def main():
# session = bp.Session(load_from_config=False)
# bp.output_server(docname='simple_precoded_srs_AN1', session=session)
bp.output_file('simple_precoded_srs.html', title="Simple Precoded SRS")
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Scenario Description xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# 3 Base Stations, each sending data to its own user while interfering
# with the other users.
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Configuration xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
num_prbs = 25 # Number of PRBs to simulate
Nsc = 12 * num_prbs # Number of subcarriers
Nzc = 149 # Size of the sequence
u1 = 1 # Root sequence index of the first user
u2 = 2 # Root sequence index of the first user
u3 = 3 # Root sequence index of the first user
numAnAnt = 4 # Number of Base station antennas
numUeAnt = 2 # Number of UE antennas
num_samples = 1 # Number of simulated channel samples (from
# Jakes process)
# Channel configuration
speedTerminal = 0 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
timeTTIDbl = 1e-3 # Time of a single TTI
subcarrierBandDbl = 15e3 # Subcarrier bandwidth (in Hz)
numOfSubcarriersPRBInt = 12 # Number of subcarriers in each PRB
L = 16 # The number of rays for the Jakes model.
# Dependent parameters
lambdaDbl = 3e8 / fcDbl # Carrier wave length
Fd = speedTerminal / lambdaDbl # Doppler Frequency
Ts = 1. / (Nsc * subcarrierBandDbl) # Sampling time
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Generate the root sequence xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
a_u1 = get_extended_ZF(calcBaseZC(Nzc, u1), Nsc / 2)
a_u2 = get_extended_ZF(calcBaseZC(Nzc, u2), Nsc / 2)
a_u3 = get_extended_ZF(calcBaseZC(Nzc, u3), Nsc / 2)
print("Nsc: {0}".format(Nsc))
print("a_u.shape: {0}".format(a_u1.shape))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Create shifted sequences for 3 users xxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# We arbitrarily choose some cyclic shift index and then we call
# zadoffchu.get_srs_seq to get the shifted sequence.
shift_index = 4
r1 = get_srs_seq(a_u1, shift_index)
r2 = get_srs_seq(a_u2, shift_index)
r3 = get_srs_seq(a_u3, shift_index)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Generate channels from users to the BS xxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
jakes_all_links = np.empty([3, 3], dtype=object)
tdlchannels_all_links = np.empty([3, 3], dtype=object)
impulse_responses = np.empty([3, 3], dtype=object)
# Dimension: `UEs x ANs x num_subcarriers x numUeAnt x numAnAnt`
freq_responses = np.empty([3, 3, Nsc, numUeAnt, numAnAnt], dtype=complex)
for ueIdx in range(3):
for anIdx in range(3):
jakes_all_links[ueIdx,
anIdx] = JakesSampleGenerator(Fd,
Ts,
L,
shape=(numUeAnt,
numAnAnt))
tdlchannels_all_links[ueIdx, anIdx] = TdlChannel(
jakes_all_links[ueIdx, anIdx],
tap_powers_dB=COST259_TUx.tap_powers_dB,
tap_delays=COST259_TUx.tap_delays)
tdlchannels_all_links[ueIdx,
anIdx].generate_impulse_response(num_samples)
impulse_responses[ueIdx, anIdx] \
= tdlchannels_all_links[
ueIdx, anIdx].get_last_impulse_response()
freq_responses[ueIdx, anIdx] = \
