Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Puncturing convolutional code at rate 1/2 to get rate 3/4 #98

Open
mpaparna opened this issue Jan 23, 2021 · 2 comments
Open

Puncturing convolutional code at rate 1/2 to get rate 3/4 #98

mpaparna opened this issue Jan 23, 2021 · 2 comments

Comments

@mpaparna
Copy link

mpaparna commented Jan 23, 2021

Hi,
I am trying to execute the readme.md example for convolutional coding at code rate 3/4. The generator matrix [5, 7] for R=1/2 is used and punctured with [[1,0,1],[1,1,0]] to get R=3/4. But the BER values of viterbi decoded outputs are higher than that of uncoded output. Also the size of the coded bits is as per 1/2 rate and not 3/4. Is it an issue with the 'conv_encode' function or am I missing any steps in between?

Also is there any method to get the coding rate 3/4 using generator matrix?

import numpy as np
import commpy.channelcoding.convcode as cc
import commpy.modulation as modulation

def BER_calc(a, b):
  num_ber = np.sum(np.abs(a - b))
  ber = np.mean(np.abs(a - b))
  return int(num_ber), ber

N = 100 #number of symbols per the frame
message_bits = np.random.randint(0, 2, N) # message

M = 2 # modulation order (BPSK)
k = np.log2(M) #number of bit per modulation symbol
modem = modulation.PSKModem(M) # M-PSK modem initialization

generator_matrix = np.array([[5, 7]]) # generator branches
trellis = cc.Trellis(np.array([2]), generator_matrix) # Trellis structure
punctureMatrix=np.array([[1,0,1],[1,1,0]])

rate = 3/4 # code rate
L = 7 # constraint length
m = np.array([L-1]) # number of delay elements

tb_depth = 5*(m.sum() + 1) # traceback depth

EbNo = 5 # energy per bit to noise power spectral density ratio (in dB)
snrdB = EbNo + 10*np.log10(k*rate) # Signal-to-Noise ratio (in dB)
noiseVar = 10**(-snrdB/10) # noise variance (power)

N_c = 10 # number of trials

BER_soft = np.zeros(N_c)
BER_hard = np.zeros(N_c)
BER_uncoded = np.zeros(N_c)

for cntr in range(N_c):
  
  message_bits = np.random.randint(0, 2, N) # message
  coded_bits = cc.conv_encode(message_bits, trellis,puncture_matrix=punctureMatrix) # encoding
  modulated = modem.modulate(coded_bits) # modulation
  modulated_uncoded = modem.modulate(message_bits) # modulation (uncoded case)

  Es = np.mean(np.abs(modulated)**2) # symbol energy
  No = Es/((10**(EbNo/10))*np.log2(M)) # noise spectrum density

  noisy = modulated + np.sqrt(No/2)*\
      (np.random.randn(modulated.shape[0])+\
       1j*np.random.randn(modulated.shape[0])) # AWGN
  
  noisy_uncoded = modulated_uncoded + np.sqrt(No/2)*\
      (np.random.randn(modulated_uncoded.shape[0])+\
       1j*np.random.randn(modulated_uncoded.shape[0])) # AWGN (uncoded case)

  demodulated_soft = modem.demodulate(noisy, demod_type='soft', noise_var=noiseVar) # demodulation (soft output)
  demodulated_hard = modem.demodulate(noisy, demod_type='hard') # demodulation (hard output)
  demodulated_uncoded = modem.demodulate(noisy_uncoded, demod_type='hard') # demodulation (uncoded case)

  decoded_soft = cc.viterbi_decode(demodulated_soft, trellis, tb_depth, decoding_type='unquantized') # decoding (soft decision)
  decoded_hard = cc.viterbi_decode(demodulated_hard, trellis, tb_depth, decoding_type='hard') # decoding (hard decision)

  NumErr, BER_soft[cntr] = BER_calc(message_bits, decoded_soft[:message_bits.size]) # bit-error ratio (soft decision)
  NumErr, BER_hard[cntr] = BER_calc(message_bits, decoded_hard[:message_bits.size]) # bit-error ratio (hard decision)
  NumErr, BER_uncoded[cntr] = BER_calc(message_bits, demodulated_uncoded[:message_bits.size]) # bit-error ratio (uncoded case)

mean_BER_soft = BER_soft.mean() # averaged bit-error ratio (soft decision)
mean_BER_hard = BER_hard.mean() # averaged bit-error ratio (hard decision)
mean_BER_uncoded = BER_uncoded.mean() # averaged bit-error ratio (uncoded case)

print("Soft decision:\n{}\n".format(mean_BER_soft))
print("Hard decision:\n{}\n".format(mean_BER_hard))
print("Uncoded message:\n{}\n".format(mean_BER_uncoded))

PS: The package and libraries are from the Github cloned version.

@SalmaEasa
Copy link

did you find the issue in the library itself or what ?

@daniel-petitfils
Copy link

There is something that looks strange in the puncturing code in the current revision:

In https://github.com/veeresht/CommPy/blob/master/commpy/channelcoding/convcode.py#L500 we define a default value for the puncturing matrix, so from this point the matrix should not be None. But later, in

if puncture_matrix is not None:
, we do not apply the puncturing matrix if the matrix actually defined. It looks to me that the puncturing is never applied on the size of p_outbits and we are stuck with the default 1/2.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants