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validation.py
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validation.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
from matplotlib import cm
import cycler
import scipy.stats as st
# n = 4
# color = mpl.cm.viridis(np.linspace(0.0, 1.0, n))
def viridis(a, b, color_amount):
color = mpl.cm.viridis(np.linspace(a, b, color_amount))
mpl.rcParams['axes.prop_cycle'] = cycler.cycler('color', color)
def valid_wifi_pcol():
viridis(0.0, 1.0, 3)
coex5g = pd.read_csv('val/coex5g_wifi_63v2.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_wifi_63v2.csv', delimiter=',')
dcf = pd.read_csv('val/DCF_valid_63v2.csv', delimiter=',')
coex5g_pcol = coex5g.groupby(['WiFi'])['PcolWifi'].mean()
matlab_pcol = matlab.groupby(['nWifi'])['pcWifi'].mean()
dcf_pcol = dcf.groupby(['Stations'])['Pcol'].mean()
ax = coex5g_pcol.plot(marker="o", legend=True, ylim=(0, 0.5))
ax = matlab_pcol.plot(marker="x", legend=True, ylim=(0, 0.5), linestyle='--')
ax = dcf_pcol.plot(marker="v", legend=True, ylim=(0, 0.5), linestyle=':')
ax.legend(['5G-Coex-SimPy', 'Matlab', 'DCF-SimPy'])
ax.set_xlabel('Number of Wifi nodes', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/wifi_pcol_63v2.svg')
def valid_wifi():
viridis(0.0, 1.0, 8)
coex5g = pd.read_csv('val/coex5g_wifi_1023.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_wifi_val_1023.csv', delimiter=',')
dcf = pd.read_csv('val/DCF_valid_1023.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['WiFi'])['ChannelOccupancyWiFi'].mean()
coex5g_eff = coex5g.groupby(['WiFi'])['ChannelEfficiencyWiFi'].mean()
matlab_cot = matlab.groupby(['nWifi'])['cotWifi'].mean()
matalb_eff = matlab.groupby(['nWifi'])['effWifi'].mean()
dcf_cot = dcf.groupby(['Stations'])['Occupancy'].mean()
dcf_eff = dcf.groupby(['Stations'])['Efficiency'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = coex5g_eff.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = matlab_cot.plot(marker="x", legend=True, ylim=(0.6, 1), linestyle='--')
ax = matalb_eff.plot(marker="x", legend=True, ylim=(0.6, 1), linestyle='--')
ax = dcf_cot.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax = dcf_eff.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax.legend(['5G-Coex-SimPy cot', '5G-Coex-SimPy eff', 'Matlab cot', 'Matlab eff', 'DCF-SimPY cot', 'DCF-SimPy eff'])
ax.set_xlabel('Number of Wifi nodes', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/wifi_airtime_1023v2.svg')
def valid_nru_pcol():
viridis(0.0, 1.0, 3)
coex5g = pd.read_csv('val/coex5g_gap_63.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_gap_63.csv', delimiter=',')
nru = pd.read_csv('val/nru_gap_63.csv', delimiter=',')
coex5g_pcol = coex5g.groupby(['Gnb'])['PcolNR'].mean()
matlab_pcol = matlab.groupby(['nLAA'])['pcLAA'].mean()
nru_pcol = nru.groupby(['N_sta'])['pc'].mean()
ax = coex5g_pcol.plot(marker="o", legend=True, ylim=(0, 0.1))
ax = matlab_pcol.plot(marker="x", legend=True, ylim=(0, 0.1), linestyle='--')
ax = nru_pcol.plot(marker="v", legend=True, ylim=(0, 0.1), linestyle=':')
ax.legend(['5G-Coex-SimPy', 'Matlab', 'NRU-SimPy'])
ax.set_xlabel('Number of gNBs', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/gap_pcol_63.svg')
def valid_nru():
viridis(0.0, 1.0, 7)
coex5g = pd.read_csv('val/coex5g_rs_1023.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_rs_1023.csv', delimiter=',')
nru = pd.read_csv('val/nru_rs_1023.csv', delimiter=',')
coex5g2 = pd.read_csv('val/coex5g_rs_1023v2.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
coex5g_eff = coex5g.groupby(['Gnb'])['ChannelEfficiencyNR'].mean()
coex5g_cot2 = coex5g2.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
coex5g_eff2 = coex5g2.groupby(['Gnb'])['ChannelEfficiencyNR'].mean()
