802.11a ofdm 过程了解

  ofdm_demo.py

import numpy as np
from scipy import interpolate
import commpy as cpy
import ofdm_debug as ofdm_debug
class OFDMSystem:def __init__(self, K=64, CP=None, P=8, pilotValue=3+3j, Modulation_type='QAM16', channel_type='random', SNRdb=25,debug=False):# 设置OFDM子载波数量self.K = K# 设置循环前缀长度if CP is not None:self.CP = CPelse:self.CP = self.K // 4  # 默认为K的四分之一# 设置导频数量self.P = P# 设置导频值self.pilotValue = pilotValue# 设置调制方式self.Modulation_type = Modulation_type# 设置信道类型self.channel_type = channel_type# 设置信噪比（dB）self.SNRdb = SNRdb# 生成所有子载波的索引self.allCarriers = np.arange(self.K)# 计算导频位置（每间隔K//P个子载波一个导频）self.pilotCarrier = self.allCarriers[::self.K // self.P]# 将最后一个子载波也作为导频self.pilotCarriers = np.hstack([self.pilotCarrier, np.array([self.allCarriers[-1]])])# 更新导频数量self.P += 1# 调制方式到比特数的映射self.m_map = {"BPSK": 1, "QPSK": 2, "8PSK": 3, "QAM16": 4, "QAM64": 6}# 获取当前调制方式对应的比特数self.mu = self.m_map[self.Modulation_type]# 获取数据子载波的位置（排除导频位置）self.dataCarriers = np.delete(self.allCarriers, self.pilotCarriers)# 计算每个OFDM符号的有效载荷位数self.payloadBits_per_OFDM = len(self.dataCarriers) * self.muif(debug==True):ofdm_debug.print_paramter(self.payloadBits_per_OFDM, self.pilotCarriers, self.dataCarriers, self.P, self.Modulation_type, self.channel_type, self.SNRdb, self.mu)def Modulation(self, bits):if self.Modulation_type == "QPSK":PSK4 = cpy.PSKModem(4)return PSK4.modulate(bits)elif self.Modulation_type == "QAM64":QAM64 = cpy.QAMModem(64)return QAM64.modulate(bits)elif self.Modulation_type == "QAM16":QAM16 = cpy.QAMModem(16)return QAM16.modulate(bits)elif self.Modulation_type == "8PSK":PSK8 = cpy.PSKModem(8)return PSK8.modulate(bits)elif self.Modulation_type == "BPSK":BPSK = cpy.PSKModem(2)return BPSK.modulate(bits)def DeModulation(self, symbol):if self.Modulation_type == "QPSK":PSK4 = cpy.PSKModem(4)return PSK4.demodulate(symbol, demod_type='hard')elif self.Modulation_type == "QAM64":QAM64 = cpy.QAMModem(64)return QAM64.demodulate(symbol, demod_type='hard')elif self.Modulation_type == "QAM16":QAM16 = cpy.QAMModem(16)return QAM16.demodulate(symbol, demod_type='hard')elif self.Modulation_type == "8PSK":PSK8 = cpy.PSKModem(8)return PSK8.demodulate(symbol, demod_type='hard')elif self.Modulation_type == "BPSK":BPSK = cpy.PSKModem(2)return BPSK.demodulate(symbol, demod_type='hard')def add_awgn(self, x_s):data_pwr = np.mean(abs(x_s**2))noise_pwr = data_pwr / (10 ** (self.SNRdb / 10))noise = 1 / np.sqrt(2) * (np.random.randn(len(x_s)) + 1j * np.random.randn(len(x_s))) * np.sqrt(noise_pwr)return x_s + noise, noise_pwrdef channel(self, in_signal):channelResponse = np.array([1, 0, 0.3 + 0.3j])if self.channel_type == "random":convolved = np.convolve(in_signal, channelResponse)return self.add_awgn(convolved)elif self.channel_type == "awgn":return self.add_awgn(in_signal)def OFDM_symbol(self, QAM_payload):symbol = np.zeros(self.K, dtype=complex)symbol[self.pilotCarriers] = self.pilotValuesymbol[self.dataCarriers] = QAM_payloadreturn symboldef IDFT(self, OFDM_data):return np.fft.ifft(OFDM_data)def addCP(self, OFDM_time):cp = OFDM_time[-self.CP:]return np.hstack([cp, OFDM_time])def removeCP(self, signal):return signal[self.CP:self.CP + self.K]def DFT(self, OFDM_RX):return np.fft.fft(OFDM_RX)def channelEstimate(self, OFDM_demod):# 提取导频位置的数据pilots = OFDM_demod[self.pilotCarriers]print(f"导频位置的1: {pilots}")  print(f"导频的值: {self.pilotValue}") # LS信道估计Hest_at_pilots = pilots / self.pilotValue # 假设pilotValue是已知的导频值print(f"导频位置的信道估计: {Hest_at_pilots}")  # 插值扩展到所有子载波Hest_abs = interpolate.interp1d(self.pilotCarriers, abs(Hest_at_pilots), kind='linear')(self.allCarriers)Hest_phase = interpolate.interp1d(self.pilotCarriers, np.angle(Hest_at_pilots), kind='linear')(self.allCarriers)Hest = Hest_abs * np.exp(1j * Hest_phase)print(f"Hest result: {Hest}")  return Hestdef equalize(self, OFDM_demod, Hest):return OFDM_demod / Hestdef simulate(self):bits = np.random.binomial(n=1, p=0.5, size=(self.payloadBits_per_OFDM,))ofdm_debug.print_bits(bits)QAM_s = self.Modulation(bits)ofdm_debug.print_qams(QAM_s)OFDM_data = self.OFDM_symbol(QAM_s)ofdm_debug.print_symble(OFDM_data)OFDM_time = self.IDFT(OFDM_data)ofdm_debug.print_ifft(OFDM_time)OFDM_withCP = self.addCP(OFDM_time)OFDM_TX = OFDM_withCPofdm_debug.print_tx(OFDM_TX)OFDM_RX, _ = self.channel(OFDM_TX)ofdm_debug.print_rx(OFDM_RX)if(self.channel_type == "awgn"):passelse:print("输入信号长度:N")print("信道冲激响应长度:M")print("卷积后输出长度:N+M-1")print(f"self.channel 信道输出: diff={len(OFDM_RX) - len(OFDM_TX)}")OFDM_RX_noCP = self.removeCP(OFDM_RX)ofdm_debug.print_nocprx(OFDM_RX_noCP)OFDM_demod = self.DFT(OFDM_RX_noCP)  #它将时域信号转换为频域信号ofdm_debug.print_DFT(OFDM_demod)Hest = self.channelEstimate(OFDM_demod)ofdm_debug.print_HST(Hest)equalized_Hest = self.equalize(OFDM_demod, Hest)QAM_est = equalized_Hest[self.dataCarriers]bits_est = self.DeModulation(QAM_est)ber = np.sum(abs(bits - bits_est)) / len(bits)return berif __name__ == '__main__':ofdm_system = OFDMSystem(K=64, CP=None, P=8, pilotValue=3+3j, Modulation_type='QAM16', channel_type='random', SNRdb=25)ber = ofdm_system.simulate()print(f"误比特率BER： {ber}")

