def test_psk_modulation():
bpsk = komm.PSKModulation(2)
qpsk = komm.PSKModulation(4)
psk8 = komm.PSKModulation(8)
r4 = (1.0 + 1.0j) / np.sqrt(2)
assert np.allclose(bpsk.constellation, [1.0, -1.0])
assert np.allclose(qpsk.constellation, [1.0, 1.0j, -1.0, -1.0j])
assert np.allclose(
psk8.constellation,
[1.0, r4, 1.0j, -r4.conjugate(), -1.0, -r4, -1.0j,
r4.conjugate()])
def _simulate(self):
order = 2**self._parameters['log_order']
amplitude = self._parameters['amplitude']
phase_offset = self._parameters['phase_offset']
labeling = self._parameters['labeling']
noise_power_db = self._parameters['noise_power_db']
modulation = komm.PSKModulation(order, amplitude, phase_offset, labeling)
modulation._constellation = np.round(modulation._constellation, 12) # Only for pedagogical reasons
awgn = komm.AWGNChannel()
num_symbols = 200*order
noise_power = 10**(noise_power_db / 10)
awgn.signal_power = modulation.energy_per_symbol
awgn.snr = awgn.signal_power / noise_power
num_bits = modulation.bits_per_symbol * num_symbols
bits = np.random.randint(2, size=num_bits)
sentword = modulation.modulate(bits)
recvword = awgn(sentword)
self.output = {
'title': str(modulation),
'constellation': modulation.constellation,
'labels': [''.join(str(b) for b in komm.int2binlist(modulation.labeling[i], width=modulation.bits_per_symbol)) for i in range(order)],
'gaussian_clouds': recvword,
}
def modulations():
while 1:
print("-----------MODULATION-PROJECT-----------")
print("1. ASK modulation")
print("2. PSK modulation")
print("3. APSK modulation")
print("4. Exit")
action = input()
input_len = 10000 * 3
inputs = [random.choice([0, 1]) for _ in range(input_len)]
if action == '1':
modulation = komm.ASKModulation(4)
print("ASK modulation")
elif action == '2':
modulation = komm.PSKModulation(4)
print("PSK modulation")
elif action == '3':
modulation = komm.APSKModulation((4, 4), (1.0, 2.0))
print("APSK modulation")
elif action == '4':
sys.exit(0)
channel = komm.AWGNChannel(1.0)
modulated = modulation.modulate(inputs)
transmitted = channel(modulated)
demodulated = modulation.demodulate(transmitted)
errors = 0
for d, i in zip(demodulated, inputs):
if d != i:
errors += 1
error_rate = errors / input_len * 100
print(f"Input size: {input_len}")
print(f"Error rate: {error_rate}%")
def main():
tx_im = Image.open("DC4_150x100.pgm")
tx_bin = np.unpackbits(np.array(tx_im))
# make sure every modulation has the same energy per symbol
bpsk = komm.PSKModulation(2)
qam_16 = komm.QAModulation(16, base_amplitudes=1/np.sqrt(10))
qam_256 = komm.QAModulation(256, base_amplitudes=1/np.sqrt(170))
snr = np.arange(0,40,2)
ber_bpsk = snr_ber_simulation(tx_bin, snr, bpsk)
ber_qam_16 = snr_ber_simulation(tx_bin, snr, qam_16)
ber_qam_256 = snr_ber_simulation(tx_bin, snr, qam_256)
# plot all snr vs ber curves
plt.figure()
plt.scatter(snr, ber_bpsk)
plt.plot(snr, ber_bpsk, label = 'BPSK')
plt.scatter(snr, ber_qam_16)
plt.plot(snr, ber_qam_16, label = '16QAM')
plt.scatter(snr, ber_qam_256)
plt.plot(snr, ber_qam_256, label = '256QAM')
plt.xlabel('SNR dB')
plt.ylabel('BER percentage')
plt.title('SNR vs BER')
plt.legend()
plt.yscale('log')
# image recovery
# plt.figure()
# rx_im = np.packbits(rx_bin).reshape(tx_im.size[1],tx_im.size[0])
plt.show()
def psk_constellation_demo(order, amplitude, phase_offset, labeling,
noise_power_db):
psk_modulation = komm.PSKModulation(order, amplitude, phase_offset,
labeling)
constellation_demo(psk_modulation,
noise_power_db,
xlim=[-3.0, 3.0],
ylim=[-3.0, 3.0])
def create_object(self, x):
"""
Creates an instance of the corresponding modulation scheme
Parameters
----------
x : string containing the name of the modulation scheme
Returns
-------
instance of the corresponding modulation scheme
k : bits per symbol
"""
if x == 'BPSK':
k = 1
obj = komm.PSKModulation(2)
if x == 'QPSK':
k = 2
obj = komm.PSKModulation(4, phase_offset=np.pi / 4)
if x == '4-QAM':
k = 2
obj = komm.QAModulation(
4, base_amplitudes=1 / np.sqrt(2.)