impulse_responses[ueIdx, anIdx].get_freq_response(Nsc)[:, :, :,
0]
# xxxxxxxxxx Channels in downlink direction xxxxxxxxxxxxxxxxxxxxxxxxxxx
# Dimension: `Nsc x numUeAnt x numAnAnt`
dH11 = freq_responses[0, 0]
dH12 = freq_responses[0, 1]
dH13 = freq_responses[0, 2]
dH21 = freq_responses[1, 0]
dH22 = freq_responses[1, 1]
dH23 = freq_responses[1, 2]
dH31 = freq_responses[2, 0]
dH32 = freq_responses[2, 1]
dH33 = freq_responses[2, 2]
# xxxxxxxxxx Principal dimension in downlink direction xxxxxxxxxxxxxxxx
sc_idx = 124 # Index of the subcarrier we are interested in
[dU11, _, _] = np.linalg.svd(dH11[sc_idx])
[dU22, _, _] = np.linalg.svd(dH22[sc_idx])
[dU33, _, _] = np.linalg.svd(dH33[sc_idx])
# xxxxxxxxxx Users precoders xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Users' precoders are the main column of the U matrix
F11 = dU11[:, 0].conj()
F22 = dU22[:, 0].conj()
F33 = dU33[:, 0].conj()
# xxxxxxxxxx Channels in uplink direction xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# int_path_loss = 0.05 # Path loss of the interfering links
# int_g = math.sqrt(int_path_loss) # Gain of interfering links
# dir_d = 1.0 # Gain of direct links
pl = np.array([[2.21e-08, 2.14e-09, 1.88e-08],
[3.45e-10, 2.17e-08, 4.53e-10],
[4.38e-10, 8.04e-10, 4.75e-08]])
# pl = np.array([[ 1, 0.1, 0.1],
# [ 0.1, 1, 0.1],
# [ 0.1, 0.1, 1]])
# Dimension: `Nsc x numAnAnt x numUeAnt`
uH11 = math.sqrt(pl[0, 0]) * np.transpose(dH11, axes=[0, 2, 1])
uH12 = math.sqrt(pl[0, 1]) * np.transpose(dH12, axes=[0, 2, 1])
uH13 = math.sqrt(pl[0, 2]) * np.transpose(dH13, axes=[0, 2, 1])
uH21 = math.sqrt(pl[1, 0]) * np.transpose(dH21, axes=[0, 2, 1])
uH22 = math.sqrt(pl[1, 1]) * np.transpose(dH22, axes=[0, 2, 1])
uH23 = math.sqrt(pl[1, 2]) * np.transpose(dH23, axes=[0, 2, 1])
uH31 = math.sqrt(pl[2, 0]) * np.transpose(dH31, axes=[0, 2, 1])
uH32 = math.sqrt(pl[2, 1]) * np.transpose(dH32, axes=[0, 2, 1])
uH33 = math.sqrt(pl[2, 2]) * np.transpose(dH33, axes=[0, 2, 1])
# Compute the equivalent uplink channels
uH11_eq = uH11.dot(F11)
uH12_eq = uH12.dot(F22)
uH13_eq = uH13.dot(F33)
uH21_eq = uH21.dot(F11)
uH22_eq = uH22.dot(F22)
uH23_eq = uH23.dot(F33)
uH31_eq = uH31.dot(F11)
uH32_eq = uH32.dot(F22)
uH33_eq = uH33.dot(F33)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Compute Received Signals xxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Calculate the received signals
comb_indexes = np.r_[0:Nsc:2]
Y1_term11 = uH11_eq[comb_indexes] * r1[:, np.newaxis]
Y1_term12 = uH12_eq[comb_indexes] * r2[:, np.newaxis]
Y1_term13 = uH13_eq[comb_indexes] * r3[:, np.newaxis]
Y1 = Y1_term11 + Y1_term12 + Y1_term13
Y2_term21 = uH21_eq[comb_indexes] * r1[:, np.newaxis]
Y2_term22 = uH22_eq[comb_indexes] * r2[:, np.newaxis]
Y2_term23 = uH23_eq[comb_indexes] * r3[:, np.newaxis]
Y2 = Y2_term21 + Y2_term22 + Y2_term23
Y3_term31 = uH31_eq[comb_indexes] * r1[:, np.newaxis]
Y3_term32 = uH32_eq[comb_indexes] * r2[:, np.newaxis]
Y3_term33 = uH33_eq[comb_indexes] * r3[:, np.newaxis]
Y3 = Y3_term31 + Y3_term32 + Y3_term33
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Estimate the equivalent channel xxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
(uH11_eq_est, uH12_eq_est, uH13_eq_est, uH21_eq_est, uH22_eq_est,
