By Alain Glavieux
This ebook offers a accomplished review of the topic of channel coding. It starts off with an outline of data conception, targeting the quantitative size of knowledge and introducing primary theorems on resource and channel coding. the fundamentals of channel coding in chapters, block codes and convolutional codes, are then mentioned, and for those the authors introduce weighted enter and output interpreting algorithms and recursive systematic convolutional codes, that are utilized in the remainder of the e-book.
Trellis coded modulations, that have their fundamental functions in excessive spectral potency transmissions, are then lined, sooner than the dialogue strikes directly to a complicated coding method known as turbocoding. those codes, invented within the Nineteen Nineties through C. Berrou and A. Glavieux, exhibit extraordinary functionality. the variations among convolutional turbocodes and block turbocodes are defined, and for every family members, the authors current the coding and interpreting options, including their performances. The booklet concludes with a bankruptcy at the implementation of turbocodes in circuits.
As such, somebody interested by the components of channel coding and blunder correcting coding will locate this booklet to be of beneficial assistance.Content:
Chapter 1 info concept (pages 1–40): Gerard Battail
Chapter 2 Block Codes (pages 41–128): Alain Poli
Chapter three Convolutional Codes (pages 129–196): Alian Glavieux and Sandrine Vaton
Chapter four Coded Modulations (pages 197–253): Ezio Biglieri
Chapter five Turbocodes (pages 255–306): Claude Berrou, Catherine Douillard, Michel Jezequel and Annie Picart
Chapter 6 Block Turbocodes (pages 307–371): Ramesh Pyndiah and Patrick Adde
Chapter 7 Block Turbocodes in a pragmatic surroundings (pages 373–414): Patrick Adde and Ramesh Pyndiah
Read Online or Download Channel Coding in Communication Networks: From Theory to Turbocodes PDF
Similar information theory books
Coordinated Multiuser Communications offers for the 1st time a unified remedy of multiuser detection and multiuser deciphering in one quantity. Many communications structures, equivalent to mobile cellular radio and instant neighborhood region networks, are topic to multiple-access interference, attributable to a mess of clients sharing a standard transmission medium.
Dieses Lehrbuch bietet eine elementare Einführung in ein mathematisch anspruchsvolles Gebiet der modernen Kryptographie, das zunehmend an praktischer Bedeutung gewinnt. Die relevanten Tatsachen über elliptische Kurven und Public-Key-Kryptographie werden ausführlich erläutert. Dabei werden nur geringe Vorkenntnisse vorausgesetzt, um den textual content für Studierende der Mathematik und Informatik ab dem five.
Maintaining directly to fact is a superb heritage of knowledge, from its inception within the flora and fauna to its function within the transformation of tradition to the present web mania and is attendant resources and liabilities. Drawing at the heritage of rules, the main points of data know-how, and the limits of the human , Borgmann illuminates the connection among issues and symptoms, among fact and data.
- Forward Error Correction Based On Algebraic-Geometric Theory
- Instruction Selection: Principles, Methods, and Applications
- Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint
- Developments in Biometrics
- Bent Functions: Fundamentals and Results
- Information und die innere Struktur des Universums
Extra info for Channel Coding in Communication Networks: From Theory to Turbocodes
XM }, xi ∈ Xn regardless of i, 1 ≤ i ≤ n. The emission of the mth codeword represents the mth message. e. the decoder associates the number m identifying the decoded message to the received sequence y, if: Pr(y|xm ) > Pr(y|xm ) ∀ m = m, 1 ≤ m ≤ M. 23] An error occurs if for a transmitted m this decision rule leads to m which is different from m. 24] Pr(y|xm )φm (y), y∈Yn where Pem is the probability of an error when m is transmitted. 25] where s is a positive parameter, which is arbitrary for the moment.
Xn be the symbols of the codeword at channel input and y1 , y2 , . . , yn be their corresponding symbols at the output. The independence of the successive transitions in the channel leads to: n Pr(yi |xi ), ∀ x ∈ Xn , ∀ y ∈ Yn . Pr(y|x) = i=1 Restricting ourselves to codes where the successive codeword symbols are chosen by random coding independently of each other, following the same law p(xi ), we have: n P (x) = p(xi ), x = (x1 , x2 , . . 29] can be transformed. e. with all the possible codewords with n symbols in the channel input alphabet: the sum of all the products of n terms is equal to the nth power of the sum of the terms written for all the symbols of this alphabet.
Vol. 44, No. 10, pp. 1261–1271, Oct. 1996.
Channel Coding in Communication Networks: From Theory to Turbocodes by Alain Glavieux