# Advanced topics in information theory

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(New page: = Reading Group: Advanced Topics in Information Theory = Summer 2009 == Participants == == Topics == * Rate distortion theory (Weeks 1,2) * Network information theory (Weeks 3,4) ...) |
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== Topics == | == Topics == | ||

- | * Rate distortion theory | + | * 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 <math>H(X) = -\sum_x p(x) \log p(x).</math> | ||

+ | |||

+ | The capacity of a channel is defined by <math>C = \max_{p(x)} I(X; Y).</math> | ||

+ | |||

+ | Compression and Capacity determine the two fundamental information theoretic limits of data transmission, | ||

+ | <math>H \leq R \leq C.</math> | ||

+ | |||

+ | * 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? | ||

+ | |||

+ | |||

+ | === 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 === |

## Revision as of 17:25, 4 July 2009

# Reading Group: Advanced Topics in Information Theory

Summer 2009

## Participants

## 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) = − | ∑ | p(x)logp(x). |

x |

The capacity of a channel is defined by *C* = max_{p(x)}*I*(*X*;*Y*).

Compression and Capacity determine the two fundamental information theoretic limits of data transmission,

- 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?