uH23_eq_est, uH31_eq_est, uH32_eq_est,
uH33_eq_est) = estimate_channels_remove_only_direct(
Y1, Y2, Y3, r1, r2, r3, Nsc, comb_indexes)
(uH11_eq_est_SIC, uH12_eq_est_SIC, uH13_eq_est_SIC, uH21_eq_est_SIC,
uH22_eq_est_SIC, uH23_eq_est_SIC, uH31_eq_est_SIC, uH32_eq_est_SIC,
uH33_eq_est_SIC) = estimate_channels_remove_direct_and_perform_SIC(
Y1, Y2, Y3, r1, r2, r3, Nsc, comb_indexes)
# Compute the MSE reduction due to SIC
improve11 = compute_channel_estimation_error_dB(
uH11_eq, uH11_eq_est) - compute_channel_estimation_error_dB(
uH11_eq, uH11_eq_est_SIC)
improve12 = compute_channel_estimation_error_dB(
uH12_eq, uH12_eq_est) - compute_channel_estimation_error_dB(
uH12_eq, uH12_eq_est_SIC)
improve13 = compute_channel_estimation_error_dB(
uH13_eq, uH13_eq_est) - compute_channel_estimation_error_dB(
uH13_eq, uH13_eq_est_SIC)
improve21 = compute_channel_estimation_error_dB(
uH21_eq, uH21_eq_est) - compute_channel_estimation_error_dB(
uH21_eq, uH21_eq_est_SIC)
improve22 = compute_channel_estimation_error_dB(
uH22_eq, uH22_eq_est) - compute_channel_estimation_error_dB(
uH22_eq, uH22_eq_est_SIC)
improve23 = compute_channel_estimation_error_dB(
uH23_eq, uH23_eq_est) - compute_channel_estimation_error_dB(
uH23_eq, uH23_eq_est_SIC)
improve31 = compute_channel_estimation_error_dB(
uH31_eq, uH31_eq_est) - compute_channel_estimation_error_dB(
uH31_eq, uH31_eq_est_SIC)
improve32 = compute_channel_estimation_error_dB(
uH32_eq, uH32_eq_est) - compute_channel_estimation_error_dB(
uH32_eq, uH32_eq_est_SIC)
improve33 = compute_channel_estimation_error_dB(
uH33_eq, uH33_eq_est) - compute_channel_estimation_error_dB(
uH33_eq, uH33_eq_est_SIC)
print(improve11)
print(improve12)
print(improve13)
print(improve21)
print(improve22)
print(improve23)
print(improve31)
print(improve32)
print(improve33)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxx Plot the true and estimated channels xxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
p1 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH11_eq, uH11_eq_est, title='Direct Channel from UE1 to AN1')
p2 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH12_eq, uH12_eq_est, title='Interfering Channel from UE2 to AN1')
p3 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH13_eq, uH13_eq_est, title='Interfering Channel from UE3 to AN1')
tab1 = bw.Panel(child=p1, title="UE1 to AN1")
tab2 = bw.Panel(child=p2, title="UE2 to AN1")
tab3 = bw.Panel(child=p3, title="UE3 to AN1")
tabs_an1 = bw.Tabs(tabs=[tab1, tab2, tab3])
p1 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH21_eq, uH21_eq_est, title='Interfering Channel from UE1 to AN2')
p2 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH22_eq, uH22_eq_est, title='Direct Channel from UE2 to AN2')
p3 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH23_eq, uH23_eq_est, title='Interfering Channel from UE3 to AN2')
tab1 = bw.Panel(child=p1, title="UE1 to AN2")
tab2 = bw.Panel(child=p2, title="UE2 to AN2")
tab3 = bw.Panel(child=p3, title="UE3 to AN2")
tabs_an2 = bw.Tabs(tabs=[tab1, tab2, tab3])
p1 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH31_eq, uH31_eq_est, title='Interfering Channel from UE1 to AN3')