) # base_amplitudes is set to a value such that obj.energy_per_symbol becomes unity.
if x == '16-QAM':
k = 4
obj = komm.QAModulation(
16, base_amplitudes=1 / np.sqrt(10.)
) # base_amplitudes is set to a value such that obj.energy_per_symbol becomes unity.
if x == '256-QAM':
k = 8
obj = komm.QAModulation(
256, base_amplitudes=1 / np.sqrt(170.)
) # base_amplitudes is set to a value such that obj.energy_per_symbol becomes unity.
return obj, k
def test_pipeline(image_path, show=True, verbose=True):
psk = komm.PSKModulation(4, phase_offset=np.pi /
4) # defining QPSK modulation
modulated_data = modulate(
psk, image_path=image_path,
verbose=verbose) # loads image from file and uses QPSK on it
received_data = send_data(
modulated_data, snr=0.99,
verbose=verbose) # simulates data transfer with loss
demodulated_data = demodulate(psk, received_data,
verbose=verbose) # demodulates the data
save_image(demodulated_data, "transmission.png",
verbose=verbose) # saves the transmitted image to a file
if show:
show_image(demodulated_data, verbose=verbose)
def main():
tx_im = Image.open("DC4_300x200.pgm")
tx_bin = np.unpackbits(np.array(tx_im))
modulation = komm.PSKModulation(2)
# modulation = komm.QAModulation(256, base_amplitudes= 1/np.sqrt(170))
SNR = np.arange(0, 20, 1)
BER = snr_ber_simulation(tx_bin, SNR, modulation)
plt.figure()
plt.scatter(SNR, BER)
plt.plot(SNR, BER, label='simulation')
# theoraticall curve
BER_theoratical = 1 / 2 * scipy.special.erfc(
np.sqrt(10**(SNR / 10) / modulation.bits_per_symbol))
plt.plot(SNR, BER_theoratical, label='theoratical')
plt.title('SNR vs BER')
plt.yscale('log')
plt.legend()
plt.show()
def main():
Result = []
SNR = 3
modulation = komm.PSKModulation(4)
tx_bin = np.random.randint(2, size = 1200)
# convolutional code
Ccode = komm.ConvolutionalCode(feedforward_polynomials=[[0o7, 0o5]])
tblen = 18
method = "soft"
Cparam = convolution_simulation.sim_param(tx_bin, SNR, modulation, Ccode, tblen, method)
BER = format(convolution_simulation.CCODE_simulation(Cparam), '.4f')
Result.append([BER,'1/2'])
Ccode = komm.ConvolutionalCode(feedforward_polynomials=[[0o155, 0o117]])
tblen = 36
method = "soft"
Cparam = convolution_simulation.sim_param(tx_bin, SNR, modulation, Ccode, tblen, method)
BER = format(convolution_simulation.CCODE_simulation(Cparam), '.4f')
Result.append([BER,'1/2'])
Ccode = komm.ConvolutionalCode(feedforward_polynomials=[[0o155, 0o117, 0o127]])
tblen = 36
method = "soft"
Cparam = convolution_simulation.sim_param(tx_bin, SNR, modulation, Ccode, tblen, method)
BER = format(convolution_simulation.CCODE_simulation(Cparam), '.4f')
Result.append([BER,'1/3'])
# BCH code
BCH_encoder = komm.BCHCode(3, 1)
BCH_param = BCH_simulation.sim_param(tx_bin, SNR, modulation, BCH_encoder)
BER = format(BCH_simulation.sim_BCH(BCH_param), '.4f')
Result.append([BER,'4/7'])
BCH_encoder = komm.BCHCode(4, 3)
BCH_param = BCH_simulation.sim_param(tx_bin, SNR, modulation, BCH_encoder)
BER = format(BCH_simulation.sim_BCH(BCH_param), '.4f')
Result.append([BER,'5/15'])
BCH_encoder = komm.BCHCode(5, 7)
