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Copy file name to clipboardExpand all lines: README.md
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@@ -9,13 +9,14 @@ A Fast Maximum-Likelihood Decoder for Convolutional Codes. Vehicular Technology
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This implementation provides a significant speedup at moderate SNRs, but is slower
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at low SNR.
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* Viterbi: implements the classical Viterbi algorithm.
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This implementation, which is faster than the one present in gr-trellis, is
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better-suited than Lazy Viterbi for low SNRs.
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This implementation is better-suited than Lazy Viterbi for low SNRs.
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* Dynamic Viterbi: Switch between the two implementations mentionned above,
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depending on SNR.
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One GRC example is provided in the examples/ directory.
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One GRC example is provided in the examples/ directory:
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*`simple.grc` simple example showing how each decoder should be use in a typical flowgraph. You will have to populate the variable `base_dir`, and put the full path to the source folder of `gr-lazyviterbi` (for example: `/usr/local/src/gr-lazyviterbi`).
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There are also two python scripts :
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*`ber_vs_ebn0_75_awgn.py` lets you compare the BER of this algorithm
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and the gr-trellis's implementation of the classical Viterbi algorithm for a
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