BER-SNR curve in Rayleigh channel using BPSK









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I am trying to generate BER-SNR curve in Rayleigh channel using BPSK modulation. Can anyone help me to find what is wrong with the python code? The theoretical BER differs from the simulated BER. The red dotted line is theoretical and the blue one is simulated.



BER-SNR Curve of BPSK



import math as mt
import numpy as np
import matplotlib.pyplot as plt
import numpy.random as nr
from scipy.linalg import toeplitz

# ------------Simulation parameters------------------------
N = 500 #No.of symbols
H_n = 10 #No.of channel elements
h_ray = np.zeros((H_n, 1), dtype = complex)

# ---------------------------------------------------------
EbN0dB = np.array(range(0, 11))
itr = len(EbN0dB)
ber = [None] * itr
theoreticalBER = [None] * itr

# -----------------------------Transmitter----------------------------
s = 2 * (nr.rand(N, 1) >= 0.5) - 1 # BPSK

# ------------------------------Channel-------------------------------
h_ray = (1/mt.sqrt(2)) * (nr.randn(H_n, 1) + 1j * nr.randn(H_n, 1))
# Rayleigh fading channel
z = np.zeros((N-1,1))
c = np.concatenate((h_ray, z), axis=0)
r = np.concatenate(([h_ray[0]], np.zeros((1, N-1))), axis=1)
h_ray_y=toeplitz(c, r) # Toeplitz matrix instead of convolution

y_ray = np.dot(h_ray_y, s)

for n in range(itr):
EbN0dB_n = EbN0dB[n]
EbN0 = 10.0 ** (-EbN0dB_n/ 20.0)
noise = 1/mt.sqrt(2) * (nr.randn(N+H_n-1) + 1j * nr.randn(N+H_n-1))
r_ray = y_ray + EbN0 * noise
# --------------------------Receiver------------------------------
r_t = np.dot(np.dot(np.linalg.pinv(np.dot(h_ray_y.T, h_ray_y)), h_ray_y.T), r_ray) # (H.T * H)^(-1)*H.T*received
r_d = 2 * (np.real(r_t) >= 0) - 1
errors = (s != r_d).sum()
ber[n] = 1.0 * errors / N

EbN0_th = 10.0 ** (EbN0dB_n / 10.0)
theoreticalBER[n] = 0.5 *(1 - mt.sqrt(EbN0_th/ (1 + EbN0_th)))
ber=np.asanyarray(ber)
#-------------------------------------------------------------------------
plt.plot(EbN0dB, np.log10(ber), '--bo')
plt.plot(EbN0dB, np.log10(theoreticalBER), 'ro')
plt.xlabel('EbN0(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BPSK - Rayleigh')
plt.show()









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  • 3




    What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
    – jonrsharpe
    Nov 11 at 15:21










  • The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
    – POOJA P ANIL
    Nov 11 at 15:26















up vote
0
down vote

favorite












I am trying to generate BER-SNR curve in Rayleigh channel using BPSK modulation. Can anyone help me to find what is wrong with the python code? The theoretical BER differs from the simulated BER. The red dotted line is theoretical and the blue one is simulated.



BER-SNR Curve of BPSK



import math as mt
import numpy as np
import matplotlib.pyplot as plt
import numpy.random as nr
from scipy.linalg import toeplitz

# ------------Simulation parameters------------------------
N = 500 #No.of symbols
H_n = 10 #No.of channel elements
h_ray = np.zeros((H_n, 1), dtype = complex)

# ---------------------------------------------------------
EbN0dB = np.array(range(0, 11))
itr = len(EbN0dB)
ber = [None] * itr
theoreticalBER = [None] * itr

