def do(self, nb_tx, nb_rx):
prod_nb = nb_tx * nb_rx
# Real channel
chan = MIMOFlatChannel(nb_tx, nb_rx)
assert_allclose(chan.k_factor, 0,
err_msg='k-factor should be 0 with Rayleigh fading in SISO channels')
mean = randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = mean, Rs, Rr
assert_allclose(chan.k_factor, 3,
err_msg='Wrong k-factor with rician fading in SISO channels')
# Complex channel
chan.fading_param = (zeros((nb_rx, nb_tx), complex), identity(nb_tx), identity(nb_rx))
assert_allclose(chan.k_factor, 0,
err_msg='k-factor should be 0 with Rayleigh fading in SISO channels')
mean = randn(nb_rx, nb_tx) + 1j * randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rs, Rr)
assert_allclose(chan.k_factor, 3,
err_msg='Wrong k-factor with rician fading in SISO channels')
def do(self, nb_tx, nb_rx):
def check_noise(mod, chan, corrected_SNR_lin):
msg = choice(mod, self.msg_length)
chan.propagate(msg)
P_msg = signal_power(msg) # previous test asserted that channel neither add nor remove energy
P_noise = signal_power(chan.noises)
assert_allclose(abs(chan.noises.mean()), 0., atol=0.5,
err_msg='Noise mean is not 0')
if corrected_SNR_lin == inf:
assert_allclose(P_noise, 0, atol=1e-2,
err_msg='There is noise that should not be here')
else:
assert_allclose(chan.nb_tx * P_msg / P_noise, corrected_SNR_lin, atol=0.2,
err_msg='Wrong SNR')
fading_param = zeros((nb_rx, nb_tx), complex), identity(nb_tx), identity(nb_rx)
chan = MIMOFlatChannel(nb_tx, nb_rx, fading_param=fading_param)
for mod in self.all_mods:
chan.noise_std = 0
check_noise(mod, chan, inf)
chan.set_SNR_lin(6, Es=signal_power(mod))
check_noise(mod, chan, 6)
chan.set_SNR_lin(6, .5, signal_power(mod))
check_noise(mod, chan, 3)
chan.set_SNR_dB(0, Es=signal_power(mod))
check_noise(mod, chan, 1)
chan.set_SNR_dB(0, .5, signal_power(mod))
check_noise(mod, chan, .5)
def do(self, nb_tx, nb_rx):
def check(chan):
assert_equal(chan.noises, None,
err_msg='Default noises is not None')
assert_equal(chan.channel_gains, None,
err_msg='Default channel gains is not None')
assert_equal(chan.unnoisy_output, None,
err_msg='Default unnoisy output is not None')
chan = MIMOFlatChannel(nb_tx, nb_rx)
# Test output state before any propagation
check(chan)
# Test that noise standard deviation must be set before propagation
with assert_raises(AssertionError):
chan.propagate(array((1, 1)))
# Test output state before any propagation
check(chan)
def do(self, nb_tx, nb_rx):
# Without padding
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
out = chan.propagate(ones(nb_tx * 2))
assert_array_equal(chan.channel_gains.shape, (2, nb_rx, nb_tx),
err_msg='Wrong channel shape without padding')
assert_array_equal(chan.noises.shape, (2, nb_rx),
err_msg='Wrong channel shape without padding')
assert_array_equal(chan.unnoisy_output.shape, (2, nb_rx),
err_msg='Wrong channel shape without padding')
assert_array_equal(out.shape, (2, nb_rx),
err_msg='Wrong channel shape without padding')
# With padding
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
out = chan.propagate(ones(nb_tx * 2 + 1))
assert_array_equal(chan.channel_gains.shape, (3, nb_rx, nb_tx),
err_msg='Wrong channel shape with padding')
assert_array_equal(chan.noises.shape, (3, nb_rx),
err_msg='Wrong channel shape with padding')
assert_array_equal(chan.unnoisy_output.shape, (3, nb_rx),
err_msg='Wrong channel shape with padding')
assert_array_equal(out.shape, (3, nb_rx),
err_msg='Wrong channel shape with padding')
def do(self, nb_tx, nb_rx):
def check_noise(mod, chan, corrected_SNR_lin):
msg = choice(mod, self.msg_length)
chan.propagate(msg)
P_msg = signal_power(
msg
) # previous test asserted that channel neither add nor remove energy
P_noise = signal_power(chan.noises)
assert_allclose(abs(chan.noises.mean()),
0.,
atol=0.5,
err_msg='Noise mean is not 0')
if corrected_SNR_lin == inf:
assert_allclose(
P_noise,
0,
atol=1e-2,
err_msg='There is noise that should not be here')
else:
assert_allclose(chan.nb_tx * P_msg / P_noise,
corrected_SNR_lin,
atol=0.2,
err_msg='Wrong SNR')
fading_param = zeros((nb_rx, nb_tx),
