

Researchers at MIT Develop The Fastest Possible Data Transmission Method - polarslice
http://bostinno.com/2012/02/12/researchers-at-mit-develop-the-fastest-possible-data-transmission-method/

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pangram
Based on a paper in 2007:
[http://allegro.mit.edu/pubs/posted/journal/2007-erez-
trott-w...](http://allegro.mit.edu/pubs/posted/journal/2007-erez-trott-
wornell-it.pdf) Patent: <http://www.google.com/patents/US8023570>

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wickedchicken
Thank you for this, it's quite difficult to follow the paper trail to the
exact algorithm they're talking about. I had seen a talk about an extension to
Reed-Solomon that promised more efficiency and was hoping this would talk
about that, instead it looks like a wireless-optimized fountain code.

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Geee
Really interesting, but definitely this has little to do with 'fastest
possible data transmission'. It just is a nice way to find the optimal code
rate for the current channel. Current systems just adapt the code rate and
send all data again. I wonder if something similar exists or could be
developed for source encoding, i.e. progressively enhanced video streaming, so
that if you want higher quality you would just combine the original with
additional data.

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wickedchicken
> It just is a nice way to find the optimal code rate for the current channel.
> Current systems just adapt the code rate and send all data again.

Actually, this is likely an improvement on rateless/fountain codes. A key
feature of these codes is the ability to reconstruct an entire message block
if you collect "enough" subcodes, regardless of order. These actually obviate
the need for retransmission and are crucial for things like reliable multicast
wireless: it would be a nightmare to keep track of each of your receivers and
retransmit lost packets to each one, with rateless codes you theoretically
don't even need to know your receivers are there. Just keep spewing out data
and eventually they will receive enough to make up for missing blocks.

A casual glance at this paper implies that they've found a model of these
codes that takes into account standard models of noisy wireless channels;
using this model allows you to optimize your code to have less error
correcting overhead (meaning you need to collect fewer packets to successfully
reconstruct the original message).

More info, and perhaps a more coherent explanation:
<http://en.wikipedia.org/wiki/Fountain_code>

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United857
The bottom part of this Quora snwer is a analogy of the technique in the paper
for sending physical money which might help you better understand things:

[http://www.quora.com/What-is-the-safest-way-to-send-
someone-...](http://www.quora.com/What-is-the-safest-way-to-send-
someone-1-000-cash-physical-bills/answer/Ben-Maurer)

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robgibbons
I half expected to be reading about quantum entanglement. Rats!

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marshallp
Does this have any bearing on AI or machine learning. According to Marcus
Hutter, the best AI is one that can compress the most (the hutter prize for
compression). If compression is the same problem as data transmission, than
this might an optimal AI algorithm as well.

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ramchip
This isn't about compressing data, it's rather the opposite: expand the data
so that even if a certain portion of it is affected by interference, the
original data can still be reconstructed without errors.

To take a really simple code, let's say you have this data:

10110

Then you append a checksum (actually a parity bit here) to the data 1+0+1+1+0
= 1 (in binary)

101101

Now let's say there's interference and a bit flips:

100101

The receiver calculates the checksum and sees that the sum is 1+0+0+1+0 = 0,
which does not fit since the sum is supposed to be 1. Thus the receiver knows
that an error happened and can request a retransmission. More advanced codes
like those discussed here can allow arbitrary levels of error correction,
instead of only detection of single errors like the parity bit; but higher
levels of error correction come with an overhead, and apparently the
innovation here is a technique to reduce this cost in wireless networks.

~~~
klipt
> This isn't about compressing data, it's rather the opposite: expand the data
> so that even if a certain portion of it is affected by interference, the
> original data can still be reconstructed without errors.

The two problems (data compression, and noisy channel coding) are tied
together quite neatly by information theory though:
<http://www.inference.phy.cam.ac.uk/mackay/itila/book.html>