BCH_param = BCH_simulation.sim_param(tx_bin, SNR, modulation, BCH_encoder)
BER = format(BCH_simulation.sim_BCH(BCH_param), '.4f')
Result.append([BER,'6/31'])
BCH_encoder = komm.BCHCode(6, 13)
BCH_param = BCH_simulation.sim_param(tx_bin, SNR, modulation, BCH_encoder)
BER = format(BCH_simulation.sim_BCH(BCH_param), '.4f')
Result.append([BER,'10/63'])
# ARQ
BER = format(ARQ_simulation.ARQ_simulation(tx_bin, modulation, SNR), '.4f')
Result.append([BER, '7/8'])
columns = ['BER','Code rate']
rows = ['(0o7,0o5)','(0o155,0o117)','(0o155,0o117,0o127)','(7,4)BCH','(15,5)BCH','(31,6)BCH','(63,10)BCH','ARQ']
plt.table(cellText = Result, rowLabels = rows, colLabels = columns, loc='center')
plt.axis('tight')
plt.axis('off')
plt.title("Comparison among different FEC code")
plt.show()
def main():
# param specification
SNR = 0
modulation = komm.PSKModulation(4)
tx_bin = np.random.randint(2, size=1200)
decision_method = "soft"
param = []
# Convolution param (0o7,0o5)
tblen = 18
C_code = komm.ConvolutionalCode(feedforward_polynomials=[[0o7, 0o5]])
BCH_code = komm.BCHCode(3, 1)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(4, 3)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(5, 7)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(6, 13)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
# Convolution param (0o155,0o117)
tblen = 36
C_code = komm.ConvolutionalCode(feedforward_polynomials=[[0o155, 0o117]])
BCH_code = komm.BCHCode(3, 1)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(4, 3)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(5, 7)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(6, 13)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
# Convolution param (0o155,0o117,0o127)
tblen = 36
C_code = komm.ConvolutionalCode(
feedforward_polynomials=[[0o155, 0o117, 0o127]])
BCH_code = komm.BCHCode(3, 1)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(4, 3)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(5, 7)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BCH_code = komm.BCHCode(6, 13)
param.append(
con_param(tx_bin, SNR, modulation, BCH_code, C_code, tblen,
decision_method))
BER = []
for i in range(0, len(param), 4):
temp = [
format(sim_CON(param[i]), '.4f'),
format(sim_CON(param[i + 1]), '.4f'),
format(sim_CON(param[i + 2]), '.4f'),
format(sim_CON(param[i + 3]), '.4f')
]
BER.append(temp)
columns = ['(7,4)BCH', '(15,5)BCH', '(31,6)BCH', '(63,10)BCH']
rows = ['(0o7,0o5)', '(0o155,0o117)', '(0o155,0o117,0o127)']
plt.table(cellText=BER, rowLabels=rows, colLabels=columns, loc='center')
plt.axis('tight')
plt.axis('off')
plt.title(
"Comparison among different combination of BCH code and Conv code")
plt.show()
args = parser.parse_args()
script_dir = os.path.abspath(os.path.dirname(__file__))
image_path = os.path.join(script_dir, 'data/DC4_150x100.pgm')
# input image
tx_im = Image.open(image_path)
# number of pixels
Npixels = tx_im.size[1] * tx_im.size[0]
# # plot input image
# plt.figure(1)
# plt.title('Input Image')
# plt.imshow(np.array(tx_im),cmap="gray",vmin=0,vmax=255)
# create an instance of the qpsk modulation scheme
qpsk = komm.PSKModulation(4, phase_offset=np.pi / 4)
