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dal ha scritto:




Wonderful!




Can you explain this to me? This is a critical point!
I had a look at a master thesis of a guy here at the university...
He wrote that a floating point representation is required (but he
didn't write why).
It would be much easier to operate with fixed-point numbers, but I
should understand why...




Do you mean the Viterbi decoder?




I think you mean with fixed-point numbers...




Thank you for your suggestions!
The problem is that my boss told me:
"Ok, we want to have an FPGA implementation of an 802.11a modem.
Before starting with VHDL, we need to understand which are the critical
blocks and find an appropriate FPGA for our project.
You have 1 week for this task."
We will have a meeting during the next days, I will explain him the
approach you suggested me.

Ciao,
Franco.

A floating point FFT is likely to produce more accurate results than a
fixed point FFT. That said you can make a fixed point FFT as accurate
as you wish.

For 802.11a, which I believe uses a maximum of 64QAM, I find it hard to
believe that floating point is necessary. Basically a less accurate fft
will add a little extra noise to your system. If it's small enough its
not worth worrying about.





Viterbi is not such a hard thing to implement but it can be tricky to
get it performing properly.

There's also the QAM/BPSK/QPSK modulator/demodulator and data formatting
for the MAC.

The real magic in these modems, however, is the front end processing
required to equalize the channel. Issues such as AGC, alignment and
equalization are a real swine to get right and have a huge bearing on
the quality of results (at least thats my take from implementing a
couple of wireline modem standards).

Ok, you are the second person telling me to use a fixed-point
representation, then it must be the way it has to be done... :o)
Let's consider the problem from scratch.
Let's suppose that I want to implement a 64-QAM symbol mapper.
This means that every 6 input bits the mapper has to output the
correspondent symbol, that consist af a real and an imaginary part. The
problem is to choose the representation of each of these numbers. We
know that each number can be one of the following: -7, -5, -3, -1, 1,
3, 5, 7. The 802.11a standard says that we must consider a correction
factor of 1/sqrt(42) that is approximately 0.154. Then we have to
multiply all the above mentioned numbers for this factor. Then, we are
not dealing with integer numbers! After this, to make it simple, we
have to apply the 64-point fft to 64 complex numbers (made up of two
real numbers of the type described above). The problem is now to
understand how to represent these numbers. I had a look at a master
thesis (not so good thesis, I must say). It considers all these numbers
represented in 32 floating-point format. That means, for example in the
64-QAM mapper, we have a ROM that outputs the correspondent 32 floating
point symbols (32+32 for real and imaginary part) according to the 6
input bits. With a floating-point representation the ROM outputs
exactly the calculated values. If I want to use a fixed-point
representation, how should I convert the non-integer numbers to
fixed-point numbers? And furthermore, what about the output of the IFFT
block, how should I interpret the fixed-point output numbers?

It's clear that I have few ideas but well-confused... :o)
But that's why I started this post.... :o)))


Can you go more in detail?
I think the Viterbi decoder is another critical part.


I WANT, I WANT! :o)))))))
But probably other people are interested and can contribute on the
newsgroup...

Ciao,
Franco

Hi Andy!

Andy Ray ha scritto:

Perfect.
Then it's only a matter of studying the FFT theory.
The important thing is to understand that this block is not so critical
as it appeared.
And of course, if you can suggest some documentation it would be really
useful.


This is a really interesting point.
Can you suggest some text/documentation in order to understand a
systematic way to evaluate the noise due to the fixed-point
representation? Of course this noise must be somehow affected by the
number of bits we dedicate to the foxed-point representation. I think
an in-depth theory must be available in some book...

And another critical point... Even the DAC (after the ofdm modulator,
before the RF part of the transmitter) introduces noise, due to the
number of bits of its resolution. How can we quantify its effects?
Also here, this problem must have been already studied.



I think we will consider these problems at a later stage.
As you have probably understood, we are really at the beginning of this
work and for the moment we are mostly acquiring information. The
complete project (a 802.11a transceiver) will take several months
(several years?). Unfortunately the people involved in my group lack of
the necessary expertise (but we are of course working to build it up).

Ciao,
Franco

Hi, Ray.
Thanks for your suggestions.
What do you mean with the following two phrases?




and




Ciao,
Franco

Floating point is a numeric representation to fit a larger dynamic range
into a fixed number of bits. 32 bits can represent 2^32 unique values
regardless of the format. For fixed point representation, those 2^32
values are equally spaced between 0 and 2^32-1 for unsigned. Floating
point reserves some of the bits to indicate a scale factor to extend the
range of values that can be represented, but that comes at the price of
minimum resolution that varies depending on the scale. IEEE single
precision floating point gives you 24 bits of accuracy (instead of 32),
but that is scaled so that the msb of the 24 bit field is always
significant.

The FFT conserves energy, it can be looked at as redistributing the
input signal in time. If you have a full scale input that is at a
single frequency that has a period that is an integer sub-multiple of
the FFT size, all the energy from that signal will fall into a single
FFT bin. For an N point FFT, the output at that bin will be N times the
input signal amplitude. Since the input is at full scale already, there
is nothing you can do to the signal to increase the output at that bin
more than N* the full scale input. Adding any other frequency content
will move some of that energy to another FFT bin, diminishing the
spectral content in that bin. What I am saying, is the maximum output
value from an N point FFT is N times the maximum input value. For a 64
point FFT, the maximum output is no more than 64 times the maximum
input. For a fixed point representation, that would require the output
to have at most six more bits than the input to represent all possible
outputs without either overflowing or truncating the lsbs.

Ray Andraka ha scritto:
[cut]


More than clear!
Anyway, after my question I started to write down some formulas and I
found exactly what you have just explained me. :o)

Ciao,
Franco