本文不涉及原理讲解，只提供实现方法。需要借助Commpy开源包去实现通信中的各种处理。
安装方法
方法一 pip install scikit-commpy 方法二 git clone https://github.com/veeresht/CommPy.git cd CommPy python setup.py install2 实现 2.1 调制
import commpy as cpy bits = np.random.binomial(n=1,p=0.5,size=(128)) Modulation_type ="BPSK" if Modulation_type=="BPSK": bpsk = cpy.PSKModem(2) symbol = bpsk.modulate(bits) return symbol elif Modulation_type=="QPSK": qpsk = cpy.PSKModem(4) symbol = qpsk.modulate(bits) return symbol elif Modulation_type=="8PSK": psk8 = cpy.PSKModem(8) symbol = psk8.modulate(bits) return symbol elif Modulation_type=="8QAM": qam8 = cpy.QAMModem(8) symbol = qam8.modulate(bits) return symbol elif Modulation_type=="16QAM": qam16 = cpy.QAMModem(16) symbol = qam16.modulate(bits) return symbol elif Modulation_type=="64QAM": qam64 = cpy.QAMModem(64) symbol = qam64.modulate(bits) return symbol2.2 解调
# 和调制一样，需要先定义调制方法的类，再去调用解调的函数。 import commpy as cpy bits = np.random.binomial(n=1,p=0.5,size=(128)) # Modem : QPSK modem = mod.QAMModem(4) signal = modem.modulate(bits) modem.demodulate(signal, 'hard')3 完整编码和解码的例子
来源Commpy 例子
# Authors: CommPy contributors
# License: BSD 3-Clause
from __future__ import division, print_function # Python 2 compatibility
import math
import matplotlib.pyplot as plt
import numpy as np
import commpy.channelcoding.convcode as cc
import commpy.channels as chan
import commpy.links as lk
import commpy.modulation as mod
import commpy.utilities as util
# =============================================================================
# Convolutional Code 1: G(D) = [1+D^2, 1+D+D^2]
# Standard code with rate 1/2
# =============================================================================
# Number of delay elements in the convolutional encoder
memory = np.array(2, ndmin=1)
# Generator matrix
g_matrix = np.array((0o5, 0o7), ndmin=2)
# Create trellis data structure
trellis1 = cc.Trellis(memory, g_matrix)
# =============================================================================
# Convolutional Code 1: G(D) = [1+D^2, 1+D^2+D^3]
# Standard code with rate 1/2
# =============================================================================
# Number of delay elements in the convolutional encoder
memory = np.array(3, ndmin=1)
# Generator matrix (1+D^2+D^3 <-> 13 or 0o15)
g_matrix = np.array((0o5, 0o15), ndmin=2)
# Create trellis data structure
trellis2 = cc.Trellis(memory, g_matrix)
# =============================================================================
# Convolutional Code 2: G(D) = [[1, 0, 0], [0, 1, 1+D]]; F(D) = [[D, D], [1+D, 1]]
# RSC with rate 2/3
# =============================================================================
# Number of delay elements in the convolutional encoder
memory = np.array((1, 1))
# Generator matrix & feedback matrix
g_matrix = np.array(((1, 0, 0), (0, 1, 3)))
feedback = np.array(((2, 2), (3, 1)))
# Create trellis data structure
trellis3 = cc.Trellis(memory, g_matrix, feedback, 'rsc')
# =============================================================================
# Basic example using homemade counting and hard decoding
# =============================================================================
# Traceback depth of the decoder
tb_depth = None # Default value is 5 times the number or memories
for trellis in (trellis1, trellis2, trellis3):
for i in range(10):
# Generate random message bits to be encoded
message_bits = np.random.randint(0, 2, 1000)
# Encode message bits
coded_bits = cc.conv_encode(message_bits, trellis)
# Introduce bit errors (channel)
coded_bits[np.random.randint(0, 1000)] = 0
coded_bits[np.random.randint(0, 1000)] = 0
coded_bits[np.random.randint(0, 1000)] = 1
coded_bits[np.random.randint(0, 1000)] = 1
# Decode the received bits
decoded_bits = cc.viterbi_decode(coded_bits.astype(float), trellis, tb_depth)
num_bit_errors = util.hamming_dist(message_bits, decoded_bits[:len(message_bits)])
if num_bit_errors != 0:
print(num_bit_errors, "Bit Errors found!")
elif i == 9:
print("No Bit Errors :)")
# ==================================================================================================
# Complete example using Commpy features and compare hard and soft demodulation. Example with code 1
# ==================================================================================================
# Modem : QPSK
modem = mod.QAMModem(4)
# AWGN channel
channels = chan.SISOFlatChannel(None, (1 + 0j, 0j))
# SNR range to test
SNRs = np.arange(0, 6) + 10 * math.log10(modem.num_bits_symbol)
# Modulation function
def modulate(bits):
return modem.modulate(cc.conv_encode(bits, trellis1, 'cont'))
# Receiver function (no process required as there are no fading)
def receiver_hard(y, h, constellation, noise_var):
return modem.demodulate(y, 'hard')
# Receiver function (no process required as there are no fading)
def receiver_soft(y, h, constellation, noise_var):
return modem.demodulate(y, 'soft', noise_var)
# Decoder function
def decoder_hard(msg):
return cc.viterbi_decode(msg, trellis1)
# Decoder function
def decoder_soft(msg):
return cc.viterbi_decode(msg, trellis1, decoding_type='soft')
# Build model from parameters
code_rate = trellis1.k / trellis1.n
model_hard = lk.linkModel(modulate, channels, receiver_hard,
modem.num_bits_symbol, modem.constellation, modem.Es,
decoder_hard, code_rate)
model_soft = lk.linkModel(modulate, channels, receiver_soft,
modem.num_bits_symbol, modem.constellation, modem.Es,
decoder_soft, code_rate)
# Test
BERs_hard = model_hard.link_performance(SNRs, 10000, 600, 5000, code_rate)
BERs_soft = model_soft.link_performance(SNRs, 10000, 600, 5000, code_rate)
plt.semilogy(SNRs, BERs_hard, 'o-', SNRs, BERs_soft, 'o-')
plt.grid()
plt.xlabel('Signal to Noise Ration (dB)')
plt.ylabel('Bit Error Rate')
plt.legend(('Hard demodulation', 'Soft demodulation'))
plt.show()
