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@francescamen
francescamen committed Apr 14, 2022
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132 changes: 5 additions & 127 deletions .gitignore
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# Byte-compiled / optimized / DLL files

input_files/*
.idea/*
*.pdf
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
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dist/
downloads/
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lib64/
parts/
sdist/
var/
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share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
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# Installer logs
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# Translations
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# pyenv
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# pipenv
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

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__pypackages__/

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*.sage.py

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*/
126 changes: 126 additions & 0 deletions Python_code/CSI_doppler_computation.py
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"""
Copyright (C) 2022 Francesca Meneghello
contact: meneghello@dei.unipd.it
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""

import argparse
import numpy as np
import scipy.io as sio
import math as mt
from scipy.fftpack import fft
from scipy.fftpack import fftshift
from scipy.signal.windows import hann
import pickle
import os


if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('dir', help='Directory of data')
parser.add_argument('subdirs', help='Sub-directories')
parser.add_argument('dir_doppler', help='Directory to save the Doppler data')
parser.add_argument('start', help='Start processing', type=int)
parser.add_argument('end', help='End processing (samples from the end)', type=int)
parser.add_argument('sample_length', help='Number of packet in a sample', type=int)
parser.add_argument('sliding', help='Number of packet for sliding operations', type=int)
parser.add_argument('noise_level', help='Level for the noise to be removed', type=float)
parser.add_argument('--bandwidth', help='Bandwidth in [MHz] to select the subcarriers, can be 20, 40, 80 '
'(default 80)', default=80, required=False, type=int)
parser.add_argument('--sub_band', help='Sub_band idx in [1, 2, 3, 4] for 20 MHz, [1, 2] for 40 MHz '
'(default 1)', default=1, required=False, type=int)
args = parser.parse_args()

num_symbols = args.sample_length # 51
middle = int(mt.floor(num_symbols / 2))

Tc = 6e-3
fc = 5e9
v_light = 3e8
delta_v = round(v_light / (Tc * fc * num_symbols), 3)

sliding = args.sliding
noise_lev = args.noise_level
bandwidth = args.bandwidth
sub_band = args.sub_band

list_subdir = args.subdirs

for subdir in list_subdir.split(','):
path_doppler = args.dir_doppler + subdir
if not os.path.exists(path_doppler):
os.mkdir(path_doppler)

exp_dir = args.dir + subdir + '/'

names = []
all_files = os.listdir(exp_dir)
for i in range(len(all_files)):
names.append(all_files[i][:-4])

for name in names:
path_doppler_name = path_doppler + '/' + name + '.txt'
if os.path.exists(path_doppler_name):
continue

print(path_doppler_name)
name_file = exp_dir + name + '.mat'
mdic = sio.loadmat(name_file)
csi_matrix_processed = mdic['csi_matrix_processed']

csi_matrix_processed = csi_matrix_processed[args.start:-args.end, :, :]

csi_matrix_processed[:, :, 0] = csi_matrix_processed[:, :, 0] / np.mean(csi_matrix_processed[:, :, 0],
axis=1, keepdims=True)

csi_matrix_complete = csi_matrix_processed[:, :, 0]*np.exp(1j*csi_matrix_processed[:, :, 1])

if bandwidth == 40:
if sub_band == 1:
selected_subcarriers_idxs = np.arange(0, 117, 1)
elif sub_band == 2:
selected_subcarriers_idxs = np.arange(128, 245, 1)
num_selected_subcarriers = selected_subcarriers_idxs.shape[0]
csi_matrix_complete = csi_matrix_complete[:, selected_subcarriers_idxs]
elif bandwidth == 20:
if sub_band == 1:
selected_subcarriers_idxs = np.arange(0, 57, 1)
elif sub_band == 2:
selected_subcarriers_idxs = np.arange(60, 117, 1)
elif sub_band == 3:
selected_subcarriers_idxs = np.arange(128, 185, 1)
elif sub_band == 4:
selected_subcarriers_idxs = np.arange(188, 245, 1)
num_selected_subcarriers = selected_subcarriers_idxs.shape[0]
csi_matrix_complete = csi_matrix_complete[:, selected_subcarriers_idxs]

csi_d_profile_list = []
for i in range(0, csi_matrix_complete.shape[0]-num_symbols, sliding):
csi_matrix_cut = csi_matrix_complete[i:i+num_symbols, :]
csi_matrix_cut = np.nan_to_num(csi_matrix_cut)

hann_window = np.expand_dims(hann(num_symbols), axis=-1)
csi_matrix_wind = np.multiply(csi_matrix_cut, hann_window)
csi_doppler_prof = fft(csi_matrix_wind, n=100, axis=0)
csi_doppler_prof = fftshift(csi_doppler_prof, axes=0)

csi_d_map = np.abs(csi_doppler_prof * np.conj(csi_doppler_prof))
csi_d_map = np.sum(csi_d_map, axis=1)
csi_d_profile_list.append(csi_d_map)
csi_d_profile_array = np.asarray(csi_d_profile_list)
csi_d_profile_array_max = np.max(csi_d_profile_array, axis=1, keepdims=True)
csi_d_profile_array = csi_d_profile_array/csi_d_profile_array_max
csi_d_profile_array[csi_d_profile_array < mt.pow(10, noise_lev)] = mt.pow(10, noise_lev)

with open(path_doppler_name, "wb") as fp: # Pickling
pickle.dump(csi_d_profile_array, fp)
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