Download Error-Correction Coding and Decoding: Bounds, Codes, by Martin Tomlinson, Cen Jung Tjhai, Marcel A. Ambroze, PDF

By Martin Tomlinson, Cen Jung Tjhai, Marcel A. Ambroze, Mohammed Ahmed, Mubarak Jibril

This booklet discusses either the idea and useful purposes of self-correcting info, generally known as error-correcting codes. The functions integrated display the significance of those codes in quite a lot of daily applied sciences, from smartphones to safe communications and transactions. Written in a comfortably comprehensible sort, the ebook provides the authors’ twenty-five years of study equipped into 5 parts:

Part I is anxious with the theoretical functionality possible by utilizing errors correcting codes to accomplish communications potency in electronic communications systems.

Part II explores the development of error-correcting codes and explains the various households of codes and the way they're designed. suggestions are defined for generating the superior codes.

Part III addresses the research of low-density parity-check (LDPC) codes, basically to calculate their preventing units and low-weight codeword spectrum which determines the functionality of those codes.

Part IV bargains with decoders designed to gain optimal performance.

Part V describes purposes which come with mixed errors correction and detection, public key cryptography utilizing Goppa codes, correcting mistakes in passwords and watermarking.

This ebook is a priceless source for an individual drawn to error-correcting codes and their functions, starting from non-experts to execs on the leading edge of analysis of their field.

This publication is open entry lower than a CC by means of 4.0 license.

Show description

Read Online or Download Error-Correction Coding and Decoding: Bounds, Codes, Decoders, Analysis and Applications PDF

Similar information theory books

Information theory: structural models for qualitative data

Krippendorff introduces social scientists to details conception and explains its software for structural modeling. He discusses key issues akin to: the best way to ensure a data thought version; its use in exploratory learn; and the way it compares with different methods comparable to community research, course research, chi sq. and research of variance.

Ours To Hack and To Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet

The on-demand economic climate is reversing the rights and protections staff fought for hundreds of years to win. usual net clients, in the meantime, preserve little keep watch over over their own information. whereas promising to be the nice equalizers, on-line systems have frequently exacerbated social inequalities. Can the web be owned and ruled otherwise?

Extra info for Error-Correction Coding and Decoding: Bounds, Codes, Decoders, Analysis and Applications

Example text

7 (Asymptotic Elias Bound) The information rate αq (δ) is upper bounded by, αq (δ) ≤ 1 − Hq (θ − θ (θ − δ)) provided 0 < δ < θ where θ = 1 − q −1 . 5 MRRW Bounds The McEliece–Rodemich–Rumsey–Welch (MRRW) bounds are asymptotic bounds obtained using linear programming. 8 (Asymptotic MRRW Bound I) Provided 0 < r < θ , θ = 1 − q −1 then, 1 (q − 1 − (q − 2)δ − 2 δ(1 − δ)(q − 1)) αq (δ) ≤ Hq q The second MRRW bound applies to the case when q = 2. 2 Bounds on the Construction of Error-Correcting Codes where g(x) = H2 1− √ 1−x 2 19 .

325 Research Laboratory of Electronics, MIT (1957) References 23 21. : Principles of Communication Engineering. Wiley, New York (1965) 22. : Modulation and demodulation for probabilistic coding. IEEE Trans. Inf. 0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material.

The difference in the performance of the codes using the binomial approximation and actual weight distribution decreases as NEb0 increases. 8 shows the performance of the (255, 120, 40) using a binomial weight distribution. 2 dB. 2 dB, we δ can estimate the performance of the (255, 120, 40) code under maximum likelihood soft decision decoding in the AWGN channel using the lower union bound. Fig. 2 Performance of Self-dual Codes A self-dual code C has the property that it is its own dual such that, C = C ⊥.

Download PDF sample

Rated 4.04 of 5 – based on 44 votes