samples per symbol
时间: 2024-04-10 08:23:43 浏览: 35
Samples per symbol(每个符号的样本数)是指在数字通信中,每个符号所对应的采样数。在数字通信中,信号通常是以连续时间的形式传输的,但是为了进行数字处理和传输,需要将连续时间信号转换为离散时间信号。这个过程称为采样。
在采样过程中,连续时间信号被离散化为一系列的样本点。每个样本点代表了信号在某个特定时间点的幅度。而每个符号则代表了一段特定的信息,比如一个二进制位或者一个调制符号。
Samples per symbol的值决定了每个符号所对应的采样数。较高的Samples per symbol意味着更密集的采样,可以提高信号的准确性和可靠性,但也会增加传输和处理的复杂性和成本。较低的Samples per symbol则意味着较少的采样,可以降低成本和复杂性,但可能会损失一些信号信息。
在数字通信系统中,Samples per symbol的选择需要考虑到信道带宽、噪声干扰、调制方式等因素。通常会根据系统需求和性能要求进行权衡和选择。
相关问题
详细解释以下Python代码:import numpy as np import adi import matplotlib.pyplot as plt sample_rate = 1e6 # Hz center_freq = 915e6 # Hz num_samps = 100000 # number of samples per call to rx() sdr = adi.Pluto("ip:192.168.2.1") sdr.sample_rate = int(sample_rate) # Config Tx sdr.tx_rf_bandwidth = int(sample_rate) # filter cutoff, just set it to the same as sample rate sdr.tx_lo = int(center_freq) sdr.tx_hardwaregain_chan0 = -50 # Increase to increase tx power, valid range is -90 to 0 dB # Config Rx sdr.rx_lo = int(center_freq) sdr.rx_rf_bandwidth = int(sample_rate) sdr.rx_buffer_size = num_samps sdr.gain_control_mode_chan0 = 'manual' sdr.rx_hardwaregain_chan0 = 0.0 # dB, increase to increase the receive gain, but be careful not to saturate the ADC # Create transmit waveform (QPSK, 16 samples per symbol) num_symbols = 1000 x_int = np.random.randint(0, 4, num_symbols) # 0 to 3 x_degrees = x_int*360/4.0 + 45 # 45, 135, 225, 315 degrees x_radians = x_degrees*np.pi/180.0 # sin() and cos() takes in radians x_symbols = np.cos(x_radians) + 1j*np.sin(x_radians) # this produces our QPSK complex symbols samples = np.repeat(x_symbols, 16) # 16 samples per symbol (rectangular pulses) samples *= 2**14 # The PlutoSDR expects samples to be between -2^14 and +2^14, not -1 and +1 like some SDRs # Start the transmitter sdr.tx_cyclic_buffer = True # Enable cyclic buffers sdr.tx(samples) # start transmitting # Clear buffer just to be safe for i in range (0, 10): raw_data = sdr.rx() # Receive samples rx_samples = sdr.rx() print(rx_samples) # Stop transmitting sdr.tx_destroy_buffer() # Calculate power spectral density (frequency domain version of signal) psd = np.abs(np.fft.fftshift(np.fft.fft(rx_samples)))**2 psd_dB = 10*np.log10(psd) f = np.linspace(sample_rate/-2, sample_rate/2, len(psd)) # Plot time domain plt.figure(0) plt.plot(np.real(rx_samples[::100])) plt.plot(np.imag(rx_samples[::100])) plt.xlabel("Time") # Plot freq domain plt.figure(1) plt.plot(f/1e6, psd_dB) plt.xlabel("Frequency [MHz]") plt.ylabel("PSD") plt.show(),并分析该代码中QPSK信号的功率谱密度图的特点
这段Python代码的作用是使用ADI Pluto SDR设备生成并传输一个QPSK信号,并将接收到的信号进行功率谱密度分析。下面是对代码的注释:
```
import numpy as np
import adi
import matplotlib.pyplot as plt
# 设置采样率、中心频率和采样点数
sample_rate = 1e6 # Hz
