Author: Kim Swan TanPublisher:
ISBN:
Category : Coding theory
Languages : en
Pages :
Book Description
Simulation of Serially Concatenated Convolutional Code with Iterative Decoding
Author: Kim Swan TanPublisher:
ISBN:
Category : Coding theory
Languages : en
Pages :
Book Description
Performance Comparisons for Serial Concatenated Block Convolutional Codes when Sequentially and Iteratively Decoded
Author: Dana Nazir DannanPublisher:
ISBN:
Category :
Languages : en
Pages : 178
Book Description
Serially Concatenated Codes, a concept building on classical concatenated codes and parallel concatenated codes known as "Turbo Codes", have been studied for ma ny years. One of the most important forms of serial concatenated codes that was first discovered by Forney [14], consists of a simple convolutional code as an inner c ode and a Reed_ Solomon code as an outer code, and the decoding for this coding scheme is done sequentially, using each decoder once. In this thesis, other powerful linear block codes such as Hamming and BCH codes were considered. Simulations were done for three models with and without an inte rleaver and for different block lengths. Comparative study of the three models w as done in order to find out how the block code may affect the performance. The simulation results showed that both Hamming and BCH codes give the same performa nce for the same values of Energy bit to Noise ratio (EbNo), but a Reed_ Solomon model gives a good performance. In the second part of the thesis, other ways of serial poncatenations were appli ed but here the used decoder will be changed from sequential decoder into iterat ive decoder. The new iterative decoder consists of an outer (SISO), and an inner block decode r that corresponds to the used block encoder, simulation results showed a very b ad performance comparing to the previous models. A new way of serial concatenati on was done, by connecting a block encoder serially to an (SCCC) in the encoder side, and for the decoder side, a block decoder was serially connected to the it erative decoder. Simulation results showed that the new way of concatenating gives a very good performance comparing to the first models for different block codes. Important comparisons were also done in order to see the effect of the block codes when they are modified to a S erial Concatenated Convolutional Codes (SCCCs). Results showed that both Hamming and BCH codes give approximately same performance that is obtained by the equiv alent (SCCC). But when using the Reed_ Solomon code, a very good performance was achieved comparing to all other models, even when compared to Serial Concatenat ed Convolutional Code (SCCC).
Iterative Decoding of Parallel and Serial Concatenated Codes
Author: Chuan Hsian PuPublisher: LAP Lambert Academic Publishing
ISBN: 9783838316581
Category : Coding theory
Languages : en
Pages : 148
Book Description
In the past few decades, many forward error correction (FEC) codes have been proposed to improve the performance of digital communication systems. The problem involving many conventional FEC techniques are that the complexity and cost of such coding systems usually increase dramatically with respect to the attainable coding gain. The author focused on the study of the concatenated convolutional codes and their iterative decoding methodologies. Parallel concatenated convolutional codes (PCCC) and serial convolutional concatenated codes (SCCC) are studied to explore their potentiality to approximate Shannon s channel capacity with feasible complexity. Simulation are performed on PCCC and SCCC to understand the performance of the mentioned coding schemes in various circumstances and enables reseaerchers, academicians to fully exploit the potential of such coding schemes for future 4G communication system requirements.
Performance of Serial Concatenated Convolutional Codes with MSK Over ISI Wireless Channels
Author: Le FengTrellis and Turbo Coding
Author: Christian B. SchlegelPublisher: John Wiley & Sons
ISBN: 111910632X
Category : Science
Languages : en
Pages : 518
Book Description
This new edition has been extensively revised to reflect the progress in error control coding over the past few years. Over 60% of the material has been completely reworked, and 30% of the material is original. Convolutional, turbo, and low density parity-check (LDPC) coding and polar codes in a unified framework Advanced research-related developments such as spatial coupling A focus on algorithmic and implementation aspects of error control coding
Fundamentals of Convolutional Coding
Author: Rolf JohannessonPublisher: John Wiley & Sons
ISBN: 0470276835
Category : Technology & Engineering
Languages : en
Pages : 686
Book Description
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes Distance properties of convolutional codes Includes a downloadable solutions manual
Sequential Iterative Decoding of Concatenated Recursive Systematic Convolutional Codes
Author: Ravi SivasankaranPublisher:
ISBN:
Category : Intelligent control systems
Languages : en
Pages : 318
Book Description
Introduction to Convolutional Codes with Applications
Author: Ajay DholakiaPublisher: Springer Science & Business Media
ISBN: 1461527120
Category : Technology & Engineering
Languages : en
Pages : 256
Book Description
Introduction to Convolutional Codes with Applications is an introduction to the basic concepts of convolutional codes, their structure and classification, various error correction and decoding techniques for convolutionally encoded data, and some of the most common applications. The definition and representations, distance properties, and important classes of convolutional codes are also discussed in detail. The book provides the first comprehensive description of table-driven correction and decoding of convolutionally encoded data. Complete examples of Viterbi, sequential, and majority-logic decoding technique are also included, allowing a quick comparison among the different decoding approaches. Introduction to Convolutional Codes with Applications summarizes the research of the last two decades on applications of convolutional codes in hybrid ARQ protocols. A new classification allows a natural way of studying the underlying concepts of hybrid schemes and accommodates all of the new research. A novel application of fast decodable invertible convolutional codes for lost packet recovery in high speed networks is described. This opens the door for using convolutional coding for error recovery in high speed networks. Practicing communications, electronics, and networking engineers who want to get a better grasp of the underlying concepts of convolutional coding and its applications will greatly benefit by the simple and concise style of explanation. An up-to-date bibliography of over 300 papers is included. Also suitable for use as a textbook or a reference text in an advanced course on coding theory with emphasis on convolutional codes.
