openofdm/docs/source/sync_long.rst

Symbol Alignment
================

- **Module**: :file:`sync_long.v`
- **Input**: ``I (16), Q (16), phase_offset (32), short_gi (1)``
- **Output**: ``long_preamble_detected (1), fft_re (16), fft_im (16)``

After detecting the packet, the next step is to determine precisely where each
OFDM symbol starts. In 802.11, each OFDM symbol is 4 |us| long. At 20 MSPS
sampling rate, this means each OFDM symbol contains 80 samples. The task is to
group the incoming streaming of samples into 80-sample OFDM symbols. This can be
achieved using the long preamble following the short preamble.

.. _fig_training:
.. figure:: /images/training.png
    :align: center

    802.11 OFDM Packet Structure (Fig 18-4 in 802.11-2012 Std)

As shown in :numref:`fig_training`, the long preamble duration is 8 |us| (160
samples), and contains two identical long training sequence (LTS), 64 samples
each.  The LTS is known and we can use `cross correlation
<https://en.wikipedia.org/wiki/Cross-correlation>`_ to find it.

The cross validation *score* at sample :math:`i` can be calculated as follows.

.. math:: 
    :label: eq_cross_corr

    Y[i] = \sum_{k=0}^{63}(S[i+k]\overline{H[k]})

where :math:`H` is the 64 sample known LTS in time domain, and can be found in
Table L-6 in :download:`802.11-2012 std </files/802.11-2012.pdf>` (index 96 to
159). A numpy readable file of the LTS (64 samples) can be found :download:`here
</files/lts.txt>`, and can be read like this:

.. code-block:: python

    >>> import numpy as np
    >>> lts = np.loadtxt('lts.txt').view(complex)

.. _fig_lts:
.. figure:: /images/lts.png
    :align: center

    Long Preamble and Cross Correlation Result

To plot :numref:`fig_lts`, load the data file (see :ref:`sec_sample`), then:

.. code-block:: python

    # in scripts/decode.py
    import decode
    import numpy as np
    from matplotlib import pyplot as plt

    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
    ax[0].plot([c.real for c in samples][:500])
    # lts is from the above code snippet
    ax[1].plot([abs(c) for c in np.correlate(samples, lts, mode='valid')][:500], '-ro')
    plt.show()

    

:numref:`fig_lts` shows the long preamble samples and also the result of cross
correlation. We can clearly see two spikes corresponding the two LTS in long
preamble. And the spike width is only 1 sample which shows exactly the beginning
of each sequence. Suppose the sample index if the first spike is :math:`N`, then
the 160 sample long preamble starts at sample :math:`N-32`.

This all seems nice and dandy, but as it comes to Verilog implementation, we
have to make a compromise.

From :eq:`eq_cross_corr` we can see for each sample, we need to perform 64
complex number multiplications, which would consume a lot FPGA resources.
Therefore, we need to reduce the size of cross validation. The idea is to only
use a portion instead of all the LTS samples.

.. _fig_match_size:
.. figure:: /images/match_size.png
    :align: center

    Cross Correlation with Various Size (8, 16, 32, 64)

:numref:`fig_match_size` can be plotted as:

.. code-block:: python

    lp = decode.LONG_PREAMBLE

    fig, ax = plt.subplots(nrows=5, ncols=1, sharex=True)
    ax[0].plot([c.real for c in lp])
    ax[1].plot([abs(c) for c in np.correlate(lp, lts[:8], mode='valid')], '-ro')
    ax[2].plot([abs(c) for c in np.correlate(lp, lts[:16], mode='valid')], '-ro')
    ax[3].plot([abs(c) for c in np.correlate(lp, lts[:32], mode='valid')], '-ro');
    ax[4].plot([abs(c) for c in np.correlate(lp, lts, mode='valid')], '-ro')
    plt.show()

:numref:`fig_match_size` shows the long preamble (160 samples) as well as cross
validation with different size. It can be seen that using the first 16 samples
of LTS is good enough to exhibit two narrow spikes. Therefore, |project| use
cross correlation of first 16 samples of LTS for symbol alignment. To confirm,
:numref:`fig_lts_16` shows the cross correlation of the first 16 samples of LTS
on the actual packet. The two spikes are not as obvious as the ones in
:numref:`fig_lts`, but are still clearly visible.

.. _fig_lts_16:
.. figure:: /images/lts_16.png
    :align: center

    Cross Validation using the First 16 Samples of LTS

To find the two spikes, we keep a record of the max correlation sample for the
first 64 samples (since the first spike is supposed to be at the 32th sample).
Similarly, we also keep a record of the max correlation sample for the second 64
samples. For further eliminate false positives, we also check if the two spike
sample indexes are :math:`64 \pm 1` apart.


FFT
---

Now we have located the start of each OFDM symbol, the next task is to perform
FFT on the last 64 data samples inside each symbol. For this we utilize the
`XFFT core
<https://www.xilinx.com/support/documentation/ip_documentation/xfft_ds260.pdf>`_
generated by Xilinx ISE. Depend on if `short guard interval (SGI)
<https://en.wikipedia.org/wiki/Guard_interval>`_ is used, the first 16 or 8
samples of each OFDM symbol need to be skipped.

But before performing FFT, we need to first apply the frequency offset
correction (see :ref:`freq_offset`). This is achieved via the ``rotate`` module
(see :ref:`rotate`).