parameters.py

# this code is to calculate the reference signal strength(rss) and signal attenuation factor(n)

# importing libraries
import time
import subprocess
import numpy
import math

# declaring an empty list to store average signal strength value at 10 different locations
Y = []
ssid = 'ZTE_5G_qaquFU'  # ssid for which parameters are to be determined


# function to calculate average value of signal strength at a particular location by taking 20 readings of
# signal strength at that location
def average_rssi():
    avg = 0
    for num in range(20):
        subprocess.run(['netsh', 'interface', 'set', 'interface', 'name="Wi-Fi"', 'admin=disabled'])
        subprocess.run(['netsh', 'interface', 'set', 'interface', 'name="Wi-Fi"', 'admin=enabled'])
        time.sleep(4)

        # getting the result of command line command and storing it in a variable
        result = subprocess.check_output(["netsh", "wlan", "show", "network", "mode=Bssid"])

        # converting the result(datatype-bytes) first into string and then storing substrings in list y
        cmd_output_list = list((result.decode('ASCII')).split())

        i_SSID = [index for index, x in enumerate(cmd_output_list) if x == 'SSID']
        i_Network = [index for index, x in enumerate(cmd_output_list) if x == 'Network']
        i_Signal = [index for index, x in enumerate(cmd_output_list) if x == 'Signal']

        for m in range(len(i_SSID)):
            c = cmd_output_list[(i_SSID[m] + 3):i_Network[m]]
            c = tuple(c)
            s = " "
            if s.join(c) == ssid:
                a = cmd_output_list[i_Signal[m] + 2]
                a = a[0: len(a) - 1]
                dbm = ((int(a)) / 2) - 100
                avg += dbm
                break

        time.sleep(20)

    avg /= 20
    return avg


# getting the average value of signal strength at 10 different locations and storing it in the list Y
for i in range(10):
    rssi = average_rssi()
    Y.append(rssi)
    print(Y)
    time.sleep(5)

print(Y)
# converting list Y into a numpy array
Y = numpy.array(Y)

# reshaping Y from dimension (1,10) into (10,1)
Y.reshape((10, 1))

# declaring a numpy array according to distance calculating formula P(d) = P(d0) - 10*n*log(d/d0) where P(d) is
# the signal strength at distance d, d0=1m is the reference distance and P(d0) is received signal strength at d0
Z = numpy.array([[1, -10 * math.log(1.5, 10)],
                 [1, -10 * math.log(2, 10)],
                 [1, -10 * math.log(3, 10)],
                 [1, -10 * math.log(3.5, 10)],
                 [1, -10 * math.log(4, 10)],
                 [1, -10 * math.log(4.5, 10)],
                 [1, -10 * math.log(5, 10)],
                 [1, -10 * math.log(5.5, 10)],
                 [1, -10 * math.log(6, 10)],
                 [1, -10 * math.log(6.5, 10)]])

# X=[[rss], [n]] therefore Y=Z*X and by method of least square algorithm to minimize error we get
# X=(Z_transpose.Z)^(-1).(Z_transpose.Y)
Z_trans = Z.transpose()
A = numpy.linalg.inv(Z_trans.dot(Z))
B = Z_trans.dot(Y)
X = A.dot(B)
rss = X[0][0]
n = X[1][0]
print(rss)
print(n)