matlab_cot = matlab.groupby(['nLAA'])['cotLAA'].mean()
matalb_eff = matlab.groupby(['nLAA'])['effLAA'].mean()
#nru_cot = nru.groupby(['N_sta'])['occ'].mean()
nru_eff = nru.groupby(['N_sta'])['eff'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = coex5g_eff.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = matlab_cot.plot(marker="x", legend=True, ylim=(0.6, 1), linestyle='--')
ax = matalb_eff.plot(marker="x", legend=True, ylim=(0.6, 1), linestyle='--')
#ax = nru_cot.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax = nru_eff.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax = coex5g_cot2.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = coex5g_eff2.plot(marker="o", legend=True, ylim=(0.6, 1))
ax.legend(['5G-Coex-SimPy cot', '5G-Coex-SimPy eff', 'Matlab cot', 'Matlab eff', 'NRU-SimPy eff', '5G-Coex-SimPy2 cot', '5G-Coex-SimPy2 eff'])
ax.set_xlabel('Number of gNBs', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/rs_airtime_1023v2.svg')
def valid_nru_pcol_gap():
viridis(0.0, 1.0, 3)
coex5g = pd.read_csv('val/coex5g_gap_63v2.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_gap_63.csv', delimiter=',')
nru = pd.read_csv('val/nru_gap_63.csv', delimiter=',')
coex5g_pcol = coex5g.groupby(['Gnb'])['PcolNR'].mean()
matlab_pcol = matlab.groupby(['nNR'])['pcNR'].mean()
nru_pcol = nru.groupby(['N_sta'])['pc'].mean()
ax = coex5g_pcol.plot(marker="o", legend=True, ylim=(0, 0.1))
ax = matlab_pcol.plot(marker="x", legend=True, ylim=(0, 0.1), linestyle='--')
ax = nru_pcol.plot(marker="v", legend=True, ylim=(0, 0.1), linestyle=':')
ax.legend(['5G-Coex-SimPy', 'Matlab', 'NRU-SimPy'])
ax.set_xlabel('Number of gNBs', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/gap_pcol_63v2.svg')
def valid_nru_gap():
viridis(0.0, 1.0, 3)
coex5g = pd.read_csv('val/coex5g_gap_1023v2.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_gap_1023.csv', delimiter=',')
nru = pd.read_csv('val/nru_gap_1023.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
matalb_eff = matlab.groupby(['nNR'])['effNR'].mean()
nru_eff = nru.groupby(['N_sta'])['eff'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0.6, 1))
ax = matalb_eff.plot(marker="x", legend=True, ylim=(0.6, 1), linestyle='--')
#ax = nru_cot.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax = nru_eff.plot(marker="v", legend=True, ylim=(0.6, 1), linestyle=':')
ax.legend(['5G-Coex-SimPy cot', 'Matlab cot', 'NRU-SimPy cot'])
ax.set_xlabel('Number of gNBs', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/gap_airtime_1023v2.svg')
def calc():
alpha = 0.05
wifi = pd.read_csv('val/coex5g_gap_1023.csv', delimiter=',')
std = wifi.groupby('Gnb').std().loc[:, 'ChannelOccupancyNR']
var = wifi.groupby('Gnb').var().loc[:, 'ChannelOccupancyNR']
n = wifi.groupby('Gnb').count().loc[:, 'ChannelOccupancyNR']
# Calculate confidence intervals
yerr = std / np.sqrt(n) * st.t.ppf(1 - alpha / 2, n - 1)
print(std)
print(var)
print(yerr)
def coex():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex5g_wifi_gap_1023.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_wifi_gap_1023.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
matalb_cot = matlab.groupby(['nNR'])['cotNR'].mean()
coex5g_cot2 = coex5g.groupby(['WiFi'])['ChannelOccupancyWiFi'].mean()
matlab_cot2 = matlab.groupby(['nWifi'])['cotWifi'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_cot2.plot(marker="o", legend=True, ylim=(0, 1))
ax = matalb_cot.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matlab_cot2.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot', 'Matlab NR-U cot', 'Matlab Wi-Fi cot'])
#ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex_airtime_1023v2.svg')