Low Complexity Capacity-approaching Codes for Data Transmission
Author: Christopher J. NelsonPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
This thesis analyzes the design of low complexity capacity approaching codes suitable for data transmission. The research documented in this thesis describes new and novel design methods for three well-known error control coding techniques, Turbo codes, LDPC block codes and LDPC convolutional codes, which are suitable for implementation in a number of modem digital communication systems. Firstly, we present Partial Unit Memory (PUM) based Turbo codes. A variant of Turbo codes which encompasses the advantages of both block and convolutional codes. The design methods of PUM Turbo codes are presented and Bit Error Rate (BER) simulations and Extrinsic Information Transfer (EXIT) chart analysis illustrates their performance. Partial Unit Memory codes are a class of low complexity, non-binary convolutional codes and have been shown to outperform equivalent convolutional codes. We present the EXIT charts of parallel concatenated PUM codes and PUM Woven Turbo Codes and analyse them to assess their performance compared with standard Turbo code designs. Resulting Extrinsic Information Transfer charts indicate that the proposed PUM-based codes have higher mutual information during iterative decoding than the equivalent Recursive, Systematic, Convolutional Turbo codes (RSC- TC) for the same Eb/No, i.e. the output of the decoders provides a better approximation of the decoded bits. The EXIT chart analysis is supported by BER plots, which confirms the behaviour predicted by the EXIT charts. We show that the concatenated PUM codes outperform the well-known turbo codes in the waterfall region, with comparable performance in the error floor region. In the second section we present Low Density Generator Matrix codes; a variant of LDPC codes that have low complexity encoding and decoding techniques. We present results of three construction methods and describe how LDGM codes can be modified to improve the error-floor region. We describe the design of random, structured and semi-random, semi- structured codes and how, by replacing the identity matrix with a staircase matrix, LDGM codes can show significant improvements in the error-floor region. Furthermore, we analyse the performance of serially concatenated LDGM codes and how they can benefit when we use the modified LDGM codes in either the outer code or the inner code. The results indicate that concatenated LDGM codes that incorporate LDGM staircase codes in the inner code will show improvements in error-floor performance while maintaining near capacity limit performances. While in the case of LDGM staircase codes being used as the outer codes no significant improvements in waterfall or error-floor regions are observed compared to a concatenated scheme that employs an LDGM identity outer code. Finally, we propose a new design of LDPC convolutional code, which we term as time invariant Low Density Parity Check Unit Memory (LDPC-UM) codes. The performance of LDPC block and Low Density Parity Check Unit Memory codes are compared, in each case, the Low Density Parity Check Unit Memory codes performance is at least as good as that of the LDPC block codes from which they are derived. LDPC-UM codes are the convolutional counterparts of LDPC block codes. Here, we describe techniques for the design of low complexity time invariant LDPC-UM codes by unwrapping the Tanner graph of algebraically constructed quasi-cyclic LDPC codes. The Tanner graph is then used to describe a pipelined message passing based iterative decoder for LDPC-UM codes and standard LDPC convolutional codes that outputs decoding results continuously.
Trellis and Turbo Coding
Author: Christian B. SchlegelPublisher: John Wiley & Sons
ISBN: 1119106338
Category : Science
Languages : en
Pages : 521
Book Description
This new edition has been extensively revised to reflect the progress in error control coding over the past few years. Over 60% of the material has been completely reworked, and 30% of the material is original. Convolutional, turbo, and low density parity-check (LDPC) coding and polar codes in a unified framework Advanced research-related developments such as spatial coupling A focus on algorithmic and implementation aspects of error control coding