Have a look at Polar Coding.

Sam Hunt

On 06/03/2013 22:48, David Rowe wrote:
> I've been talking to some Turbo Coding and LPDC guys and I now think
> they would be worth testing, for example with block sizes of 256 bits.
> While these codes are optimal at blocks with several thousand bits (a
> few seconds delay of Codec 2) they might still be useful at block
> sizes/delays of a few 100ms.
>
> We could also develop a "weak signal" mode which has all the delay we
> need to interleave over time, use the best codes available, and just put
> up with 5-10 seconds delay and no SSB-type break in experience.  If it
> out-performs SSB (say error free, noise free voice at SNR=-3dB) then it
> might be useful in some applications.
>
> - David
>
> On Wed, 2013-03-06 at 12:52 -0800, Bruce Perens wrote:
>> Some of the super optimal coding schemes don't work on really small
>> blocks.
>>
>> On 03/06/2013 11:50 AM, ZPO wrote:
>>> Are there any LDPC implementations out there that aren't patent
>>> encumbered?
>>>
>>> 73-KY9K/Brian
>>>
>>> On Mar 6, 2013 10:05 AM, "Rick Muething" <[email protected]>
>>> wrote:
>>>          Daniel,
>>>           
>>>          RS and similar block codes are fairly efficient and have
>>>          some nice properties especially when mixed (layered) with
>>>          other types of FEC.  E.g.  Viterbi (convolutional) type
>>>          decoders work well with random errors.  But these type codes
>>>          perform poorly in burst error situations.  Sometimes if the
>>>          timing permits bursts can be reduced by interleaving but
>>>          there are limits especially when you don’t want to delay the
>>>          signal much in time as in the case of digital voice.  In
>>>          such cases adding a block code “outside” the convolutional
>>>          codes can be effectively in many systems.
>>>           
>>>          Reed Solomon adds two “parity” characters for each carrier
>>>          correction desired. ( a character can be any number of bits
>>>          from 2 on up although often is done with 8 bit characters)
>>>          E.g. if I were sending 8 bit characters and I wanted to
>>>          correct up to 8 character errors I would have to add 16  8
>>>          bit “parity” characters to my data and these could correct
>>>          up to 8 character errors in the entire block of data (data +
>>>          Parity).  The amount of Parity you need is a function of the
>>>          channel and the error rates.  So the “efficiency” of a RS
>>>          type code is a function of how many errors you wish to be
>>>          able to correct.  For example if I were sending a block of
>>>          100 data characters and wished to correct up to 8 erroneous
>>>          characters I would need to add 16 total parity characters so
>>>          my overall efficiency would be 100/116 or about 86% which is
>>>          reasonable.  As the number of corrected characters desired
>>>          increases the efficiency of course goes down but the
>>>          robustness increases.  Correcting up to 32 characters would
>>>          require 64 characters of Parity so the efficiency would be
>>>          100/164 or about 61%.  The beauty of RS and other similar
>>>          block codes is they are tolerant of block errors.  It
>>>          corrects by character whether there is one bit in error or 8
>>>          bits in error of the character.  So it compliments the
>>>          convolutional code nicely by correcting bursts that the
>>>          convolutional code can’t correct.
>>>           
>>>          This is why layering two types of FEC is effective and why
>>>          for example NASA often uses layered block coding on top of
>>>          convolutional coding in very weak signal transmission.
>>>           
>>>          In the end the amount and selection of FEC is a function of
>>>          the channel and the type and number and type of errors
>>>          expected.  But in general adding the correct type and level
>>>          of FEC can usually provide a net improvement over uncoded
>>>          systems in terms of net BER.  This is usually expressed as
>>>          equivalent dB improvement. (the amount of improvement you
>>>          would get by increasing the S/N by the same number of dB.
>>>           
>>>          In digital Voice we have another issue in that unlike in
>>>          binary message transmission some level of uncorrected errors
>>>          may be acceptable (resulting in some speech artifact which
>>>          may not severely impact intelligibility).
>>>           
>>>          I have experimented with this a lot in the development of
>>>          WINMOR and often optimization is the result of iterating
>>>          different promising FEC coding and evaluating them over
>>>          specific (some times standardized CCIR) channels using a HF
>>>          channel simulator.   Prediction with theory alone is
>>>          possible on White Gaussian Noise channels but we seldom see
>>>          these channels in HF propagation.
>>>           
>>>          73,
>>>           
>>>          Rick Muething, KN6KB
>>>           
>>>          
>>>          
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Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the  
endpoint security space. For insight on selecting the right partner to 
tackle endpoint security challenges, access the full report. 
http://p.sf.net/sfu/symantec-dev2dev
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