def coex2():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex5g_wifi_gap_1023.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_wifi_gap_1023.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['Gnb'])['ChannelEfficiencyNR'].mean()
matalb_cot = matlab.groupby(['nNR'])['effNR'].mean()
coex5g_cot2 = coex5g.groupby(['WiFi'])['ChannelEfficiencyWiFi'].mean()
matlab_cot2 = matlab.groupby(['nWifi'])['effWifi'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_cot2.plot(marker="o", legend=True, ylim=(0, 1))
ax = matalb_cot.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matlab_cot2.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U eff', '5G-Coex-SimPy Wi-Fi eff', 'Matlab NR-U eff', 'Matlab Wi-Fi eff'])
#ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex_airtime_eff_1023v2.svg')
def coex_rs():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex5g_wifi_rs_63.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_wifi_rs_63.csv', delimiter=',')
coex5g_cot = coex5g.groupby(['Gnb'])['ChannelEfficiencyNR'].mean()
matalb_cot = matlab.groupby(['nLAA'])['effLAA'].mean()
coex5g_cot2 = coex5g.groupby(['WiFi'])['ChannelEfficiencyWiFi'].mean()
matlab_cot2 = matlab.groupby(['nWifi'])['effWifi'].mean()
ax = coex5g_cot.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_cot2.plot(marker="o", legend=True, ylim=(0, 1))
ax = matalb_cot.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matlab_cot2.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U eff', '5G-Coex-SimPy Wi-Fi eff', 'Matlab NR-U eff', 'Matlab Wi-Fi eff'])
#ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex_airtime_rs_63.svg')
def coex_pcol():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex5g_coex_1023.csv', delimiter=',')
matlab = pd.read_csv('val/matlab_coexGap_1023.csv', delimiter=',')
coex5g_pcol = coex5g.groupby(['Gnb'])['PcolNR'].mean()
matlab_pcol = matlab.groupby(['nNR'])['pcNR'].mean()
coex5g_pcol2 = coex5g.groupby(['WiFi'])['PcolWifi'].mean()
matlab_pcol2 = matlab.groupby(['nWifi'])['pcWifi'].mean()
ax = coex5g_pcol.plot(marker="o", legend=True, ylim=(0, 0.5))
ax = coex5g_pcol2.plot(marker="o", legend=True, ylim=(0, 0.5))
ax = matlab_pcol.plot(marker="x", legend=True, ylim=(0, 0.5), linestyle='--')
ax = matlab_pcol2.plot(marker="x", legend=True, ylim=(0, 0.5), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U','5G-Coex-SimPy Wi-Fi', 'Matlab NR-U', 'Matlab Wi-Fi'])
# ax.legend(['5G-Coex-SimPy NR-U', '5G-Coex-SimPy Wi-Fi'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex_pcol_1023.svg')
def coexistence_rs():
viridis(0.0, 1.0, 8)
coex5g = pd.read_csv('val/coex/coex5g_coexistence_rs_63v4.csv', delimiter=',')
#coex5g = pd.read_csv('val/coex/coex5g_coexistence_rs_63.csv', delimiter=',')
matlab = pd.read_csv('val/coex/matlab_coexistence_rs_63_6ms.csv', delimiter=',')
coex5g_eff_nru = coex5g.groupby(['Gnb'])['ChannelEfficiencyNR'].mean()
matalb_eff_nru = matlab.groupby(['nLAA'])['effLAA'].mean()
coex5g_eff_wifi = coex5g.groupby(['WiFi'])['ChannelEfficiencyWiFi'].mean()
matlab_eff_wifi = matlab.groupby(['nWifi'])['effWifi'].mean()
coex5g_cot_nru = coex5g.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
matalb_cot_nru = matlab.groupby(['nLAA'])['cotLAA'].mean()
coex5g_cot_wifi = coex5g.groupby(['WiFi'])['ChannelOccupancyWiFi'].mean()
matlab_cot_wifi = matlab.groupby(['nWifi'])['cotWifi'].mean()
ax = coex5g_cot_nru.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_cot_wifi.plot(marker="o", legend=True, ylim=(0, 1), linestyle='--')
ax = coex5g_eff_nru.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_eff_wifi.plot(marker="o", legend=True, ylim=(0, 1), linestyle='--')
ax = matalb_cot_nru.plot(marker="x", legend=True, ylim=(0, 1))