center_freq = 915e6 # Hz
num_samps = 100000 # number of samples per call to rx()
# 连接ADI Pluto SDR设备
sdr = adi.Pluto("ip:192.168.2.1")
sdr.sample_rate = int(sample_rate)
# 配置发送端的参数
sdr.tx_rf_bandwidth = int(sample_rate) # filter cutoff, just set it to the same as sample rate
sdr.tx_lo = int(center_freq)
sdr.tx_hardwaregain_chan0 = -50 # Increase to increase tx power, valid range is -90 to 0 dB
# 配置接收端的参数
sdr.rx_lo = int(center_freq)
sdr.rx_rf_bandwidth = int(sample_rate)
sdr.rx_buffer_size = num_samps
sdr.gain_control_mode_chan0 = 'manual'
sdr.rx_hardwaregain_chan0 = 0.0 # dB, increase to increase the receive gain, but be careful not to saturate the ADC
# 创建发送的QPSK信号
num_symbols = 1000
x_int = np.random.randint(0, 4, num_symbols) # 0 to 3
x_degrees = x_int*360/4.0 + 45 # 45, 135, 225, 315 degrees
x_radians = x_degrees*np.pi/180.0 # sin() and cos() takes in radians
x_symbols = np.cos(x_radians) + 1j*np.sin(x_radians) # this produces our QPSK complex symbols
samples = np.repeat(x_symbols, 16) # 16 samples per symbol (rectangular pulses)
samples *= 2**14 # The PlutoSDR expects samples to be between -2^14 and +2^14, not -1 and +1 like some SDRs
# 启动发送端并发送信号
sdr.tx_cyclic_buffer = True # Enable cyclic buffers
sdr.tx(samples) # start transmitting
# 接收接收端的信号
for i in range (0, 10):
raw_data = sdr.rx() # Receive samples
rx_samples = sdr.rx()
print(rx_samples)
# 停止发送端
sdr.tx_destroy_buffer()
# 计算接收到的信号的功率谱密度
psd = np.abs(np.fft.fftshift(np.fft.fft(rx_samples)))**2
psd_dB = 10*np.log10(psd)
f = np.linspace(sample_rate/-2, sample_rate/2, len(psd))
# 绘制时域图
plt.figure(0)
plt.plot(np.real(rx_samples[::100]))
plt.plot(np.imag(rx_samples[::100]))
plt.xlabel("Time")
# 绘制频域图
plt.figure(1)
plt.plot(f/1e6, psd_dB)
plt.xlabel("Frequency [MHz]")
plt.ylabel("PSD")
plt.show()
```
以上代码生成了一个随机QPSK信号,通过ADI Pluto SDR设备将其传输,并使用Pluto SDR设备接收该信号。接收到的信号进行了功率谱密度分析,并绘制了频域图。
QPSK信号的功率谱密度图的特点是,其频谱表现为四个簇,每个簇对应QPSK信号的一个符号。每个簇的带宽约为基带信号的带宽,且由于使用矩形脉冲,每个簇的带宽之间有一定的重叠。此外,功率谱密度图中还可以看到一些其他频率分量,这些分量可能是由于接收信号中存在其他干扰或噪声导致的。
用simulink实现16QAM的调制与解调
下面是用Simulink实现16QAM调制与解调的步骤:
1. 创建一个新模型
2. 在模型中添加一个Sine Wave Generator(正弦波发生器)和一个 Random Integer Generator(随机整数生成器),并连接到16QAM Modulator(16QAM调制器)的输入端口。设置Sine Wave Generator的振幅为1,频率为1 Hz,将Random Integer Generator的样本时间设置为1。
3. 在模型中添加16QAM Modulator,并连接到16QAM Demodulator(16QAM解调器)的输入端口。
4. 在16QAM Modulator的对话框中,设置“Symbol Order”为Gray,设置“Bit Input”为“On”,设置“Samples per Symbol”为1。
5. 在16QAM Demodulator的对话框中,设置“Symbol Order”为Gray,设置“Bit Output”为“On”,设置“Decision Type”为Hard Decision。
6. 在模型中添加一个Scope(示波器),并连接到16QAM Demodulator的输出端口。
7. 运行模型,观察Scope的输出。
在Simulink中实现16QAM调制与解调的详细步骤可以参考以下链接:https://www.mathworks.com/help/comm/examples/16-qam-simulation-using-simulink.html