p2 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH32_eq, uH32_eq_est, title='Interfering Channel from UE2 to AN3')
p3 = plot_true_and_estimated_channel_with_bokeh_all_antennas(
uH33_eq, uH33_eq_est, title='Direct Channel from UE3 to AN3')
tab1 = bw.Panel(child=p1, title="UE1 to AN3")
tab2 = bw.Panel(child=p2, title="UE2 to AN3")
tab3 = bw.Panel(child=p3, title="UE3 to AN3")
tabs_an3 = bw.Tabs(tabs=[tab1, tab2, tab3])
# Put each AN tab as a panel of an "ANs tab" and show it
tabs1 = bw.Panel(child=tabs_an1, title="AN1")
tabs2 = bw.Panel(child=tabs_an2, title="AN2")
tabs3 = bw.Panel(child=tabs_an3, title="AN3")
tabs_all = bw.Tabs(tabs=[tabs1, tabs2, tabs3])
bp.show(tabs_all)
input_data = np.random.random_integers(0, 4 - 1, num_symbols)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx QPSK and OFDM Modulators xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
qpsk_obj = QPSK()
ofdm_obj = OFDM(fft_size=fft_size, cp_size=cp_size,
num_used_subcarriers=num_used_subcarriers)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Modulate data xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
modulated_data = qpsk_obj.modulate(input_data)
transmit_data = ofdm_obj.modulate(modulated_data)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Transmit data through the channel xxxxxxxxxxxxxxxxxxxxxxxx
jakes = JakesSampleGenerator(Fd, Ts, L)
channel = SuChannel(jakes, channel_profile)
channel_memory = channel.num_taps_with_padding - 1
received_data = channel.corrupt_data(transmit_data)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx OFDM Demodulate data xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
ofdm_received_data = ofdm_obj.demodulate(received_data[:-channel_memory])
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx One-Tap Equalization xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# First we need to get the frequency response
impulse_response = channel.get_last_impulse_response()
freq_response = impulse_response.get_freq_response(fft_size)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
def test_estimate_channel_multiple_rx(self):
Nsc = 24
size = Nsc
Nr = 3 # Number of receive antennas
num_taps_to_keep = 15
cover_codes = [np.array([-1, 1]), np.array([1, 1])]
user1_seq = DmrsUeSequence(RootSequence(root_index=25, size=size),
1,
cover_code=cover_codes[0])
user2_seq = DmrsUeSequence(RootSequence(root_index=25, size=size),
4,
cover_code=cover_codes[0])
ue1_channel_estimator = CazacBasedWithOCCChannelEstimator(user1_seq)
speed_terminal = 3 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
subcarrier_bandwidth = 15e3 # Subcarrier bandwidth (in Hz)
wave_length = 3e8 / fcDbl # Carrier wave length
Fd = speed_terminal / wave_length # Doppler Frequency
Ts = 1. / (Nsc * subcarrier_bandwidth) # Sampling interval
L = 16 # Number of jakes taps
# Create the fading generators and set multiple receive antennas
jakes1 = JakesSampleGenerator(Fd, Ts, L, shape=(Nr, 1))
jakes2 = JakesSampleGenerator(Fd, Ts, L, shape=(Nr, 1))
# Create a TDL channel object for each user
tdlchannel1 = TdlChannel(jakes1, channel_profile=COST259_TUx)
tdlchannel2 = TdlChannel(jakes2, channel_profile=COST259_TUx)
