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Fountain Codes

Amin Shokrollahi. EPFL and Digital Fountain, Inc. Fountain Codes. BEC. BEC(p 1 ). BEC(p 2 ). BEC(p 3 ). BEC(p 4 ). BEC(p 5 ). BEC(p 6 ). Communication on Multiple Unknown Channels. Example: Popular Download. Example: Peer-to-Peer. Example: Satellite.

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Fountain Codes

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  1. Amin Shokrollahi EPFL and Digital Fountain, Inc. Fountain Codes

  2. BEC

  3. BEC(p1) BEC(p2) BEC(p3) BEC(p4) BEC(p5) BEC(p6) Communication on Multiple Unknown Channels

  4. Example: Popular Download

  5. Example: Peer-to-Peer

  6. Example: Satellite

  7. The erasure probabilities are unknown. Want to come arbitrarily close to capacity on each of the erasure channels, with minimum amount of feedback. Traditional codes don’t work in this setting since their rate is fixed. Need codes that can adapt automatically to the erasure rate of the channel.

  8. Traditional FEC Original Content Blocks Original Original Original Redundant Redundant Redundant

  9. Problems with FEC Fraction of losses must be less than K/(N+K) Worst user dictates amount of redundancy Loss provisioning is complicated and leads to overhead

  10. Users reconstruct Original content as soon as they receive enough packets Original content Encoded packets Encoding Engine Transmission What we Really Want Reconstruction time should depend only on size of content

  11. Content Enc Digital buckets

  12. Fountain Codes Sender sends a potentially limitless stream of encoded bits. Receivers collect bits until they are reasonably sure that they can recover the content from the received bits, and send STOP feedback to sender. Automatic adaptation: Receivers with larger loss rate need longer to receive the required information. Want that each receiver is able to recover from the minimum possible amount of received data, and do this efficiently.

  13. Distribution on Fountain Codes

  14. Universality and Efficiency [Universality] Want sequences of Fountain Codes for which the overhead is arbitrarily small [Efficiency] Want per-symbol-encoding to run in close to constant time, and decoding to run in time linear in number of output symbols.

  15. Choose weight Prob Weight 1 0.055 2 0.3 Weight table 3 0.1 4 0.08 100000 0.0004 The Fountain Coding Process Input symbols Choose 2 Random original symbols XOR 2 Insert header, and send

  16. Decoding

  17. Decoding

  18. Decoding

  19. Decoding

  20. Decoding

  21. Decoding

  22. Decoding

  23. Decoding

  24. Decoding

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