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,源码下载地址Commpy源码下载
方法一 pip install scikit-commpy 方法二 git clone https://github.com/veeresht/CommPy.git cd CommPy python setup.py install
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 symbol
# 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')
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()