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Impact of quantization on performance

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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.

Free download from Shareware Connection - Impact of quantization on performance of convolutional codes plots the expected BER curve of soft decision quantized Viterbi decoders

Publisher: B Gremont | License: Freeware | Price: 0.00
Version: 1.1 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 19-01-2013 | Rating: 0 | Title: Impact of quantization on performance

Author Url: http://www.mathworks.com/
Program Info Url: http://www.mathworks.com/
Download Url: http://www.mathworks.com/matlabcentral/fx_files/14840/1/BERcurve_CV_soft2_QA.zip

More downloads from Impact of quantization on performance publisher B Gremont:

Hard decision convolutional coding/decoding - hard decision convolutional coding/decoding - A few m-files showing CV encoding/Viterbi hamming distance decoding

MPSK simulation - MPSK simulation plots the simulated and theoretical BER/SER curve of MPSK

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