ax = matlab_cot_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matalb_eff_nru.plot(marker="x", legend=True, ylim=(0, 1))
ax = matlab_eff_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot','5G-Coex-SimPy NR-U eff', '5G-Coex-SimPy Wi-Fi eff', 'Matlab NR-U cot', 'Matlab Wi-Fi cot', 'Matlab NR-U eff', 'Matlab Wi-Fi eff'])
# ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex/coexistence_rs_63_6ms.svg')
def coexistence_rs_pcol():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex/coex5g_coexistence_rs_63debuged.csv', delimiter=',')
matlab = pd.read_csv('val/coex/matlab_coexistence_rs_63.csv', delimiter=',')
coex5g_pc_nru = coex5g.groupby(['Gnb'])['PcolNR'].mean()
matalb_pc_nru = matlab.groupby(['nLAA'])['pcLAA'].mean()
coex5g_pc_wifi = coex5g.groupby(['WiFi'])['PcolWifi'].mean()
matlab_pc_wifi = matlab.groupby(['nWifi'])['pcWifi'].mean()
ax = coex5g_pc_nru.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_pc_wifi.plot(marker="o", legend=True, ylim=(0, 1))
ax = matalb_pc_nru.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matlab_pc_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U pcol', '5G-Coex-SimPy Wi-Fi pcol',
'Matlab NR-U pcol', 'Matlab Wi-Fi pcol'])
# ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex/coexistence_rs_63_pcol_ok.svg')
def coexistence_gap():
viridis(0.0, 1.0, 6)
coex5g = pd.read_csv('val/coex/coex5g_coexistence_gap_63.csv', delimiter=',')
matlab = pd.read_csv('val/coex/matlab_coexistence_gap_63v2.csv', delimiter=',')
coex5g_eff_wifi = coex5g.groupby(['WiFi'])['ChannelEfficiencyWiFi'].mean()
matlab_eff_wifi = matlab.groupby(['nWifi'])['effWifi'].mean()
coex5g_cot_nru = coex5g.groupby(['Gnb'])['ChannelOccupancyNR'].mean()
matalb_cot_nru = matlab.groupby(['nNR'])['cotNR'].mean()
coex5g_cot_wifi = coex5g.groupby(['WiFi'])['ChannelOccupancyWiFi'].mean()
matlab_cot_wifi = matlab.groupby(['nWifi'])['cotWifi'].mean()
ax = coex5g_cot_nru.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_cot_wifi.plot(marker="o", legend=True, ylim=(0, 1), linestyle='--')
ax = coex5g_eff_wifi.plot(marker="o", legend=True, ylim=(0, 1), linestyle='--')
ax = matalb_cot_nru.plot(marker="x", legend=True, ylim=(0, 1),)
ax = matlab_cot_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax = matlab_eff_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot', '5G-Coex-SimPy Wi-Fi eff',
'Matlab NR-U cot', 'Matlab Wi-Fi cot', 'Matlab Wi-Fi eff'])
# ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Channel occupation time', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex/coexistence_gap_63.svg')
def coexistence_gap_pcol():
viridis(0.0, 1.0, 4)
coex5g = pd.read_csv('val/coex/coex5g_coexistence_gap_1023.csv', delimiter=',')
matlab = pd.read_csv('val/coex/matlab_coexistence_gap_1023.csv', delimiter=',')
coex5g_pc_nru = coex5g.groupby(['Gnb'])['PcolNR'].mean()
matalb_pc_nru = matlab.groupby(['nNR'])['pcNR'].mean()
coex5g_pc_wifi = coex5g.groupby(['WiFi'])['PcolWifi'].mean()
matlab_pc_wifi = matlab.groupby(['nWifi'])['pcWifi'].mean()
ax = coex5g_pc_nru.plot(marker="o", legend=True, ylim=(0, 1))
ax = coex5g_pc_wifi.plot(marker="o", legend=True, ylim=(0, 1), linestyle='--')
ax = matalb_pc_nru.plot(marker="x", legend=True, ylim=(0, 1))
ax = matlab_pc_wifi.plot(marker="x", legend=True, ylim=(0, 1), linestyle='--')
ax.legend(['5G-Coex-SimPy NR-U pcol', '5G-Coex-SimPy Wi-Fi pcol',
'Matlab NR-U pcol', 'Matlab Wi-Fi pcol'])
# ax.legend(['5G-Coex-SimPy NR-U cot', '5G-Coex-SimPy Wi-Fi cot'])
ax.set_xlabel('Number of gNBs/stations', fontsize=14)
ax.set_ylabel('Collision probability', fontsize=14)
plt.tight_layout()
plt.savefig('val/coex/coexistence_gap_1023_pcol.svg')
if __name__ == "__main__":
#valid_wifi()
#valid_nru()
# calc()
#coex_rs()
coexistence_rs()