Lecture Series in Mobile Telecommunications and Networks (1583KB)

From plain old Telephony to flawless mobile audio communication
have been converted, to introduce wideband services everywhere, where you have the GSM phone. However, that is
not a standard but just an idea from research.
Mobile audio – communication
Now we have nice coding schemes, you might introduce wideband, super-wideband, or artificial extension or whatever,
but we have adverse conditions on the radio channel and we need error protection. There are very sophisticated
techniques. You have heard about turbo-coding, turbo error protection, turbo error correction and error concealment.
4 Turbo error protection
The turbo concept can be extended, which was originally defined for two channel codes which help each other in an
iterative way at the receiver. It can be extended to a channel decoder and to a source decoder. At the receiving end, we
have a channel decoder which produces soft information – and soft information means that the channel decoder does
not produce 0 and 1, but it produces reliability information. It says, eventually it is 0 or 1, with the probability. Then we
have a soft decision source decoder, where the decoder exploits some residual redundancy which is still in the
parameters. If we model the speech tract, the vocal tract, then the parameters evolve more or less smoothly, because
we do the analysis every 20 milliseconds, 50 times per second, and the vocal track doesn’t make any jumps.
There is still correlation in the parameters – and redundancy in terms of the distribution. The parameters are not white
noise. This can be used such that we do a preliminary parameter decoding and we exploit the redundancy on the level
of the parameters, and we feed that information back – it is called extrinsic information – through the channel decoder
and the channel decoder tries once more. There is a second decoding using the knowledge from the channel end, the
additional auxiliary information from the source decoder.
In this slide, the source decode has better information and can improve once more and so, by several iterations, you
have iterative source channel decoding and we can improve tremendously.
Example 5: iterative source-channel decoding
Let’s listen to one example of how we can improve by
spending at the receiving end on just additional signal
processing complexity. If the channel is clear, we do not need
it but, if the channel is getting worse, we can improve it by
additional processing. [Example of speech recording played]
In this case, we used just plain PCM to demonstrate the
concept. [Music played] We can maintain the quality if we
are allowed to use this complexity.
Mobile audio – communication
Now I would like to share a vision of what more we can do
than just introducing wideband and artificial wideband
extensions.
Iterative source-channel decoding (ISCD)
This is a comparison with state-of-the-art decoding, where we
have hard-decision decoding that is table look-up decoding
in the source decoder, or soft decoding in the source decoder
without iteration. We then do some iterations – here, in this
case, it is 10 iterations – and we see a significant
improvement. This is the parameter signal-to-noise ratio, the
quality of the filter coefficients of the speech model, for
example. This is the channel quality here, and we have a
good channel here, and a bad channel. If we have a demand
for a certain quality, we can maintain it, even in very adverse
channel conditions.
The Royal Academy of Engineering 53