path, dirs, files = os.walk(directory).__next__()
file_count = 100#len(files)
nIterations = file_count
Fs = np.intc(802e3) # Receiver sample rate. #! Must be the same as the signals
T=np.float(6e-3) # Pulse duration. #! Must be the same as the signals
# Generate logarithmic spread of Eb/N0 values.
EbN0Start = 40
EbN0End = 0
#EbN0Vector = -np.subtract(-EbN0End, np.logspace(np.log10(-EbN0Start), np.log10(-EbN0End), num=31, endpoint=True, base=10.0))
EbN0Vector = np.linspace(EbN0End, EbN0Start, 41)
#EbN0Vector = np.linspace(0, -30, 31)
snrVector = comm.EbN0toSNRdB(EbN0Vector, 2, Fs, 1/T)
fCenterEstimate = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
fCenterEstimate2 = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
R_symbEstimate = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
R_symbEstimate2 = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
for i in range(0, nIterations):
print("Iteration", i+1, "of", nIterations)
# Load from binary file
filename = str(i)
fileString = path + filename + ".pkl"
with open(fileString,'rb') as f:
sigObj = pickle.load(f)
return packet
# Configure estimators
estimators = []
estimators.append(estimator('Packet Extractor', packetExtractor, Fs=Fs, T=T, threshold=util.db2pow(-10.5)))
# Create analysis object
m_analysis = analysis('Packet_Extraction', estimators=estimators, lossFcn='MAE')
# Generate Eb/N0 range for statistics gathering.
EbN0Start = 28
EbN0End = 24
EbN0Vec = np.linspace(EbN0End, EbN0Start, 41)
SnrVec = comm.EbN0toSNRdB(EbN0Vec, 2, Fs, 1/T)
m_analysis.axis.displayName = '$E_b/N_0$ [dB]'
m_analysis.axis.displayVector = EbN0Vec
m_analysis.axis.name = 'S/N [dB]'
m_analysis.axis.vector = SnrVec
m_analysis.analyze(iterations=nIterations, parameter='packet', packetSize=10, debug=False)
# Write to binary file
path = '../jobs/'
filename = 'packetJob'
destination = path + filename + str(m_analysis.iterations) + '.pkl'
# Save job to binary file
with open(destination,'wb') as f:
pickle.dump(m_analysis, f)
estimator('$\sqrt{CRLB}$ [Hz]', CRLB, packetSize=packetSize, Fs=Fs))
# Create analysis object
m_analysis = analysis('Center_Frequency_Estimation',
estimators=estimators,
lossFcn='MAE')
# Generate Eb/N0 range for statistics gathering.
EbN0Start = 40
EbN0End = 10
EbN0Range = np.linspace(EbN0End, EbN0Start, EbN0Start - EbN0End + 1)
m_analysis.axis.displayName = '$E_b/N_0$ [dB]'
m_analysis.axis.displayVector = EbN0Range
m_analysis.axis.name = 'S/N [dB]'
m_analysis.axis.vector = comm.EbN0toSNRdB(EbN0Range, 2, Fs, 1 / T)
m_analysis.analyze(iterations=nIterations,
parameter='fCenter',
packetSize=packetSize,
debug=Debug)
"""# Write to binary file
path = '../jobs/'
filename = 'centerFrequencyJob'
destination = path + filename + str(m_analysis.iterations) + '.pkl'
# Save job to binary file
with open(destination,'wb') as f:
pickle.dump(m_analysis, f)
# Read from binary file
path = '../jobs/'
fWindowWidthHertz=50e3))
# Create analysis object
m_analysis = analysis('Symbol_Rate_Estimation',
estimators=estimators,
lossFcn='MAE')
# Generate Eb/N0 range for statistics gathering.
EbN0Start = 40
EbN0End = 10
m_analysis.axis.displayName = '$E_b/N_0$ [dB]'
m_analysis.axis.displayVector = np.linspace(EbN0End, EbN0Start,
EbN0Start - EbN0End + 1)
m_analysis.axis.name = 'S/N [dB]'
m_analysis.axis.vector = comm.EbN0toSNRdB(m_analysis.axis.displayVector, 2, Fs,
1 / T)
m_analysis.analyze(iterations=nIterations,
parameter='symbolRate',
packetSize=packetSize,
debug=Debug)
# Write to binary file
path = '../jobs/'
jobname = 'SRateJob'
destination = path + jobname + str(m_analysis.iterations) + '.pkl'
# Save job to binary file
with open(destination, 'wb') as f:
pickle.dump(m_analysis, f)
iterations = nIterations #! Must be same as job file
"""