Advanced topics in information theory

From CYPHYNETS

(Difference between revisions)
Jump to: navigation, search
(July 7: Organization. Recap of CS-683)
(Participants)
Line 5: Line 5:
== Participants ==
== Participants ==
-
 
+
Mubasher Beg
 +
Shahida Jabeem
 +
Qasim Maqbool
 +
Muhammad Bilal
 +
Muzammad Baig
 +
Zartash Uzmi
 +
Shahab Baqai
 +
Abubakr Muhammad
== Topics ==
== Topics ==

Revision as of 17:40, 4 July 2009

Contents

Reading Group: Advanced Topics in Information Theory

Summer 2009

Participants

Mubasher Beg Shahida Jabeem Qasim Maqbool Muhammad Bilal Muzammad Baig Zartash Uzmi Shahab Baqai Abubakr Muhammad

Topics

  • Rate distortion theory
  • Network information theory
  • Kolmogorov complexity
  • Quantum information theory

Sessions

July 7: Organization. Recap of CS-683

  • Basic organization, presentation assignments.
  • Review of Information theory ideas
  • Entropy, AEP, Compression and Capacity

Entropy of a random variable is given by

H(X) = -\sum_{x \in \mathcal{X}} p(x) \log p(x).

The capacity of a channel is defined by

\mathcal{C} = \max_{p(x)} I(X; Y).

Compression and Capacity determine the two fundamental information theoretic limits of data transmission, H \leq R \leq \mathcal{C}.

  • A review of Gaussain channels and their capacities.
  • Let us take these analysis one step further. How much do you loose when you cross these barriers?
  • We saw one situation when you try to transmit over the capacity. By Fano's inequality

H(X|Y) \leq H(E) + P_e (|\mathcal{X}|-1)


  • Rate distortion: A theory for lossy data compression.

July 14: Rate distortion theory - I

July 21: Rate distortion theory - II

July 28: Network Information theory- I

Aug 04: Network Information theory- II

Aug 11: Wireless networks, cognitive radios

Aug 18: Multiple access channels, network coding techniques

Personal tools