# maximim SNR[dB] value
max_snr = 10
num_bits = 8 * Npixels
# list of additive white gaussian noise sources with SNR in range 0 to max_snr
awgn = [komm.AWGNChannel(snr=10**(x / 10.)) for x in range(max_snr)]
# array to store simulated bit error ratio
ber = np.zeros(max_snr)
# array to store signal-to-noise[dB] ratio for the channel
snr = np.zeros(max_snr)
# rate 1/2 code with [0o7, 0o5] as the feedforward polynomial
code, tblen = FEC.ConvCode.create_conv_codes()
def demodulate(self, bods):
symbols = self.getSymbolsFromSignal(bods)
psk = komm.PSKModulation(self.orders[0], self.amplitudes[0],
self.phaseOffsets[0])
return psk.demodulate(symbols)
def modulate(self, bits):
psk = komm.PSKModulation(self.orders[0], self.amplitudes[0],
self.phaseOffsets[0])
return psk.modulate(bits)
def main():
SNR = np.arange(-5, 10, 1)
modulation = komm.PSKModulation(4)
tx_bin = np.random.randint(2, size = 1200)
# new figure
plt.figure()
P = multiprocessing.Pool(len(SNR))
encoder = komm.BCHCode(3, 1) # (7,4) BCH
param = []
for i in range(0,len(SNR)):
param.append(sim_param(tx_bin, SNR[i], modulation, encoder))
BER = P.map(sim_BCH, param)
plt.plot(SNR, BER, label = '(7,4) BCH code')
P.close()
P.join()
P = multiprocessing.Pool(len(SNR))
encoder = komm.BCHCode(4, 3) # (15,5) BCH
param = []
for i in range(0,len(SNR)):
param.append(sim_param(tx_bin, SNR[i], modulation, encoder))
BER = P.map(sim_BCH, param)
plt.plot(SNR, BER, label = '(15,5) BCH code')
P.close()
P.join()
P = multiprocessing.Pool(len(SNR))
encoder = komm.BCHCode(5, 7) # (31,6) BCH
param = []
for i in range(0,len(SNR)):
param.append(sim_param(tx_bin, SNR[i], modulation, encoder))
BER = P.map(sim_BCH, param)
plt.plot(SNR, BER, label = '(31,6) BCH code')
P.close()
P.join()
P = multiprocessing.Pool(len(SNR))
encoder = komm.BCHCode(6, 13) # (63,10) BCH
param = []
for i in range(0,len(SNR)):
param.append(sim_param(tx_bin, SNR[i], modulation, encoder))
BER = P.map(sim_BCH, param)
plt.plot(SNR, BER, label = '(63,10) BCH code')
P.close()
P.join()
P = multiprocessing.Pool(len(SNR))
encoder = -1
param = []
for i in range(0,len(SNR)):
param.append(sim_param(tx_bin, SNR[i], modulation, encoder))
BER = P.map(sim_BCH, param)
plt.plot(SNR, BER, label = 'QPSK without FEC')
P.close()
P.join()
plt.xlabel("SNR")
plt.ylabel("BER")
plt.yscale('log')
plt.legend()
plt.title("Comparison among different BCH codes")
plt.show()
def SimulateBER(snrArray, txBin, Npixels, modulatioInfo):
nSNR = len(snrArray)
rxDataArray = np.empty(len(txBin))
BitErrorArray = np.empty(2)
berArray = np.empty(0)
mod = 0
# Create Modulation Scheme Object
if (modulatioInfo.get("mod") == "PSK"):
mod = komm.PSKModulation(modulatioInfo.get("order"))
if (modulatioInfo.get("mod") == 'QAM'):
mod = komm.QAModulation(modulatioInfo.get("order"))
# Normalize energy per symbol
baseAmplitude = 1 / (np.sqrt(mod.energy_per_symbol))
mod = komm.QAModulation(modulatioInfo.get("order"), baseAmplitude)
print("Modulation to be used:")
print(
str(modulatioInfo.get("order")) + " " + str(modulatioInfo.get("mod")))
