia da Universidade do Porto May 2014 Maricica Nistor Network Coding Protocols with Delay and Energy Constraints Tese submetida `aFaculdade de Engenharia da Universidade do Porto para obten¸c˜ao do grau de Doutor em Telecomunica¸c˜oes Orientadores: Professor Doutor Jo˜ao Barros, Universidade do Porto, Portugal Professor Doutor Daniel E. Lucani, Aalborg University, Denmark May 2014 2 Acknowledgements I would like to thank my advisor, Professor Jo˜ao Barros, for his support, en- couragement, and guidance throughout my PhD. I would also like to thank my co-advisor, Professor Daniel E. Lucani, for his help and patience, and the way he really made my life more enjoyable during these last years. I thank my colleagues from IT Porto for the good moments we spent to- gether and all research collaborators I worked with during my PhD. A special thank goes to my family for the unconditional support, trust, and because they never stop to believe in me. I would also like to thank my friends for the encouragements, advises, and for being always close to me. Finally, I would like to thank Luis Monteiro, for his love, patience, and support. I dedicate this work to Luis Monteiro! This work was partially funded by the Funda¸c˜ao Calouste Gulbenkian un- der grant 103905 and by Funda¸c˜ao para a Ciˆencia e Tecnologia (Portuguese Foundation for Science and Technology) under grant SFRH-BD-61953-2009. -
Design of High Performance Low Latency Rateless Codes
DESIGN OF HIGH PERFORMANCE LOW LATENCY RATELESS CODES A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF ÇANKAYA UNIVERSITY BY NADHIR IBRAHIM ABDULKHALEQ IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF PHILOSOPHY OF DOCTORATE IN THE DEPARTMENT OF ELECTRONIC AND COMMUNICATION ENGINEERING SEPTEMBER 2017 ABSTRACT DESIGN OF HIGH PERFORMANCE LOW LATENCY RATELESS CODES ABDULKHALEQ, Nadhir Ibrahim Ph.D., Electronic and Communication Engineering Department Supervisor: Assoc. Prof. Dr. Orhan GAZI September 2017, 109 pages Luby Transform (LT) codes are one of the best rateless codes mainly designed for binary erasure channel. The characteristics of such codes perfectly performing when used with bulk data files, however a performance degradation has been observed when using them with short length messages. In this thesis, we present a new design for rateless codes, particularly an efficient LT codes using robust soliton distribution (RSD) as a degree generation method and tested in both binary erasure channel (BEC) and noisy channels like the additive white Gaussian noise (AWGN) channel. First, a new proposed decoding technique is defined as belief propagation-pattern recognition (BP-PR) is implemented to enhance the decoding ability of the conventional (BP) algorithm to overcome the problem of losing degree-one coded symbols which caused early decoding termination. The simulation results approve the improvement of the BP-PR when used with LT-RSD and outperforms the bit error rate (BER) records for the state of art techniques like memory-based robust Soliton distribution using conventional BP (LT-MBRSD-BP) or the Gaussian elimination assisted belief iv propagation (LT-RSD-BP-GE) and improve the records for the BER when used with MBRSD, ISD and optimal degree distribution (ODD), to form the new code called (LT-MBRSD-BP-PR),(LT-ISD-BP-PR) and (LT-ODD-BP-PR) respectively. -
An ECSO-Based Approach for Optimizing Degree Distribution of Short-Length LT Codes Peng Luo1*, Hui Fan1, Weiguang Shi2, Xiaoli Qi2, Yuhao Zhao1 and Xueqing Zhou1
Luo et al. EURASIP Journal on Wireless Communications and Networking (2019) 2019:76 https://doi.org/10.1186/s13638-019-1376-6 RESEARCH Open Access An ECSO-based approach for optimizing degree distribution of short-length LT codes Peng Luo1*, Hui Fan1, Weiguang Shi2, Xiaoli Qi2, Yuhao Zhao1 and Xueqing Zhou1 Abstract Degree distribution plays a great role in the performance of Luby transform codes. Typical degree distributions such as ideal soliton distribution and robust soliton distribution are easy to implement and widely used. Nevertheless, their adaptabilities are not always outstanding in various code lengths, especially in the case of short length. In this paper, our work is to optimize degree distributions for the short-length LT codes by using swarm intelligence algorithm, considering its conceptual simplicity, high efficiency, flexibility, and robustness. An optimization problem model based on sparse degree distributions is proposed in the first place. Then, a solution on the basis of an enhanced chicken swarm optimization algorithm, termed as ECSO, is designed for the problem. In ECSO, substitution of bottom individuals, revision of chicks’ update equation, and introduction of differential evolution are designed to enhance the ability of optimization. Simulation comparisons show that the proposed solution achieves much better performance than two other swarm intelligence-based solutions. Keywords: Fountain codes, LT code, Degree distribution, Differential evolution, Chicken swarm optimization 1 Introduction leading a wider scope of application, including deep space Power line communication (PLC) is regarded as a ser- communication [8], data distribution [9], wireless sensor ious candidate for the realization of smart grid networks networks [10], and cloud storage [11]. -
Incremental Redundancy, Fountain Codes and Advanced Topics
GRAPH CODES AND THEIR APPLICATIONS 2014 DRAFT Incremental Redundancy, Fountain Codes and Advanced Topics Suayb S. Arslan 141 Innovation Dr. Irvine, CA 92617 [email protected] “All men by nature desire knowledge." Aristotle Version 0.2 – July 2014 Revision History: • Jan. 2014, First version is released. • Feb. 2014, Uploaded on arXiv.org for public access. • Jul. 2014, Version 0.2 is released. – Table of Contents is added for easy reference. – Fixes few grammatical errors, changes the organization and extends the section of systematic fountain code constructions arXiv:1402.6016v2 [cs.IT] 14 Jul 2014 1 GRAPH CODES AND THEIR APPLICATIONS 2014 DRAFT CONTENTS 1 Abstract 3 2 Introduction 4 2.1 Notation ........................................ 4 2.2 ChannelModelandLinearCodes . ..... 5 2.3 IncrementalRedundancy . ..... 6 3 Linear Fountain Codes 8 3.1 High density Fountain codes: Maximum Likelihood Decoding .......... 11 3.2 Low Density Fountain codes: BP decoding and LT Codes . .......... 14 3.3 Asymptotical performance of LT codes under BP Decoding . ........... 20 3.4 Expected Ripple size and Degree Distribution Design . ............. 23 3.5 Systematic Constructions of LT codes . ......... 26 3.6 Generalizations/Extensions of LT codes: Unequal Error Protection . 27 4 Concatenated Linear Fountain Codes 29 4.1 Asymptotically good degree distributions . ............ 30 4.2 Precoding ....................................... 32 4.2.1 TornadoCodesasPrecodes . 33 4.2.2 LDPCCodesasPrecodes . 34 4.2.3 HammingCodesasPrecodes . 36 4.2.4 Repeat and Accumulate Codes as Precodes . ...... 38 4.3 Concatenated Fountain Code Failure Probability . ............ 38 4.4 Systematic Constructions of Concantenated Fountain Codes ........... 40 5 Advanced Topics 45 5.1 Connectionstotherandomgraphtheory . ........ 46 5.2 InactivationDecoding . ...... 49 5.3 Standardized Fountain Codes for Data Streaming .