# Generate channel that would corrupt the transmit signal.
tdlchannel1.generate_impulse_response(1)
tdlchannel2.generate_impulse_response(1)
# Get the generated impulse response
impulse_response1 = tdlchannel1.get_last_impulse_response()
impulse_response2 = tdlchannel2.get_last_impulse_response()
# Get the corresponding frequency response
freq_resp_1 = impulse_response1.get_freq_response(Nsc)
H1 = freq_resp_1[:, :, 0, 0].T
freq_resp_2 = impulse_response2.get_freq_response(Nsc)
H2 = freq_resp_2[:, :, 0, 0].T
# Sequence of the users
r1 = user1_seq.seq_array()
r2 = user2_seq.seq_array()
# Received signal (in frequency domain) of user 1
Y1 = H1[:, np.newaxis, :] * r1[np.newaxis, :, :]
Y2 = H2[:, np.newaxis, :] * r2[np.newaxis, :, :]
Y = Y1 + Y2 # Dimension: `Nr x cover_code_size x num_elements`
# Calculate expected estimated channel for user 1
cover_code1 = cover_codes[0]
Y_with_cover_code = \
(cover_code1[0] * Y[:,0,:] + cover_code1[1] * Y[:,1,:]) / 2.0
":type: np.ndarray"
r1_no_cover_code = r1[0] * cover_code1[0]
y1 = np.fft.ifft(np.conj(r1_no_cover_code[np.newaxis]) *
Y_with_cover_code,
size,
axis=1)
tilde_h1_espected = y1[:, 0:(num_taps_to_keep + 1)]
tilde_H1_espected = np.fft.fft(tilde_h1_espected, Nsc, axis=1)
# Test the CazacBasedWithOCCChannelEstimator estimation
H1_estimated = ue1_channel_estimator.estimate_channel_freq_domain(
Y, num_taps_to_keep, extra_dimension=True)
np.testing.assert_array_almost_equal(H1_estimated, tilde_H1_espected)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H1 - tilde_H1_espected)
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.shape),
decimal=2)
def test_estimate_channel_with_dmrs(self):
Nsc = 24
size = Nsc
cover_codes = [np.array([-1, 1]), np.array([1, 1])]
user1_seq = DmrsUeSequence(root_seq=RootSequence(root_index=17,
size=size),
n_cs=1,
cover_code=cover_codes[0])
user2_seq = DmrsUeSequence(root_seq=RootSequence(root_index=17,
size=size),
n_cs=4,
cover_code=cover_codes[1])
ue1_channel_estimator = CazacBasedWithOCCChannelEstimator(user1_seq)
speed_terminal = 3 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
subcarrier_bandwidth = 15e3 # Subcarrier bandwidth (in Hz)
wave_length = 3e8 / fcDbl # Carrier wave length
Fd = speed_terminal / wave_length # Doppler Frequency
Ts = 1. / (Nsc * subcarrier_bandwidth) # Sampling interval
L = 16 # Number of jakes taps
jakes1 = JakesSampleGenerator(Fd, Ts, L)
jakes2 = JakesSampleGenerator(Fd, Ts, L)
# Create a TDL channel object for each user
tdlchannel1 = TdlChannel(jakes1, channel_profile=COST259_TUx)
tdlchannel2 = TdlChannel(jakes2, channel_profile=COST259_TUx)
# Generate channel that would corrupt the transmit signal.
tdlchannel1.generate_impulse_response(1)
tdlchannel2.generate_impulse_response(1)
# Get the generated impulse response
impulse_response1 = tdlchannel1.get_last_impulse_response()
impulse_response2 = tdlchannel2.get_last_impulse_response()
# Get the corresponding frequency response
freq_resp_1 = impulse_response1.get_freq_response(Nsc)
H1 = freq_resp_1[:, 0]
freq_resp_2 = impulse_response2.get_freq_response(Nsc)
H2 = freq_resp_2[:, 0]
# Sequence of the users
r1 = user1_seq.seq_array()
r2 = user2_seq.seq_array()
# Received signal (in frequency domain) of user 1
Y1 = H1 * r1
Y2 = H2 * r2
Y = Y1 + Y2
# Calculate expected estimated channel for user 1
cover_code1 = cover_codes[0]
Y_with_cover_code = \
(cover_code1[0] * Y[0] + cover_code1[1] * Y[1]) / 2.0
":type: np.ndarray"
r1_no_cover_code = r1[0] * cover_code1[0]
y1 = np.fft.ifft(np.conj(r1_no_cover_code) * Y_with_cover_code, size)
tilde_h1 = y1[0:4]
tilde_H1 = np.fft.fft(tilde_h1, Nsc)
# Test the CazacBasedWithOCCChannelEstimator estimation
np.testing.assert_array_almost_equal(
ue1_channel_estimator.estimate_channel_freq_domain(
Y, 3, extra_dimension=True), tilde_H1)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H1 - tilde_H1)
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.size),
decimal=2)
def test_estimate_channel_multiple_rx(self):
Nsc = 300 # 300 subcarriers
size = Nsc // 2
Nzc = 139
user1_seq = SrsUeSequence(RootSequence(root_index=25,
size=size,
Nzc=Nzc),
1,
normalize=True)
user2_seq = SrsUeSequence(RootSequence(root_index=25,
size=size,
Nzc=Nzc),
4,
normalize=True)
ue1_channel_estimator = CazacBasedChannelEstimator(user1_seq)
speed_terminal = 3 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
subcarrier_bandwidth = 15e3 # Subcarrier bandwidth (in Hz)
wave_length = 3e8 / fcDbl # Carrier wave length
Fd = speed_terminal / wave_length # Doppler Frequency
Ts = 1. / (Nsc * subcarrier_bandwidth) # Sampling interval
L = 16 # Number of jakes taps
# Create the fading generators and set multiple receive antennas
jakes1 = JakesSampleGenerator(Fd, Ts, L, shape=(3, 1))
jakes2 = JakesSampleGenerator(Fd, Ts, L, shape=(3, 1))
# Create a TDL channel object for each user
tdlchannel1 = TdlChannel(jakes1, channel_profile=COST259_TUx)
tdlchannel2 = TdlChannel(jakes2, channel_profile=COST259_TUx)
