Samples per symbol
时间: 2024-08-30 07:00:45 浏览: 30
"Samples per symbol"通常是指在数字信号处理或通信系统中,每个符号(如数字、字母或一组信息单元)所对应的采样点数量。在连续时间信号数字化的过程中,为了重建信号的精确特性,需要将连续信号按照一定的速率转换成离散样本。每个符号的时间长度决定了采样的频率(即每秒采样次数),而"Samples per symbol"则是这个频率除以传输的符号率,它给出了在每个符号周期内有多少个样本点。
例如,在4kHz采样率下,如果每个码元持续时间为1毫秒(即1000 samples per second / 1ms = 1000 samples per symbol),那么就表示每个码元有1000个样本点。这有助于确保信号的完整性,并在接收端通过适当的解码算法恢复原始信号。
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samples per symbol
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。
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。