What do you mean by sync level?

I haven’t tried or done any experiments on time synchronisation. With the current setup there is a definate lag between changing the power levels of each signal. This could be related to the length of the arm waveform.

My next steps will be to add Crest Factor Reduction to improve the PAPR and hopefully improve the signal quality at higher power levels.

Thanks Andrew,

Got it working now. I think it was down to the power requirements for running 2 VSGs from one hub on my dev laptop. I successfully got it working yesterday with 1 VSG and 1 SM200 running from the same usb3 hub. And the other vsg plugged into a seperate port. The SM200 is not bus powered.

All works fine now. I hope to try 5 devices today.

Forum wont allow .py files. Hopefully the txt will work.

# -*- coding: utf-8 -*-

# This example generates a basic AWGN signal.

from vsgdevice.vsg_api import *
from time import sleep
import matplotlib.pyplot as plt
#numpy.set_printoptions(threshold=numpy.inf) #Use to print full length arrays

def plot_complex_fft(complex_data, sampling_rate):
# Calculate and plot the Frequency data
# Calculate FFT
fft_result = numpy.fft.fft(complex_data)
fft_freq = numpy.fft.fftfreq(len(complex_data), 1 / sampling_rate)
# print(fft_freq)
# Plot the results
plt.figure(figsize=(10, 6))

# Plot the frequency-domain signal (FFT)
plt.plot(fft_freq, numpy.real(fft_result), color=’red’, label=’I Data’)
plt.plot(fft_freq, numpy.imag(fft_result), color=’blue’, label=’Q Data’)
# plt.plot(numpy.abs(fft_result))
plt.title(‘FFT of Complex Signal’)
plt.xlabel(‘Frequency (Hz)’)
plt.ylabel(‘Amplitude’)

plt.show()

def plot_IQ(complex_data, sampling_rate):
# Plot the Time Series results
plt.figure(figsize=(10, 6))
# Separate the real and imaginary parts
I_data = numpy.real(complex_data)
Q_data = numpy.imag(complex_data)
# Plot the frequency-domain signal (FFT)
plt.plot(I_data, color=’red’, label=’I Data’)
plt.plot(Q_data, color=’blue’, label=’Q Data’)

# plt.plot(numpy.abs(fft_result))
plt.title(‘Complex Signal’)
plt.xlabel(‘Samples’)
plt.ylabel(‘Amplitude’)

plt.show()

def low_pass_filter(complex_data: numpy.ndarray, BW: int = 40e6, sampling_rate: int = 50e6) -> numpy.ndarray:
# translate bandlimit from Hz to dataindex according to sampling rate and data size
bandlimit_index = int(BW * (complex_data.size/2) / sampling_rate)
fsig = numpy.fft.fft(complex_data)

for i in range(bandlimit_index + 1, len(fsig) – bandlimit_index):
fsig[i] = 0

adata_filtered = numpy.fft.ifft(fsig)

return adata_filtered

def normalise_level(complex_data):
# Normalise average level to 0dB
aveSig = numpy.average(numpy.abs(complex_data))
aveSigdB = 20*numpy.log10(aveSig) # should be 0dB
print(“Normalised Signal dB = ” + str(aveSigdB))
complex_data *= 1/aveSig
aveSig = numpy.average(numpy.abs(complex_data))
aveSigdB = 20*numpy.log10(aveSig) # should be 0dB
print(“Normalised Signal dB = ” + str(aveSigdB))

return complex_data

def interleaved_to_complex(interleaved_data):
# Reshape the interleaved data into a complex array
complex_data = interleaved_data[0::2] + 1j * interleaved_data[1::2]
return complex_data

def complex_to_interleaved(complex_data):
# Separate the real and imaginary parts
real_parts = numpy.real(complex_data)
imag_parts = numpy.imag(complex_data)

# Interleave the real and imaginary parts
interleaved_iq = numpy.empty(2 * len(complex_data), dtype=numpy.float32)
interleaved_iq[0::2] = real_parts
interleaved_iq[1::2] = imag_parts

return interleaved_iq

def complex_AWGN(length, stddev):

iq = numpy.random.normal(0, stddev, length) + 1j * \
numpy.random.normal(0, stddev, length) # .astype(numpy.float32)
return iq

def generate_iq():
# Open device

ret = vsg_open_device()
print(ret)
handle = ret[“handle”]

serialNumber = vsg_get_serial_number(handle)[“serial”]

# Configure generator
freq = 1.0e9 # Hz
sample_rate = 50.0e6 # samples per second
BW = 40.0e6 # Target bandwidth of AWGN
level = -20.0 # dBm

vsg_set_frequency(handle, freq)
vsg_set_level(handle, level)
vsg_set_sample_rate(handle, sample_rate)
vsg_recal(handle)

# Gernerate Waveform
iq = complex_AWGN(16384, 100)
iq = low_pass_filter(iq, BW, sample_rate)
iq = normalise_level(iq) #Set IQ to 0dB
iq = complex_to_interleaved(iq)

vsg_repeat_waveform(handle, iq.astype(numpy.float32), int(len(iq)/2))
print(“Waveform set”)

# Ramp Power
for power in range(-100, -30, 5):

vsg_set_level(handle, power)
print(“Power = ” + str(power))
scaling = vsg_get_IQ_scale(handle)[“iq_scale”]
print(“scaling = ” + str(scaling))
sleep(1)
print(“complete”)
# Stop waveform
vsg_abort(handle)

# Done with device
vsg_close_device(handle)
print(“closed”)

if __name__ == “__main__”:
generate_iq()

Thanks Andrew

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