complex), identity(nb_tx), identity(nb_rx)
chan = MIMOFlatChannel(nb_tx, nb_rx, fading_param=fading_param)
for mod in self.all_mods:
chan.noise_std = 0
check_noise(mod, chan, inf)
chan.set_SNR_lin(6, Es=signal_power(mod))
check_noise(mod, chan, 6)
chan.set_SNR_lin(6, .5, signal_power(mod))
check_noise(mod, chan, 3)
chan.set_SNR_dB(0, Es=signal_power(mod))
check_noise(mod, chan, 1)
chan.set_SNR_dB(0, .5, signal_power(mod))
check_noise(mod, chan, .5)
def do(self, nb_tx, nb_rx):
def check_chan_gain(mod, chan):
msg = choice(mod, self.msg_length)
chan.propagate(msg)
P_msg = signal_power(msg)
P_unnoisy = signal_power(chan.unnoisy_output)
assert_allclose(P_unnoisy,
P_msg * chan.nb_tx,
rtol=0.2,
err_msg='Channel add or remove energy')
# Test value checking in constructor construction
with assert_raises(ValueError):
MIMOFlatChannel(nb_tx, nb_tx, 0, (ones(
(nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx))))
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
prod_nb = nb_tx * nb_rx
# Test on real channel
for mod in self.real_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones(
(nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros(
(nb_rx, nb_tx)), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(),
0,
atol=1e-2,
err_msg='Wrong channel mean with real channel')
assert_allclose(chan.channel_gains.var(),
1,
atol=5e-2,
err_msg='Wrong channel variance with real channel')
# Test with rician fading
mean = randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 /
einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rs, Rr)
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(0),
mean,
atol=0.2,
err_msg='Wrong channel mean with real channel')
# Test on all channel
for mod in self.all_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones(
(nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros(
(nb_rx, nb_tx), complex), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(),
0,
atol=1e-2,
err_msg='Wrong channel mean with complex channel')
assert_allclose(
chan.channel_gains.var(),
1,
atol=5e-2,
err_msg='Wrong channel variance with complex channel')
# Test with rician fading
mean = randn(nb_rx, nb_tx) + 1j * randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 /
einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rs, Rr)
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(0).real,
mean.real,
atol=0.1,
err_msg='Wrong channel mean with complex channel')
assert_allclose(chan.channel_gains.mean(0).imag,
mean.imag,
atol=0.1,
err_msg='Wrong channel mean with complex channel')
def do(self, nb_tx, nb_rx):
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
with assert_raises(TypeError):
chan.propagate(array((1, 1j)))
def do(self, nb_tx, nb_rx):
# Set seed
seed(17121996)
def check_chan_gain(mod, chan):
msg = choice(mod, self.msg_length)
chan.propagate(msg)
P_msg = signal_power(msg)
P_unnoisy = signal_power(chan.unnoisy_output)
assert_allclose(P_unnoisy, P_msg * chan.nb_tx, rtol=0.2,
err_msg='Channel add or remove energy')
def expo_correlation(t, r):
# Construct the exponent matrix
expo_tx = fromiter((j - i for i in range(chan.nb_tx) for j in range(chan.nb_tx)), int, chan.nb_tx ** 2)
expo_rx = fromiter((j - i for i in range(chan.nb_rx) for j in range(chan.nb_rx)), int, chan.nb_rx ** 2)
# Reshape
expo_tx = expo_tx.reshape(chan.nb_tx, chan.nb_tx)
expo_rx = expo_rx.reshape(chan.nb_rx, chan.nb_rx)
return t ** expo_tx, r ** expo_rx
def check_correlation(chan, Rt, Rr):
nb_ant = chan.nb_tx * chan.nb_rx
Rdes = kron(Rt, Rr)
H = chan.channel_gains
Ract = zeros_like(Rdes)
for i in range(len(H)):
Ract += H[i].T.reshape(nb_ant, 1).dot(H[i].T.reshape(1, nb_ant).conj())
Ract /= len(H)
assert_allclose(Rdes, Ract, atol=0.05,
err_msg='Wrong correlation matrix')
# Test value checking in constructor construction
with assert_raises(ValueError):
MIMOFlatChannel(nb_tx, nb_tx, 0, (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx))))
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
prod_nb = nb_tx * nb_rx
