Shayan Hassanpour

Source & Joint Source-Channel Coding Schemes Based on the Information Bottleneck Framework


Front	Back

ISBN:

978-3-8440-8743-7

Series:

Dissertationen aus dem Arbeitsbereich Nachrichtentechnik der Universität Bremen
Herausgeber: Prof. Dr.-Ing. Armin Dekorsy
Bremen

Volume:

Keywords:

Information Bottleneck; Source Coding; Joint Source-Channel Coding; Data Compression

Type of publication:

Thesis

Language:

English

Pages:

196 pages

Figures:

38 figures

Weight:

291 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

48,80 € / 61,10 SFr

Published:

August 2022

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		36,60 EUR
	Action		Purchase in obligation and display of file - 1,6 MB (1631729 Byte)
	Action		Purchase in obligation and download of file - 1,6 MB (1631729 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 245 kB (250687 Byte)
	Action		Download of file - 245 kB (250687 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

The vast potentials of the Information Bottleneck (IB) method have already been well recognized in the communications engineering society. This fact can be readily confirmed by the miscellaneous list of applications in which the IB method has been utilized, from the design of analog-to-digital converters for receiver front ends to the construction of polar codes and low-complexity discrete channel decoding schemes with promising performance and many more.

To put it briefly, its principal idea is to compress a random variable such that its information content w.r.t. a statistically correlated (relevant) variable is mostly retained. This information preservation capability is rather flexible and, in effect, can be tuned by twiddling a parameter that establishes a basic trade-off between the compactness and informativity of the resultant outcome. The IB method accomplishes this by utilizing the same concept of Mutual Information to quantify both sides of this trade-off.

Within the scope of this dissertation, the main idea and the mathematical methodology of the IB framework have been extended (over generic setups that frequently appear in a diversity of applications) to devise novel compression schemes for both the centralized and distributed (noisy) Source and Joint Source-Channel Coding scenarios. The proposed Joint Source-Channel Coding schemes reveal their practical importance, especially in applications where stringent end-to-end latency requirements must be met as they can obviate the call for separate channel encoding and decoding on the error-prone forward links. Further, they show a high theoretical relevance since the separation of Source and Channel Coding does not necessarily lead to optimal results, e.g., in transmission systems with finite block lengths.