print("Bits Per Symbol: " + str(mod.bits_per_symbol))
print("Energy Per Symbol: " + str(mod.energy_per_symbol))
print("\n")
# Modulate Data
txData = mod.modulate(txBin)
# For each transmision
for i in range(nSNR):
# Calculate based on db
awgn = komm.AWGNChannel(snr=10**(snrArray[i] / 10.))
# Simulate noise in channel
rxData = awgn(txData)
# Demodulate Data
rxBin = mod.demodulate(rxData)
# Append demodulated data as a new row
rxDataArray = np.vstack([rxDataArray, rxBin])
awgn = komm.AWGNChannel(snr=10**(snrArray[10] / 10.))
rx_data = awgn(txData)
rx_bin = mod.demodulate(rx_data)
# Plot few rx bits
plt.figure()
plt.axes().set_aspect("equal")
plt.scatter(rx_data[:10000].real, rx_data[:10000].imag, s=1, marker=".")
plt.show()
rx_im = np.packbits(rx_bin).reshape(tx_im.size[1], tx_im.size[0])
plt.figure()
plt.imshow(np.array(rx_im), cmap="gray", vmin=0, vmax=255)
plt.show()
# Measuring Bit Error Ratio
# For each transmision
for j in range(1, nSNR + 1):
# Reset number of bit errors
BitErrorCount = 0
# Compute bit errors
# i.e For each pixel
for i in range(Npixels * 8):
# If pixel value does not match
if (rxDataArray[j][i] != txBin[i]):
# Increment error count
BitErrorCount += 1
# Calculate bit error rate for transmision
ber = BitErrorCount / (Npixels * 8)
berArray = np.append(berArray, ber)
# Append new dimension containing bit count and bit error rate
BitErrorArray = np.vstack([BitErrorArray, [BitErrorCount, ber]])
print("Bit Error Array:")
print(BitErrorArray)
print("\n")
plt.figure()
plt.scatter(snrArray, berArray) #plot points
plt.plot(snrArray, berArray) #plot lines
plt.yscale("log")
plt.ylabel('$BER$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod"))))
plt.grid(True)
#plt.show()
# Calculate theoretical BER
# Modify k parameter i.e. bits per symbol
k = mod.bits_per_symbol
errfcDataSet = np.empty(0)
# For Each SNR
for i in range(nSNR):
# Calculate Theorethical BER
errfc = 0.5 * scipy.special.erfc(
math.sqrt((10**(snrArray[i] / 10)) / k))
errfcDataSet = np.append(errfcDataSet, errfc)
plt.plot(snrArray, errfcDataSet, color='r')
plt.show()
print("Errfc Data Set:")
print(errfcDataSet)
print("\n")
return berArray, errfcDataSet
def SimulateParityBits(snrArray, txBin, Npixels, modulatioInfo):
nSNR = len(snrArray)
rxBinDecoded = np.empty(0)
rxIncorrect = True
mod = 0
if (modulatioInfo.get("mod") == "PSK"):
mod = komm.PSKModulation(modulatioInfo.get("order"))
if (modulatioInfo.get("mod") == 'QAM'):
mod = komm.QAModulation(
modulatioInfo.get("order")) # add baseAmplitude
print("Base Amplitude is: " + str(mod.energy_per_symbol))
# Normalize Enerhy per symbol
baseAmplitude = 1 / (np.sqrt(mod.energy_per_symbol))
print("New Base Amplitude is: " + str(baseAmplitude))
mod = komm.QAModulation(modulatioInfo.get("order"), baseAmplitude)
print("Modulation to be used:")
print(
str(modulatioInfo.get("order")) + " " + str(modulatioInfo.get("mod")))
print("Bits Per Symbol: " + str(mod.bits_per_symbol))
print("Energy Per Symbol: " + str(mod.energy_per_symbol))
print("\n")
print("Simulating ARQ based on parity bit check!")
