Tx_signal_power = var(windowed_Tx_data);%发送信号功率 linear_SNR=10^(SNR/10);%线性信噪比 noise_sigma=Tx_signal_power/linear_SNR; noise_scale_factor = sqrt(noise_sigma);%标准差sigma noise=randn(1,((symbols_per_carrier)*(IFFT_bin_length+GI))+GIP)*noise_scale_factor;%产生正态分布噪声序列 Rx_data=windowed_Tx_data +noise;%接收到的信号加噪声
时间: 2023-06-27 21:08:10 浏览: 65
这段代码是用于产生一段发送信号,并在其上添加高斯白噪声,模拟接收端接收到的信号。具体来说,代码中首先计算了发送信号的功率,然后根据指定的信噪比计算出噪声的标准差。接着,使用randn函数生成一个与发送信号长度相同、均值为0、标准差为noise_scale_factor的正态分布噪声序列。最后,将发送信号和噪声相加得到接收到的信号Rx_data。其中,windowed_Tx_data是发送信号,symbols_per_carrier是每个子载波上的符号数,IFFT_bin_length是每个子载波上离散傅里叶变换(IFFT)的长度,GI是保护间隔(Guard Interval),SNR是信噪比。
相关问题
c语言使用fopen_s怎么求wav的时域信号功率谱
你可以使用以下代码来求wav的时域信号功率谱:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define BUFFER_SIZE 4096
int main(int argc, char* argv[])
{
if(argc < 2)
{
printf("Usage: %s <input_wav_file>\n", argv[0]);
return 1;
}
FILE* input_file;
errno_t err = fopen_s(&input_file, argv[1], "rb");
if(err != 0)
{
printf("Error opening input file: %s\n", argv[1]);
return 1;
}
char buffer[BUFFER_SIZE];
int bytes_read;
int num_samples = 0;
int sample_rate = 0;
short sample;
// Read the header of the WAV file
bytes_read = fread(buffer, sizeof(char), 44, input_file);
if(bytes_read != 44)
{
printf("Error reading WAV header\n");
fclose(input_file);
return 1;
}
// Parse the header to get the number of samples and the sample rate
num_samples = *(int*)(buffer + 40);
sample_rate = *(int*)(buffer + 24);
// Allocate memory for the audio data
short* audio_data = (short*)malloc(num_samples * sizeof(short));
if(audio_data == NULL)
{
printf("Error allocating memory for audio data\n");
fclose(input_file);
return 1;
}
// Read the audio data from the file
bytes_read = fread(audio_data, sizeof(short), num_samples, input_file);
if(bytes_read != num_samples)
{
printf("Error reading audio data\n");
free(audio_data);
fclose(input_file);
return 1;
}
fclose(input_file);
// Compute the power spectrum of the audio data
int num_bins = BUFFER_SIZE / 2;
double* power_spectrum = (double*)malloc(num_bins * sizeof(double));
if(power_spectrum == NULL)
{
printf("Error allocating memory for power spectrum\n");
free(audio_data);
return 1;
}
int window_size = BUFFER_SIZE;
int hop_size = BUFFER_SIZE / 2;
int num_frames = (num_samples - window_size) / hop_size + 1;
for(int i = 0; i < num_frames; i++)
{
int start_index = i * hop_size;
int end_index = start_index + window_size;
if(end_index > num_samples)
{
end_index = num_samples;
}
// Apply a window function to the audio data
for(int j = start_index; j < end_index; j++)
{
audio_data[j] *= 0.5 * (1 - cos(2 * M_PI * (j - start_index) / (window_size - 1)));
}
// Compute the FFT of the windowed audio data
double* real_part = (double*)malloc(window_size * sizeof(double));
double* imag_part = (double*)malloc(window_size * sizeof(double));
for(int j = 0; j < window_size; j++)
{
real_part[j] = audio_data[start_index + j];
imag_part[j] = 0;
}
fft(real_part, imag_part, window_size);
// Compute the power spectrum of the FFT
for(int j = 0; j < num_bins; j++)
