Code covered by the BSD License
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Pb=ComputeBerSoftDecisionQuan...
Pb=ComputeBerSoftDecisionQuantized(EbNodBvals, Q, Bd, coderate)
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Pb=ComputeBerSoftDecisionUnqu...
Pb=ComputeBerDoftDecisionUnquantized(EbNodBvals, Bd, coderate)
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[Bd, dfree]=EstimateBitWEF(Gp...
[Bd, dfree]=EstimateBitWEF(Gpoly)
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[K, M, nu, n, k, coderate, St...
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BERcurve_CV_soft2.m
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Impact of quantization on performance of convolutional codes (soft decisions)
by B Gremont
01 May 2007
(Updated 01 May 2007)
Plots the expected BER curve of soft decision quantized Viterbi decoders
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| File Information |
| Description |
Computes BER v EbNo curve for convolutional encoding / soft decision
Viterbi decoding scheme assuming BPSK.
Brute force Monte Carlo approach is unsatisfactory (takes too long)
to find the BER curve.
The computation uses a quasi-analytic (QA) technique that relies on the
estimation (approximate one) of the information-bits Weight Enumerating
Function (WEF) using
A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used. |
| MATLAB release |
MATLAB 7.4 (R2007a)
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