# Test on real channel
for mod in self.real_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros((nb_rx, nb_tx)), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
# Test with rician fading
mean = randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rt = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rt, Rr)
check_chan_gain(mod, chan)
# Test helper functions
chan.uncorr_rayleigh_fading(float)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 0,
err_msg='Wrong k-factor with uncorrelated Rayleigh fading')
mean = randn(nb_rx, nb_tx)
chan.uncorr_rician_fading(mean, 10)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 10,
err_msg='Wrong k-factor with uncorrelated rician fading')
# Test on complex channel
for mod in self.all_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros((nb_rx, nb_tx), complex), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(), 0, atol=1e-2,
err_msg='Wrong channel mean with complex channel')
assert_allclose(chan.channel_gains.var(), 1, atol=5e-2,
err_msg='Wrong channel variance with complex channel')
# Test with rician fading
mean = randn(nb_rx, nb_tx) + 1j * randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rt = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rt, Rr)
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(0).real, mean.real, atol=0.1,
err_msg='Wrong channel mean with complex channel')
assert_allclose(chan.channel_gains.mean(0).imag, mean.imag, atol=0.1,
err_msg='Wrong channel mean with complex channel')
# Test helper functions
chan.uncorr_rayleigh_fading(complex)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 0,
err_msg='Wrong k-factor with uncorrelated Rayleigh fading')
mean = randn(nb_rx, nb_tx) + randn(nb_rx, nb_tx) * 1j
chan.uncorr_rician_fading(mean, 10)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 10,
err_msg='Wrong k-factor with uncorrelated rician fading')
chan.expo_corr_rayleigh_fading(exp(-0.2j * pi), exp(-0.1j * pi))
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 0,
err_msg='Wrong k-factor with correlated Rayleigh fading')
Rt, Rr = expo_correlation(exp(-0.2j * pi), exp(-0.1j * pi))
check_correlation(chan, Rt, Rr)
mean = randn(nb_rx, nb_tx) + randn(nb_rx, nb_tx) * 1j
chan.expo_corr_rician_fading(mean, 10, exp(-0.1j * pi), exp(-0.2j * pi))
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 10,
err_msg='Wrong k-factor with correlated rician fading')
# Test with beta > 0
chan.expo_corr_rayleigh_fading(exp(-0.2j * pi), exp(-0.1j * pi), 1, 0.5)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 0,
err_msg='Wrong k-factor with correlated Rayleigh fading')
mean = randn(nb_rx, nb_tx) + randn(nb_rx, nb_tx) * 1j
chan.expo_corr_rician_fading(mean, 5, exp(-0.1j * pi), exp(-0.2j * pi), 3, 2)
check_chan_gain(mod, chan)
assert_allclose(chan.k_factor, 5,
err_msg='Wrong k-factor with correlated rician fading')
def test_link_performance():
# Set seed
seed(8071996)
######################################
# Build models & desired solutions
######################################
models = []
desired_bers = []
snr_range = []
labels = []
rtols = []
code_rates = []
# SISO QPSK and AWGN channel
QPSK = QAMModem(4)
def receiver(y, h, constellation, noise_var):
return QPSK.demodulate(y, 'hard')
models.append(
LinkModel(QPSK.modulate, SISOFlatChannel(fading_param=(1 + 0j, 0)),
receiver, QPSK.num_bits_symbol, QPSK.constellation, QPSK.Es))
snr_range.append(arange(0, 9, 2))
desired_bers.append(erfc(sqrt(10**(snr_range[-1] / 10) / 2)) / 2)
labels.append('SISO QPSK and AWGN channel')
rtols.append(.25)
code_rates.append(1)
# MIMO 16QAM, 4x4 Rayleigh channel and hard-output K-Best
QAM16 = QAMModem(16)
RayleighChannel = MIMOFlatChannel(4, 4)
RayleighChannel.uncorr_rayleigh_fading(complex)
def receiver(y, h, constellation, noise_var):
return QAM16.demodulate(kbest(y, h, constellation, 16), 'hard')
models.append(
LinkModel(QAM16.modulate, RayleighChannel, receiver,
QAM16.num_bits_symbol, QAM16.constellation, QAM16.Es))
snr_range.append(arange(0, 21, 5) + 10 * log10(QAM16.num_bits_symbol))
desired_bers.append((2e-1, 1e-1, 3e-2, 2e-3, 4e-5)) # From reference
