def transmission(input_bits, qamNum=3):
# qamNum = 3
payloadBits_per_OFDM = 512
mu = getModulation(qamNum)
K = getCarriersNum(qamNum) # number of OFDM subcarriers
log.info(f'K : {K}')
CP = getCP(qamNum) # length of the cyclic prefix: 25% of the block
P = Pilot_Carr[mu] # number of pilot carriers per OFDM block
pilotValue = 3 + 3j # The known value each pilot transmits
allCarriers = getAllCarriers(
qamNum
) # indices of all subcarriers ([0, 1, ... K-1])# Pilots is every (K/P)th carrier.
log.info(f'len(allCarriers) : {len(allCarriers)}')
# For convenience of channel estimation, let's make the last carriers also be a pilot
pilotCarriers = getPilotCarriers(qamNum)
log.info(f'len(pilotCarriers) : {len(pilotCarriers)}')
# data carriers are all remaining carriers
dataCarriers = getDataCarriers(qamNum)
log.info(f'len(dataCarriers) : {len(dataCarriers)}')
carrier_plt = getCarrierPlot(qamNum)
# carrier_plt.show()
bit_num = getbit_num(qamNum) # mu = 4 # bits per symbol (i.e. 16QAM)
# payloadBits_per_OFDM = dataBits_per_OFDM(qamNum) # number of payload bits per OFDM symbol
if qamNum == 1:
QAM = QAM64
elif qamNum == 2:
QAM = QAM32
elif qamNum == 3:
QAM = QAM16
mapping_table = QAM.mapping_table
demapping_table = {v: k for k, v in mapping_table.items()}
H_exact = np.fft.fft(channelResponse, K)
plt.plot(allCarriers, abs(H_exact))
plt.xlabel('Subcarrier index')
plt.ylabel('$|H(f)|$')
plt.grid(True)
plt.xlim(0, K - 1)
log.info(f'payloadBits_per_OFDM : {payloadBits_per_OFDM}')
# bits = np.random.binomial(n=1, p=0.5, size=(payloadBits_per_OFDM, ))
bits = input_bits
log.info(f"Bits count: {len(bits)}")
old_bits = bits.copy()
bits = padding.addPadding(bits, qamNum)
CRC_polynomial = "1001"
log.info(f'len(bits) : {len(bits)}')
log.info(f"Before CRC length = {(len(bits))}")
_, bits_with_crc = sender(''.join(bits.astype(str)), CRC_polynomial)
bits_with_crc = np.array(list(bits_with_crc)).astype(np.uint8)
log.info(f"After CRC length = {(len(bits_with_crc))}")
# log.info(f'Padded Bits: {padded_bits}')
log.info(f'bit_num : {bit_num}')
log.info(f'Remainder: {(len(bits)+3)%bit_num}')
def SP(bits):
return bits.reshape((len(dataCarriers), bit_num))
bits_SP = SP(bits_with_crc)
# log.info ("First 5 bit groups")
# log.info (bits_SP[:5,:])
def Mapping(bits):
return np.array([mapping_table[tuple(b)] for b in bits])
QAM = Mapping(bits_SP)
def OFDM_symbol(QAM_payload):
symbol = np.zeros(K, dtype=complex) # the overall K subcarriers
symbol[pilotCarriers] = pilotValue # allocate the pilot subcarriers
symbol[dataCarriers] = QAM_payload # allocate the pilot subcarriers
return symbol
OFDM_data = OFDM_symbol(QAM)
log.info(f"Number of OFDM carriers in frequency domain: {len(OFDM_data)}")
OFDM_time = np.fft.ifft(OFDM_data)
log.info(
f"Number of OFDM samples in time-domain before CP: {len(OFDM_time)}")
OFDM_withCP = addCP(OFDM_time, qamNum)
log.info(
f"Number of OFDM samples in time domain with CP: {len(OFDM_withCP)}")
OFDM_TX = OFDM_withCP
OFDM_RX = channel(OFDM_TX) #######
rx_tx_plot = plots.recieved_transmitted_signal_plot(OFDM_TX, OFDM_RX)
# rx_tx_plot.show()
OFDM_RX_noCP = removeCP(OFDM_RX, qamNum)
OFDM_demod = np.fft.fft(OFDM_RX_noCP)
Hest = channelEstimate(OFDM_demod, qamNum, pilotValue)
equalized_Hest = equalize(OFDM_demod, Hest)
QAM_est = get_payload(equalized_Hest, qamNum)
plt.plot(QAM_est.real, QAM_est.imag, 'bo')
plt.grid(True)
plt.xlabel('Real part')
plt.ylabel('Imaginary Part')
plt.title("Received constellation")
PS_est, hardDecision = Demapping(QAM_est, qamNum)
decision_mapping_plot = plots.decision_mapping_plot(QAM_est,
hardDecision,
show=False)
bits_est = PS(PS_est)
log.info("Bits est")
log.info(len(bits_est))
log.info(
f"Miscalculated Bits: {np.sum(abs(bits_with_crc-bits_est))}/{len(bits_with_crc)}"
)
log.info(
f"Obtained Bit error rate: {np.sum(abs(bits_with_crc-bits_est))/len(bits_with_crc)}"
)
flag = False
if reciever(''.join(bits_est.astype(str)), CRC_polynomial) == 0:
log.info("No error in data, CRC check passed.")
else:
flag = True
log.error("Error, CRC Check Failed")
num_CRC_bits = len(CRC_polynomial) - 1
bits_est = bits_est[::-1][num_CRC_bits:][::-1]
bits_est_wo_padding = padding.removePadding(bits_est, len(old_bits),
qamNum)
log.info(
f"Miscalculated Bits: {np.sum(abs(old_bits-bits_est_wo_padding))}/{len(old_bits)}"
)
log.info(
f"Obtained Bit error rate: {np.sum(abs(old_bits-bits_est_wo_padding))/len(old_bits)}"
)
return reception(bits_est_wo_padding, flag=flag)
OFDM_time = np.fft.ifft(OFDM_data)
log.info(f"Number of OFDM samples in time-domain before CP: {len(OFDM_time)}")
OFDM_withCP = addCP(OFDM_time, qamNum)
log.info(f"Number of OFDM samples in time domain with CP: {len(OFDM_withCP)}")
OFDM_TX = OFDM_withCP
OFDM_RX = channel(OFDM_TX) #######
rx_tx_plot = plots.recieved_transmitted_signal_plot(OFDM_TX, OFDM_RX)
# rx_tx_plot.show()
OFDM_RX_noCP = removeCP(OFDM_RX, qamNum)
OFDM_demod = np.fft.fft(OFDM_RX_noCP)
Hest = channelEstimate(OFDM_demod, qamNum, pilotValue)
equalized_Hest = equalize(OFDM_demod, Hest)
QAM_est = get_payload(equalized_Hest, qamNum)
plt.plot(QAM_est.real, QAM_est.imag, 'bo')
plt.grid(True)
plt.xlabel('Real part')
plt.ylabel('Imaginary Part')
plt.title("Received constellation")
PS_est, hardDecision = Demapping(QAM_est, qamNum)
decision_mapping_plot = plots.decision_mapping_plot(QAM_est,
hardDecision,
show=False)