Advanced topics in information theory

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(Participants)
(Participants)
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== Participants ==
== Participants ==
-
Mubasher Beg
+
*Mubasher Beg
-
Shahida Jabeem  
+
*Shahida Jabeem  
-
Qasim Maqbool
+
*Qasim Maqbool
-
Muhammad Bilal  
+
*Muhammad Bilal  
-
Muzammad Baig
+
*Muzammad Baig
-
Zartash Uzmi
+
*Hassan Mohy-ud-Din
-
Shahab Baqai  
+
*Zartash Uzmi
-
Abubakr Muhammad
+
*Shahab Baqai  
 +
*Abubakr Muhammad
== Topics ==
== Topics ==

Revision as of 17:41, 4 July 2009

Contents

Reading Group: Advanced Topics in Information Theory

Summer 2009

Participants

  • Mubasher Beg
  • Shahida Jabeem
  • Qasim Maqbool
  • Muhammad Bilal
  • Muzammad Baig
  • Hassan Mohy-ud-Din
  • 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

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