Rick,
Thanks for the info!
Somewhere along the line I must have gotten some wrong numbers, if RS is
really that good, it totally makes sense to use it, at least on the more
important bits.
Thanks for correcting me!
73s
Daniel Mundall
On Wed, Mar 6, 2013 at 10:03 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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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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