ofdm_debug.py

import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
import commpy as cpydef print_paramter(payloadBits_per_OFDM,pilotCarriers,dataCarriers,P,Modulation_type,channel_type,SNRdb,mu):print(f"有效载荷位数:  {payloadBits_per_OFDM}")print(f"导频位置:     {pilotCarriers}")print(f"数据位置:     {dataCarriers}")print(f"导频数:       {P}")print(f"调制方式:     {Modulation_type}")print(f"信道类型:     {channel_type}")print(f"信噪比:       {SNRdb}")print(f"dataCarriers num:       { len(dataCarriers)}")print(f"mu:       {mu}")def print_bits(bits):# 每4个比特分为一组打印print(f"bits:  {len(bits)}")for i in range(0, len(bits), 4):print(f"{int(i//4)} {bits[i:i+4]}")def print_qams(QAM_s):# 每4个比特分为一组打印print(f"QAM_s:  {len(QAM_s)}")for i in range(0, len(QAM_s)):print(f"{i} {QAM_s[i]}")def print_symble(symbol):# 每4个比特分为一组打印print(f"symbol:  {len(symbol)}")for i in range(0, len(symbol)):print(f"{i} {symbol[i]}")def print_ifft(ifft):print(f"ifft:  {len(ifft)}")for i in range(0, len(ifft)):print(f"{i} {ifft[i]}")def print_tx(tx):print(f"tx:  {len(tx)}")for i in range(0, len(tx)):print(f"{i} {tx[i]}")     def print_rx(rx):print(f"rx:  {len(rx)}")for i in range(0, len(rx)):print(f"{i} {rx[i]}")def print_nocprx(rx):print(f"nocprx:  {len(rx)}")for i in range(0, len(rx)):print(f"{i} {rx[i]}")def print_DFT(DFT):print(f"DFT:  {len(DFT)}")for i in range(0, len(DFT)):print(f"{i} {DFT[i]}")def print_HST(HST):print(f"HST:  {len(HST)}")for i in range(0, len(HST)):print(f"{i} {HST[i]}")

https://zhuanlan.zhihu.com/p/434928660