labels.append('MIMO 16QAM, 4x4 Rayleigh channel and hard-output K-Best')
rtols.append(1.25)
code_rates.append(1)
# MIMO 16QAM, 4x4 Rayleigh channel and soft-output best-first
QAM16 = QAMModem(16)
RayleighChannel = MIMOFlatChannel(4, 4)
RayleighChannel.uncorr_rayleigh_fading(complex)
ldpc_params = get_ldpc_code_params(
'commpy/channelcoding/designs/ldpc/wimax/1440.720.txt', True)
def modulate(bits):
return QAM16.modulate(
triang_ldpc_systematic_encode(bits, ldpc_params,
False).reshape(-1, order='F'))
def decoder(llrs):
return ldpc_bp_decode(llrs, ldpc_params, 'MSA',
15)[0][:720].reshape(-1, order='F')
def demode(symbs):
return QAM16.demodulate(symbs, 'hard')
def receiver(y, h, constellation, noise_var):
return best_first_detector(y, h, constellation, (1, 3, 5), noise_var,
demode, 500)
models.append(
LinkModel(modulate, RayleighChannel, receiver, QAM16.num_bits_symbol,
QAM16.constellation, QAM16.Es, decoder, 0.5))
snr_range.append(arange(17, 20, 1))
desired_bers.append((1.7e-1, 1e-1, 2.5e-3)) # From reference
labels.append(
'MIMO 16QAM, 4x4 Rayleigh channel and soft-output best-first')
rtols.append(2)
code_rates.append(.5)
######################################
# Make tests
######################################
for test in range(len(models)):
BERs = link_performance(models[test], snr_range[test], 5e5, 200, 720,
models[test].rate)
assert_allclose(BERs,
desired_bers[test],
rtol=rtols[test],
err_msg='Wrong performance for ' + labels[test])
full_metrics = models[test].link_performance_full_metrics(
snr_range[test], 2500, 200, 720, models[test].rate)
assert_allclose(full_metrics[0],
desired_bers[test],
rtol=rtols[test],
err_msg='Wrong performance for ' + labels[test])
def do(self, nb_tx, nb_rx):
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
with assert_raises(TypeError):
chan.propagate(array((1, 1j)))
def do(self, nb_tx, nb_rx):
def check_chan_gain(mod, chan):
msg = choice(mod, self.msg_length)
chan.propagate(msg)
P_msg = signal_power(msg)
P_unnoisy = signal_power(chan.unnoisy_output)
assert_allclose(P_unnoisy, P_msg * chan.nb_tx, rtol=0.2,
err_msg='Channel add or remove energy')
# Test value checking in constructor construction
with assert_raises(ValueError):
MIMOFlatChannel(nb_tx, nb_tx, 0, (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx))))
chan = MIMOFlatChannel(nb_tx, nb_rx, 0)
prod_nb = nb_tx * nb_rx
# Test on real channel
for mod in self.real_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros((nb_rx, nb_tx)), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(), 0, atol=1e-2,
err_msg='Wrong channel mean with real channel')
assert_allclose(chan.channel_gains.var(), 1, atol=5e-2,
err_msg='Wrong channel variance with real channel')
# Test with rician fading
mean = randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rs, Rr)
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(0), mean, atol=0.2,
err_msg='Wrong channel mean with real channel')
# Test on all channel
for mod in self.all_mods:
# Test value checking after construction
with assert_raises(ValueError):
chan.fading_param = (ones((nb_tx, nb_tx)), ones((nb_tx, nb_tx)), ones((nb_rx, nb_rx)))
# Test with Rayleigh fading
chan.fading_param = (zeros((nb_rx, nb_tx), complex), identity(nb_tx), identity(nb_rx))
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(), 0, atol=1e-2,
err_msg='Wrong channel mean with complex channel')
assert_allclose(chan.channel_gains.var(), 1, atol=5e-2,
err_msg='Wrong channel variance with complex channel')
# Test with rician fading
mean = randn(nb_rx, nb_tx) + 1j * randn(nb_rx, nb_tx)
mean *= sqrt(prod_nb * 0.75 / einsum('ij,ij->', absolute(mean), absolute(mean)))
Rs = self.random_SDP_matrix(nb_tx) * sqrt(prod_nb) * 0.5
Rr = self.random_SDP_matrix(nb_rx) * sqrt(prod_nb) * 0.5
chan.fading_param = (mean, Rs, Rr)
check_chan_gain(mod, chan)
assert_allclose(chan.channel_gains.mean(0).real, mean.real, atol=0.1,
err_msg='Wrong channel mean with complex channel')
assert_allclose(chan.channel_gains.mean(0).imag, mean.imag, atol=0.1,
err_msg='Wrong channel mean with complex channel')