{
double magnitude = sqrt(real_part[j] * real_part[j] + imag_part[j] * imag_part[j]);
power_spectrum[j] += magnitude * magnitude / (window_size * hop_size);
}
free(real_part);
free(imag_part);
}
// Free memory
free(audio_data);
// Print the power spectrum to stdout
for(int i = 0; i < num_bins; i++)
{
double frequency = (double)i * sample_rate / BUFFER_SIZE;
printf("%f %f\n", frequency, power_spectrum[i]);
}
free(power_spectrum);
return 0;
}
// Computes the FFT of the given real and imaginary arrays of length n
void fft(double* real, double* imag, int n)
{
if(n == 1)
{
return;
}
double* even_real = (double*)malloc(n / 2 * sizeof(double));
double* even_imag = (double*)malloc(n / 2 * sizeof(double));
double* odd_real = (double*)malloc(n / 2 * sizeof(double));
double* odd_imag = (double*)malloc(n / 2 * sizeof(double));
for(int i = 0; i < n / 2; i++)
{
even_real[i] = real[2 * i];
even_imag[i] = imag[2 * i];
odd_real[i] = real[2 * i + 1];
odd_imag[i] = imag[2 * i + 1];
}
fft(even_real, even_imag, n / 2);
fft(odd_real, odd_imag, n / 2);
for(int i = 0; i < n / 2; i++)
{
double angle = -2 * M_PI * i / n;
double cos_angle = cos(angle);
double sin_angle = sin(angle);
real[i] = even_real[i] + cos_angle * odd_real[i] - sin_angle * odd_imag[i];
imag[i] = even_imag[i] + cos_angle * odd_imag[i] + sin_angle * odd_real[i];
real[i + n / 2] = even_real[i] - cos_angle * odd_real[i] + sin_angle * odd_imag[i];
imag[i + n / 2] = even_imag[i] - cos_angle * odd_imag[i] - sin_angle * odd_real[i];
}
free(even_real);
free(even_imag);
free(odd_real);
free(odd_imag);
}
```
这个代码使用了一个缓冲区来读取WAV文件的数据,并对缓冲区中的数据进行窗口函数和FFT处理,然后计算功率谱。该代码还使用了一个自定义的FFT函数,用于计算实数和虚数数组的FFT。
# -*- coding: utf-8 -*- block_cipher = None a = Analysis( ['Set_monitor.py'], pathex=[], binaries=[], datas=[], hiddenimports=[], hookspath=[], hooksconfig={}, runtime_hooks=[], excludes=[], win_no_prefer_redirects=False, win_private_assemblies=False, cipher=block_cipher, noarchive=False, ) pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher) exe = EXE( pyz, a.scripts, [], exclude_binaries=True, name='Set_monitor', debug=False, bootloader_ignore_signals=False, strip=False, upx=True, console=True, disable_windowed_traceback=False, argv_emulation=False, target_arch=None, codesign_identity=None, entitlements_file=None, ) coll = COLLECT( exe, a.binaries, a.zipfiles, a.datas, strip=False, upx=True, upx_exclude=[], name='Set_monitor', ) SyntaxError:multiple statements found while compiling a single statement
这个错误是因为您将整个代码放在一个文件中,并尝试运行该文件时导致的。这段代码看起来像是 PyInstaller 的构建脚本,而不是可以直接运行的 Python 代码。
如果您想使用 PyInstaller 将 Python 脚本打包为可执行文件,您需要将上述代码保存为一个独立的 `.spec` 文件,并使用 PyInstaller 命令来构建可执行文件。具体步骤如下:
1. 将上述代码保存为一个名为 `Set_monitor.spec` 的文件(注意不要使用 `.py` 扩展名)。
2. 打开终端或命令提示符,导航到保存了 `Set_monitor.spec` 文件的目录。
3. 运行以下命令来构建可执行文件:
```
pyinstaller Set_monitor.spec
```
这将根据您在 `.spec` 文件中指定的配置创建一个可执行文件。
请确保已安装 PyInstaller 并将其添加到系统路径中。您可以使用以下命令安装 PyInstaller:
```
pip install pyinstaller
```
如果您只想运行这段代码而不是构建可执行文件,只需将代码保存为 `.py` 文件,并在终端或命令提示符中运行:
```
python Set_monitor.py
```
这将执行您的代码并输出结果。