Giter Club home page Giter Club logo

phd-thesis's Introduction

Complexity and Second Moment of the Mathematical Theory of Communication

This is my PhD dissertation and defense slides.

Abstract

The performance of an error correcting code is evaluated by its block error probability, code rate, and encoding and decoding complexity. The performance of a series of codes is evaluated by, as the block lengths approach infinity, whether their block error probabilities decay to zero, whether their code rates converge to channel capacity, and whether their growth in complexities stays under control.

Over any discrete memoryless channel, I build codes such that: for one, their block error probabilities and code rates scale like random codes'; and for two, their encoding and decoding complexities scale like polar codes'. Quantitatively, for any constants $\pi, \rho > 0$ such that $\pi + 2\rho < 1$, I construct a series of error correcting codes with block length $N$ approaching infinity, block error probability $\exp(-N^\pi)$, code rate $N^{-\rho}$ less than the channel capacity, and encoding and decoding complexity $O(N \log N)$ per code block.

Over any discrete memoryless channel, I also build codes such that: for one, they achieve channel capacity rapidly; and for two, their encoding and decoding complexities outperform all known codes over non-BEC channels. Quantitatively, for any constants $\tau,\rho>0$ such that $2\rho<1$, I construct a series of error correcting codes with block length $N$ approaching infinity, block error probability $\exp(-(\log N)^\tau)$, code rate $N^{-\rho}$ less than the channel capacity, and encoding and decoding complexity $O(N \log(\log N))$ per code block.

The two aforementioned results are built upon two pillars---a versatile framework that generates codes on the basis of channel polarization, and a calculus--probability machinery that evaluates the performances of codes.

The framework that generates codes and the machinery that evaluates codes can be extended to many other scenarios in network information theory. To name a few: lossless compression with side information, lossy compression, Slepian--Wolf problem, Wyner--Ziv Problem, multiple access channel, wiretap channel of type I, and broadcast channel. In each scenario, the adapted notions of block error probability and code rate approach their limits at the same paces as specified above.

phd-thesis's People

Contributors

symbol1 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.