BPSK QPSK 16QAM 64QAM modulation python code script

This page covers BPSK, QPSK, 16QAM and 64QAM modulation python code script.

Introduction: Digital modulation techniques such as QPSK, 16QAM and 64QAM helps in achieving bandwidth efficiency by mapping multiple bits on single carrier. BPSK is the most robust modulation technique used for long distance communication and pilot subcarrier mapping.

Data mapping or modulation Python script

Following python code can be used to map digital binary data as per different modulation constellation points such as BPSK, QPSK, 16QAM and 64QAM.

# Binary data generator (1's and 0's as per user defined length)

import random

import numpy as np

import numpy

import matplotlib.pyplot as plt

# Python code to generate binary stream of data

input_data = ""

len1 = 1000  # length of input data

for i in range(len1):

  temp1 = str(random.randint(0, 1))

  input_data += temp1

print(input_data)

input_data1 = [int(i) for i in input_data]  # convert string to list

print(input_data1)

# Setting up parameters such as modulation type, number of bits per carrier (nc) and norm_factor

modulation_type = int(input("Enter modulation type (0: BPSK, 1: QPSK, 2:16QAM, 3: 64QAM: "))

print(modulation_type)

norm_factor = [1.0, 0.7071, 0.3162, 0.1543]

nc = [1, 2, 4, 6]

k = norm_factor[modulation_type]

print(k)

mode = nc[modulation_type]

print(mode)

M0 = []

match mode:

case 1:

    M0 = np.array([1, -1])

    i = len(M0)

    b = np.zeros(i)

    for i in M0:

      M0 = numpy.multiply(M0, k)

    plt.plot(M0.real, M0.imag, "g*")

    plt.title("Figure-1 : BPSK constellation")

    plt.show()

case 2:

    M0 = np.array([1. + 1.j, -1. + 1.j, 1. - 1.j, -1. - 1.j])

    i = len(M0)

    b = np.zeros(i)

    for i in M0:

      N0 = numpy.multiply(M0, k)

    plt.plot(M0.real, M0.imag, "g*")

    plt.title("Figure-2 : QPSK constellation")

    plt.show()

case 4:

    M0 = np.array([1. + 1.j, 1. + 3.j, 1. - 1.j, 1. - 3.j, 3. + 1.j, 3. + 3.j,

    3. - 1.j, 3. - 3.j, -1. + 1.j, -1. + 3.j, -1. - 1.j, -1. - 3.j, -3. + 1.j,

    -3. + 3.j, -3. - 1.j, -3. - 3.j])

    i = len(M0)

    b = np.zeros(i)

    for i in M0:

      M0 = numpy.multiply(M0, k)

    plt.plot(M0.real, M0.imag, "g*")

    plt.title("Figure-3 : 16QAM constellation")

    plt.show()

case 6:

    M0 = np.array([3 + 3j, 3 + 1j, 3 + 5j, 3 + 7j, 3 - 3j, 3 - 1j, 3 - 5j,

    3 - 7j, 1 + 3j, 1 + 1j, 1 + 5j, 1 + 7j, 1 - 3j, 1 - 1j, 1 - 5j, 1 - 7j,

    5 + 3j, 5 + 1j, 5 + 5j, 5 + 7j, 5 - 3j, 5 - 1j, 5 - 5j, 5 - 7j, 7 + 3j,

    7 + 1j, 7 + 5j, 7 + 7j, 7 - 3j, 7 - 1j, 7 - 5j, 7 - 7j, -3 + 3j, 3 + 1j,

    -3 + 5j, -3 + 7j, -3 - 3j, -3 - 1j, -3 - 5j, -3 - 7j, -1 + 3j, -1 + 1j,

    -1 + 5j, -1 + 7j, -1 - 3j, -1 - 1j, -1 - 5j, -1 - 7j, -5 + 3j, -5 + 1j,

    -5 + 5j, -5 + 7j, -5 - 3j, -5 - 1j, -5 - 5j, -5 - 7j, -7 + 3j, -7 + 1j,

    -7 + 5j, -7 + 7j, -7 - 3j, -7 - 1j, -7 - 5j, -7 - 7j])

    i = len(M0)

    b = np.zeros(i)

    for i in M0:

      M0 = numpy.multiply(M0, k)

    plt.plot(M0.real, M0.imag, "g*")

    plt.title("Figure-4 : 64QAM constellation")

    plt.show()

# checking length of data to add padding zero at the end if required

remainder = len1 % mode

print(remainder)

if remainder != 0:

  remainder = mode-remainder

 input_data1 = np.pad(input_data1, (0, remainder))

len1 = len(input_data1)

len2 = int(len1/mode)

input_data_r = np.reshape(input_data1, (len2, mode))

print(input_data_r)



s1 = input_data

s2 = []

chunks = [s1[i:i+mode] for i in range(0, len(s1), mode)]

for piece in chunks:

  temp = int(piece, 2)

  print(temp, piece)

  s2.append(temp)



map_out = []

for value in s2:

  map_out.append(M0[value])

  print(M0[value])