print("Adding Parity Bits!")
# Add parity bits
# For each pixel
for i in range(Npixels):
startIndex = i * 8
# If the sum of on bits is not even
if (((np.sum(txBin[startIndex:startIndex + 7])) % 2) != 0):
# Change parity bit to 1
txBin[(startIndex + 7)] = 1
# The sum of on bits is even
else:
# Change parity bit to 0
txBin[(startIndex + 7)] = 0
# Modulate data
txDataParity = mod.modulate(txBin)
print("Simulating Transmision!")
indexFactor = int(8 / mod.bits_per_symbol)
berArray = np.empty(0)
arqArray = np.empty(0)
for c in range(nSNR):
print("Simulating SNR: " + str(snrArray[c]))
# Set Average Gausian Noise to reflect new SNR
awgn = komm.AWGNChannel(snr=10**(snrArray[c] / 10.))
ARQ = 0
# For Each Symbol
for i in range(Npixels):
# Compute Index of the codeword
startIndex = i * indexFactor
# Until the Parity bit check is not passed
while (rxIncorrect):
# Simulate noise in the channel during transmision only
rxData = awgn(txDataParity[startIndex:startIndex +
indexFactor])
# Demodulate Data
rxBin = mod.demodulate(rxData)
# Check if parity = 0
if ((np.sum(rxBin) % 2) != 0):
# Error During Transmision
# Increment Request Counter
ARQ += 1
else:
# Passed parity check, assume data is correct
# Append Data Bits to final binary array
rxBinDecoded = np.append(rxBinDecoded, rxBin)
# Set while loop flag to false indicating this codeword has been rx without error
rxIncorrect = False
#Set while loop flag to true to process next codeword
rxIncorrect = True
# Convert to real int
rxBinDecoded = np.real(rxBinDecoded)
rxBinDecoded = rxBinDecoded.astype(int)
# For SNR 10 Plot graphs
if (c == 0):
