David, the current block scheme of soft-decision demodulators (as far as I
konow) includes a demodulator that still outputs integer values, but it
outputs current quantisation tresholds and accepts a feedback for the
decision making process. My theoretical demodulator would be a non-decision
demodulator which outputs raw data. It is true that you would have to have
much more memory and processing power available in order to process such
large data quantities but today we have such posibilities.
It is quite intersting that already in 1993 an article was published,
Dynamic Adaptation of Quantisation Thresholds for Soft-Decision Viterbi
Decoding with a Reinforcement Learning Neural Network. It uses a soft
demodulator wich uses feedback from a neural network. That's a concept 25
Years old, when neural networks were more like a concept with little
practical implementations. Today we already have such networks available.
So there are 3 steps we could take: Develop a non decision making
demodulator, feeding raw data into a neural network, feeding processed raw
data intro the modified decoder, made able to process floating point binary
data.
Tomas, the encoding side is a different story. The H 2.64 loop filter
prevents some encoding artefacts from becoming to obvious. Now, first we
would have to consider wich artefacts we would like to smoothen, encoding
artafacts or specific possible BER artefacts. Then, we would have objective
and subjective artefacts, that means you would have artefacts affecting the
objective form of the signal, but we do not know unless we test with a
group of subjects how muh these artefacts affect the subjective perception
of the speech.
The only simple approach for an objective filtering, for both encoding and
BER artefacts I see here is a filter which is modelled by a neural network.
You take the filter parameters and model them with the help of a neural
network which compares original and encoded signal.
However, I would leave this for another topic and I would concentrate on
the receiver part for now.
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