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example-module.py
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example-module.py
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"""
Copyright (c) 2022 Beijing Jiaotong University
PhotLab is licensed under [Open Source License].
You can use this software according to the terms and conditions of the [Open Source License].
You may obtain a copy of [Open Source License] at: [https://open.source.license/]
THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
See the [Open Source License] for more details.
Author: Zhenjie Wei
Created: 2023/8/19
Supported by: National Key Research and Development Program of China
"""
import phot
if __name__ == "__main__":
"""双偏振光收发模块 + 光纤信道"""
"""本代码为程序主函数 本代码主要适用于 QPSK，16QAM，32QAM，64QAM 调制格式的单载波相干背靠背（B2B）信号"""
phot.config(plot=True, backend="numpy") # 全局开启画图，backend 使用 numpy
# 设置全局系统仿真参数
num_symbols = 2**16 # 符号数目
bits_per_symbol = 4 # 2 for QPSK; 4 for 16QAM; 5 for 32QAM; 6 for 64QAM 设置调制格式
total_baud = 10e9 # 信号波特率，符号率
up_sampling_factor = 2 # 上采样倍数
sampling_rate = up_sampling_factor * total_baud # 信号采样率
""" 程序从此开始为发射端的仿真代码 """
# 首先产生发射端X/Y双偏振信号
bits = phot.gen_bits(num_symbols * bits_per_symbol) # 生成两列随机二进制序列
# QAM调制器
symbols = phot.qam_modulate(bits, bits_per_symbol)
# 此处先存储发射端原始发送信号，作为最后比较BER
prev_symbols = symbols
RRC_ROLL_OFF = 0.02 # RRC脉冲整形滚降系数
shaper = phot.PulseShaper(
up_sampling_factor=up_sampling_factor,
len_filter=128 * up_sampling_factor,
alpha=RRC_ROLL_OFF,
ts=1 / total_baud,
fs=sampling_rate,
)
signals = shaper.tx_shape(symbols)
""" 加入AWG中DAC的量化噪声 """
sampling_rate_awg = 96e9 # DAC采样率
dac_resolution_bits = 8 # DAC的bit位数
signals = phot.dac_noise(signals, sampling_rate_awg,
sampling_rate, dac_resolution_bits)
""" 加入发射端激光器产生的相位噪声 """
linewidth_tx = 150e3 # 激光器线宽
signals = phot.phase_noise(
signals, sampling_rate / total_baud, linewidth_tx, total_baud)
""" 根据设置的OSNR来加入高斯白噪声 """
osnr_db = 30 # 设置系统OSNR，也就是光信号功率与噪声功率的比值，此处单位为dB
signals = phot.gaussian_noise(signals, osnr_db, sampling_rate)
""" 发射端代码此处截止 """
""" Optical Fiber Channel """
# 实际情况：1000公里 10米一步
num_spans = 5 # 多少个 span (每个span经过一次放大器)
span_length = 75 # 一个 span 的长度 (km)
delta_z = 1 # 单步步长 (km)
alpha = 0.2
beta2 = 21.6676e-24
gamma = 1.3
# signals, signals_power = phot.fiber(signals, sampling_rate, num_spans, beta2, delta_z, gamma, alpha, span_length)
signals_power = phot.signal_power(signals)
""" 添加接收端激光器产生的相位噪声 """
linewidth_rx = 150e3 # 激光器线宽
signals = phot.phase_noise(
signals, sampling_rate / total_baud, linewidth_rx, total_baud)
""" 添加收发端激光器造成的频偏，就是发射端激光器和接收端激光器的中心频率的偏移差 """
frequency_offset = 2e9 # 设置频偏，一般激光器的频偏范围为 -3G~3G Hz
signals = phot.add_freq_offset(signals, frequency_offset, sampling_rate)
""" 模拟接收机造成的I/Q失衡，主要考虑幅度失衡和相位失衡，这里将两者都加在虚部上 """
signals = phot.add_iq_imbalance(signals)
""" 加入ADC的量化噪声 """
adc_sample_rate = 160e9 # ADC采样率
adc_resolution_bits = 8 # ADC的bit位数
signals = phot.adc_noise(signals, sampling_rate,
adc_sample_rate, adc_resolution_bits)
""" IQ正交化补偿，就是将之前的I/Q失衡的损伤补偿回来 """
signals = phot.iq_compensation(
signals, signals_power, sampling_rate, beta2, num_spans, span_length)
""" 粗糙的频偏估计和补偿，先进行一个频偏的补偿，因为后面有一个帧同步，而帧同步之前需要先对频偏进行补偿，否则帧同步不正确 """
signals = phot.freq_offset_compensation(signals, sampling_rate)
""" 接收端相应的RRC脉冲整形，具体的参数代码与发射端的RRC滤波器是一致的 """
signals = shaper.rx_shape(signals)
""" 帧同步，寻找与发射端原始信号头部对应的符号 """
signals, prev_symbols = phot.sync_frame(
signals, prev_symbols, up_sampling_factor)
""" 自适应均衡，此处采用恒模算法（CMA）对收敛系数进行预收敛，再拿收敛后的滤波器系数对正式的信号使用半径定向算法（RDE）进行均衡收敛，总的思想采用梯度下降法 """
num_tap = 25 # 均衡器抽头数目，此处均衡器内部是采用FIR滤波器，具体可查阅百度或者论文，
ref_power_cma = 2 # 设置CMA算法的模
cma_convergence = 30000 # CMA预均衡收敛的信号长度
step_size_cma = 1e-9 # CMA的更新步长，梯度下降法的步长
step_size_rde = 1e-9 # RDE的更新步长，梯度下降法的步长，%% CMA和RDE主要就是损失函数不同
signals = phot.adaptive_equalize(
signals,
num_tap,
cma_convergence,
ref_power_cma,
step_size_cma,
step_size_rde,
up_sampling_factor,
bits_per_symbol,
total_baud,
)
""" 均衡后进行精确的频偏估计和补偿 采用FFT-FOE算法，与前面的粗估计一样，防止前面粗估计没补偿完全，此处做一个补充 """
signals = phot.freq_offset_compensation(signals, total_baud)
""" 相位恢复 采用盲相位搜索算法（BPS）进行相位估计和补偿 """
num_test_angle = 64 # BPS算法的测试角数目，具体算法原理可以参考函数内部给的参考文献
block_size = 100 # BPS算法的块长设置
signals = phot.bps_restore(
signals, num_test_angle, block_size, bits_per_symbol)
# 分析器画星座图
phot.constellation_diagram(signals)
# 分析器画眼图
phot.eye_diagram(signals, up_sampling_factor)
""" 此处开始计算误码率 """
# 返回误码率和 Q 影响因子
ber, q_factor = phot.bits_error_count(
signals, prev_symbols, bits_per_symbol)