# Plot few rx bits
# plt.figure()
# plt.axes().set_aspect("equal")
# plt.scatter(rxBinDecoded[:10000].real,rxBinDecoded[:10000].imag,s=1,marker=".")
# plt.show()
rx_im = np.packbits(rxBinDecoded).reshape(tx_im.size[1],
tx_im.size[0])
plt.figure()
plt.imshow(np.array(rx_im), cmap="gray", vmin=0, vmax=255)
plt.show()
# Count Bit errors
print("Computing BER: " + str(snrArray[c]))
BitErrorCount = 0
# For each bit in the rx data
for i in range(Npixels * 8):
# If bit value does not match
if (rxBinDecoded[i] != txBin[i]):
# Increment error count
BitErrorCount += 1
# Calculate bit error rate for the transmision
berArray = np.append(berArray, (BitErrorCount / (Npixels * 8)))
arqArray = np.append(arqArray, (ARQ / (Npixels * 8)))
print("BER Array:")
print(berArray)
print("\n")
print("ARQ Array:")
print(arqArray)
print("\n")
plt.figure()
plt.scatter(snrArray, berArray) #plot points
plt.plot(snrArray, berArray) #plot lines
plt.yscale("log")
plt.ylabel('$BER$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod")) + " BER"))
plt.grid(True)
# Calculate theoretical BER
# Modify k parameter i.e. bits per symbol
k = mod.bits_per_symbol
errfcDataSet = np.empty(0)
# For Each SNR
for i in range(nSNR):
# Calculate Theorethical BER
errfc = 0.5 * scipy.special.erfc(
math.sqrt((10**(snrArray[i] / 10)) / k))
errfcDataSet = np.append(errfcDataSet, errfc)
plt.plot(snrArray, errfcDataSet, color='r')
plt.show()
plt.figure()
plt.scatter(snrArray, arqArray) #plot points
plt.plot(snrArray, arqArray) #plot lines
plt.yscale("log")
plt.ylabel('$ARQ Rate$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod")) + " ARQ/nBits"))
plt.grid(True)
return berArray, arqArray, rxBinDecoded
def main():
# param specification
SNR = np.arange(-5, 10, 1)
modulation = komm.PSKModulation(4)
tx_bin = np.random.randint(2, size=1200)
tblen = 36
# hard decision simulation
decision_method = "hard"
code = komm.ConvolutionalCode(feedforward_polynomials=[[0o7, 0o5]])
BER = []
param = []
p = multiprocessing.dummy.Pool(len(SNR))
for i in range(0, len(SNR)):
temp = sim_param(tx_bin, SNR[i], modulation, code, tblen,
decision_method)
param.append(temp)
BER = p.map(CCODE_simulation, param)
p.close()
p.join()
plt.figure()
plt.title("Comparison between Ccode with soft and hard decision")
plt.yscale('log')
plt.plot(SNR, BER, label='(0o7,0o5) ccode with hard decision')
# soft decision simulation
decision_method = "soft"
code = komm.ConvolutionalCode(feedforward_polynomials=[[0o7, 0o5]])
BER = []
param = []
p = multiprocessing.dummy.Pool(len(SNR))
for i in range(0, len(SNR)):
temp = sim_param(tx_bin, SNR[i], modulation, code, tblen,
decision_method)
param.append(temp)
BER = p.map(CCODE_simulation, param)
p.close()
p.join()
plt.plot(SNR, BER, label='(0o7,0o5) ccode with soft decision')
plt.legend()
plt.xlabel("SNR")
plt.ylabel("BER")
plt.figure()
plt.yscale('log')
plt.title("Comparison among different Ccode with soft decision")
plt.plot(SNR, BER, label='(0o7,0o5) ccode')
# (0o155,0o117) simulation
decision_method = "soft"
code = komm.ConvolutionalCode(feedforward_polynomials=[[0o155, 0o117]])
BER = []
param = []
p = multiprocessing.dummy.Pool(len(SNR))
for i in range(0, len(SNR)):
temp = sim_param(tx_bin, SNR[i], modulation, code, tblen,
decision_method)
param.append(temp)
BER = p.map(CCODE_simulation, param)
p.close()
p.join()
plt.plot(SNR, BER, label='(0o155,0o117) ccode')
# (0o155,0o117,0o127) simulation
decision_method = "soft"
code = komm.ConvolutionalCode(
feedforward_polynomials=[[0o155, 0o117, 0o127]])
BER = []
param = []
p = multiprocessing.dummy.Pool(len(SNR))
for i in range(0, len(SNR)):
temp = sim_param(tx_bin, SNR[i], modulation, code, tblen,
decision_method)
param.append(temp)
BER = p.map(CCODE_simulation, param)
p.close()
p.join()
plt.plot(SNR, BER, label='(0o155,0o117,0o127) ccode')
plt.xlabel("SNR")
plt.ylabel("BER")
plt.legend()
plt.show()