# -----------------------------Transmitter----------------------------
s = 2 * (nr.rand(N, 1) >= 0.5) - 1 # BPSK

# ------------------------------Channel-------------------------------
h_ray = (1/mt.sqrt(2)) * (nr.randn(H_n, 1) + 1j * nr.randn(H_n, 1))
# Rayleigh fading channel
z = np.zeros((N-1,1))
c = np.concatenate((h_ray, z), axis=0)
r = np.concatenate(([h_ray[0]], np.zeros((1, N-1))), axis=1)
h_ray_y=toeplitz(c, r) # Toeplitz matrix instead of convolution

y_ray = np.dot(h_ray_y, s)

for n in range(itr):
EbN0dB_n = EbN0dB[n]
EbN0 = 10.0 ** (-EbN0dB_n/ 20.0)
noise = 1/mt.sqrt(2) * (nr.randn(N+H_n-1) + 1j * nr.randn(N+H_n-1))
r_ray = y_ray + EbN0 * noise
# --------------------------Receiver------------------------------
r_t = np.dot(np.dot(np.linalg.pinv(np.dot(h_ray_y.T, h_ray_y)), h_ray_y.T), r_ray) # (H.T * H)^(-1)*H.T*received
r_d = 2 * (np.real(r_t) >= 0) - 1
errors = (s != r_d).sum()
ber[n] = 1.0 * errors / N

EbN0_th = 10.0 ** (EbN0dB_n / 10.0)
theoreticalBER[n] = 0.5 *(1 - mt.sqrt(EbN0_th/ (1 + EbN0_th)))
ber=np.asanyarray(ber)
#-------------------------------------------------------------------------
plt.plot(EbN0dB, np.log10(ber), '--bo')
plt.plot(EbN0dB, np.log10(theoreticalBER), 'ro')
plt.xlabel('EbN0(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BPSK - Rayleigh')
plt.show()









share|improve this question



















  • 3




    What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
    – jonrsharpe
    Nov 11 at 15:21










  • The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
    – POOJA P ANIL
    Nov 11 at 15:26













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I am trying to generate BER-SNR curve in Rayleigh channel using BPSK modulation. Can anyone help me to find what is wrong with the python code? The theoretical BER differs from the simulated BER. The red dotted line is theoretical and the blue one is simulated.



BER-SNR Curve of BPSK



import math as mt
import numpy as np
import matplotlib.pyplot as plt
import numpy.random as nr
from scipy.linalg import toeplitz

# ------------Simulation parameters------------------------
N = 500 #No.of symbols
H_n = 10 #No.of channel elements
h_ray = np.zeros((H_n, 1), dtype = complex)

# ---------------------------------------------------------
EbN0dB = np.array(range(0, 11))
itr = len(EbN0dB)
ber = [None] * itr
theoreticalBER = [None] * itr

# -----------------------------Transmitter----------------------------
s = 2 * (nr.rand(N, 1) >= 0.5) - 1 # BPSK

# ------------------------------Channel-------------------------------
h_ray = (1/mt.sqrt(2)) * (nr.randn(H_n, 1) + 1j * nr.randn(H_n, 1))
# Rayleigh fading channel
z = np.zeros((N-1,1))
c = np.concatenate((h_ray, z), axis=0)
r = np.concatenate(([h_ray[0]], np.zeros((1, N-1))), axis=1)
h_ray_y=toeplitz(c, r) # Toeplitz matrix instead of convolution

y_ray = np.dot(h_ray_y, s)

for n in range(itr):
EbN0dB_n = EbN0dB[n]
EbN0 = 10.0 ** (-EbN0dB_n/ 20.0)
noise = 1/mt.sqrt(2) * (nr.randn(N+H_n-1) + 1j * nr.randn(N+H_n-1))
r_ray = y_ray + EbN0 * noise
# --------------------------Receiver------------------------------
r_t = np.dot(np.dot(np.linalg.pinv(np.dot(h_ray_y.T, h_ray_y)), h_ray_y.T), r_ray) # (H.T * H)^(-1)*H.T*received
r_d = 2 * (np.real(r_t) >= 0) - 1
errors = (s != r_d).sum()
ber[n] = 1.0 * errors / N

