def run_gfsk_simulation(snr):
u = np.random.randint(2, size=chunkSize)
s = gfsk.modulate(u)
w = channel.noise.awgn(len(s), snr=snr)
r = s+w
hat_U = gfsk.demodulate(r)
wrong, n = common.count_symbol_errors(u, hat_U)
return wrong, n
def feed_data(self, data, snr) -> None:
symbols = self.symbolenc.encode_msb(data)
moded = self.modulator.modulate(symbols)
received = moded + channel.awgn(len(moded), snr)
symbols_hat = self.modulator.demodulate(received)
data_hat = self.symbolenc.decode_msb(symbols_hat)
self.bit_stats += np.array(
common.count_symbol_errors(symbols, symbols_hat))
self.sym_stats += np.array(common.count_bit_errors(data, data_hat))
def test(self):
data = np.arange(100) * 23
data_before = np.copy(data)
data[0] = 23
data[23] = 12
data[3] = 12
data[99] = 12
faults, total = common.count_symbol_errors(data, data_before)
self.assertEqual(total, 100)
self.assertEqual(faults, 4)
branch_snrs = []
for i, gamma in enumerate(snr_todo):
# create channel with the curretn branch and snr
ch = channel.RayleighAWGNChannel(N=branch, snr=gamma)
errors = 0
numberOfBits = 0
for k in range(max_tries):
fram = make_frame_array()
for slot in fram:
signal = gfsk.modulate(slot)
recieved, h = ch.run(signal)
hat_recieved, _ = diversity_technique.selection_from_power(
recieved)
hat_slot = gfsk.demodulate(hat_recieved)
err, n = common.count_symbol_errors(slot, hat_slot)
errors += err
numberOfBits += n
ch.frame_sent()
if errors >= cfg.stop_at_errors:
print(f"Stopping early at {k} runs")
break
ber = errors / numberOfBits
print('snr', gamma, 'branch', branch, 'Prob:', ber)
snrs[j][i] = gamma
probs[j][i] = ber
# Stop when below give_up_value