深度学习的qam解调器设计代码

以下是一个简单的基于深度学习的QAM解调器设计代码示例，使用Python和Keras库进行实现：

import numpy as np
import keras

# 生成QAM调制信号
def generate_qam_signal(M=16, N=10000):
    bits_per_symbol = int(np.log2(M))
    bits = np.random.randint(low=0, high=2, size=N*bits_per_symbol)
    symbols = np.zeros(N, dtype=np.complex)
    for i in range(N):
        symbol_bits = bits[i*bits_per_symbol:(i+1)*bits_per_symbol]
        symbol_index = int(''.join([str(b) for b in symbol_bits]), 2)
        symbols[i] = np.exp(1j*(2*np.pi/M)*(symbol_index))
    return symbols

# 添加噪声
def add_noise(symbols, SNR_dB):
    signal_power = np.mean(np.abs(symbols)**2)
    noise_power = signal_power/(10**(SNR_dB/10))
    noise = np.sqrt(noise_power/2)*(np.random.randn(symbols.shape[0]) + 1j*np.random.randn(symbols.shape[0]))
    return symbols + noise

# 生成训练数据
def generate_train_data(M=16, SNR_dB=20, N=10000):
    symbols = generate_qam_signal(M, N)
    noisy_symbols = add_noise(symbols, SNR_dB)
    input_data = np.concatenate((np.real(noisy_symbols)[:, np.newaxis], np.imag(noisy_symbols)[:, np.newaxis]), axis=1)
    output_data = keras.utils.to_categorical(np.arange(M)[np.newaxis, :] * np.ones((N, 1)), M)
    return input_data, output_data

# 构建模型
def build_model(input_shape, num_classes):
    model = keras.models.Sequential()
    model.add(keras.layers.Dense(64, activation='relu', input_shape=input_shape))
    model.add(keras.layers.Dense(64, activation='relu'))
    model.add(keras.layers.Dense(num_classes, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 训练模型
def train_model(model, input_data, output_data, batch_size=128, epochs=10, validation_split=0.1):
    model.fit(input_data, output_data, batch_size=batch_size, epochs=epochs, validation_split=validation_split)

# 测试模型
def test_model(model, M=16, SNR_dB=20, N=10000):
    symbols = generate_qam_signal(M, N)
    noisy_symbols = add_noise(symbols, SNR_dB)
    input_data = np.concatenate((np.real(noisy_symbols)[:, np.newaxis], np.imag(noisy_symbols)[:, np.newaxis]), axis=1)
    output_data = keras.utils.to_categorical(np.arange(M)[np.newaxis, :] * np.ones((N, 1)), M)
    loss, accuracy = model.evaluate(input_data, output_data)
    print('Test loss:', loss)
    print('Test accuracy:', accuracy)

# 使用示例
M = 16
SNR_dB = 20
N_train = 10000
N_test = 1000
input_shape = (2,)
num_classes = M

input_data, output_data = generate_train_data(M, SNR_dB, N_train)
model = build_model(input_shape, num_classes)
train_model(model, input_data, output_data, batch_size=128, epochs=10, validation_split=0.1)
test_model(model, M, SNR_dB, N_test)

该代码生成了一个具有两个ReLU层和一个Softmax输出层的简单神经网络模型，用于识别16-QAM调制信号。它使用随机生成的训练数据进行训练，并在测试数据上进行测试。在测试数据上的准确性可以作为模型的性能指标。