EbN0_th = 10.0 ** (EbN0dB_n / 10.0)
theoreticalBER[n] = 0.5 *(1 - mt.sqrt(EbN0_th/ (1 + EbN0_th)))
ber=np.asanyarray(ber)
#-------------------------------------------------------------------------
plt.plot(EbN0dB, np.log10(ber), '--bo')
plt.plot(EbN0dB, np.log10(theoreticalBER), 'ro')
plt.xlabel('EbN0(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BPSK - Rayleigh')
plt.show()









share|improve this question















I am trying to generate BER-SNR curve in Rayleigh channel using BPSK modulation. Can anyone help me to find what is wrong with the python code? The theoretical BER differs from the simulated BER. The red dotted line is theoretical and the blue one is simulated.



BER-SNR Curve of BPSK



import math as mt
import numpy as np
import matplotlib.pyplot as plt
import numpy.random as nr
from scipy.linalg import toeplitz

# ------------Simulation parameters------------------------
N = 500 #No.of symbols
H_n = 10 #No.of channel elements
h_ray = np.zeros((H_n, 1), dtype = complex)

# ---------------------------------------------------------
EbN0dB = np.array(range(0, 11))
itr = len(EbN0dB)
ber = [None] * itr
theoreticalBER = [None] * itr

# -----------------------------Transmitter----------------------------
s = 2 * (nr.rand(N, 1) >= 0.5) - 1 # BPSK

# ------------------------------Channel-------------------------------
h_ray = (1/mt.sqrt(2)) * (nr.randn(H_n, 1) + 1j * nr.randn(H_n, 1))
# Rayleigh fading channel
z = np.zeros((N-1,1))
c = np.concatenate((h_ray, z), axis=0)
r = np.concatenate(([h_ray[0]], np.zeros((1, N-1))), axis=1)
h_ray_y=toeplitz(c, r) # Toeplitz matrix instead of convolution

y_ray = np.dot(h_ray_y, s)

for n in range(itr):
EbN0dB_n = EbN0dB[n]
EbN0 = 10.0 ** (-EbN0dB_n/ 20.0)
noise = 1/mt.sqrt(2) * (nr.randn(N+H_n-1) + 1j * nr.randn(N+H_n-1))
r_ray = y_ray + EbN0 * noise
# --------------------------Receiver------------------------------
r_t = np.dot(np.dot(np.linalg.pinv(np.dot(h_ray_y.T, h_ray_y)), h_ray_y.T), r_ray) # (H.T * H)^(-1)*H.T*received
r_d = 2 * (np.real(r_t) >= 0) - 1
errors = (s != r_d).sum()
ber[n] = 1.0 * errors / N

EbN0_th = 10.0 ** (EbN0dB_n / 10.0)
theoreticalBER[n] = 0.5 *(1 - mt.sqrt(EbN0_th/ (1 + EbN0_th)))
ber=np.asanyarray(ber)
#-------------------------------------------------------------------------
plt.plot(EbN0dB, np.log10(ber), '--bo')
plt.plot(EbN0dB, np.log10(theoreticalBER), 'ro')
plt.xlabel('EbN0(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BPSK - Rayleigh')
plt.show()






python scipy






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edited Nov 11 at 15:32









jonrsharpe

76.3k10100206




76.3k10100206










asked Nov 11 at 15:18









POOJA P ANIL

11




11







  • 3




    What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
    – jonrsharpe
    Nov 11 at 15:21










  • The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
    – POOJA P ANIL
    Nov 11 at 15:26













  • 3




    What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
    – jonrsharpe
    Nov 11 at 15:21










  • The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
    – POOJA P ANIL
    Nov 11 at 15:26








3




3




What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
– jonrsharpe
Nov 11 at 15:21




What makes you think something is wrong? Give a Minimal, Complete, and Verifiable example.
– jonrsharpe
Nov 11 at 15:21












The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
– POOJA P ANIL
Nov 11 at 15:26





The simulated BER differs from the theoretical BER. The screenshot of the graphs will be attached to the question shortly. Thank you.
– POOJA P ANIL
Nov 11 at 15:26


















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