# Generate channel that would corrupt the transmit signal.
tdlchannel1.generate_impulse_response(1)
tdlchannel2.generate_impulse_response(1)
# Get the generated impulse response
impulse_response1 = tdlchannel1.get_last_impulse_response()
impulse_response2 = tdlchannel2.get_last_impulse_response()
# Get the corresponding frequency response
freq_resp_1 = impulse_response1.get_freq_response(Nsc)
H1 = freq_resp_1[:, :, 0, 0]
freq_resp_2 = impulse_response2.get_freq_response(Nsc)
H2 = freq_resp_2[:, :, 0, 0]
# Sequence of the users
r1 = user1_seq.seq_array()
r2 = user2_seq.seq_array()
# Received signal (in frequency domain) of user 1
comb_indexes = np.arange(0, Nsc, 2)
Y1 = H1[comb_indexes, :] * r1[:, np.newaxis]
Y2 = H2[comb_indexes, :] * r2[:, np.newaxis]
Y = Y1 + Y2
# Calculate expected estimated channel for user 1
y1 = np.fft.ifft(r1.size * np.conj(r1[:, np.newaxis]) * Y,
size,
axis=0)
tilde_h1_espected = y1[0:16]
tilde_H1_espected = np.fft.fft(tilde_h1_espected, Nsc, axis=0)
# Test the CazacBasedChannelEstimator estimation
H1_estimated = ue1_channel_estimator.estimate_channel_freq_domain(
Y.T, 15)
np.testing.assert_array_almost_equal(H1_estimated, tilde_H1_espected.T)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H1[50:-50, :] - tilde_H1_espected[50:-50, :])
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.shape),
decimal=2)
def test_estimate_channel_without_comb_pattern(self):
Nsc = 300 # 300 subcarriers
size = Nsc # The size is also 300, since there is no comb pattern
Nzc = 139
user1_seq = SrsUeSequence(
RootSequence(root_index=25, size=size, Nzc=Nzc), 1)
user2_seq = SrsUeSequence(
RootSequence(root_index=25, size=size, Nzc=Nzc), 4)
# Set size_multiplier to 1, since we won't use the comb pattern
ue1_channel_estimator = CazacBasedChannelEstimator(user1_seq,
size_multiplier=1)
speed_terminal = 3 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
subcarrier_bandwidth = 15e3 # Subcarrier bandwidth (in Hz)
wave_length = 3e8 / fcDbl # Carrier wave length
Fd = speed_terminal / wave_length # Doppler Frequency
Ts = 1. / (Nsc * subcarrier_bandwidth) # Sampling interval
L = 16 # Number of jakes taps
jakes1 = JakesSampleGenerator(Fd, Ts, L)
jakes2 = JakesSampleGenerator(Fd, Ts, L)
# Create a TDL channel object for each user
tdlchannel1 = TdlChannel(jakes1, channel_profile=COST259_TUx)
tdlchannel2 = TdlChannel(jakes2, channel_profile=COST259_TUx)
# Generate channel that would corrupt the transmit signal.
tdlchannel1.generate_impulse_response(1)
tdlchannel2.generate_impulse_response(1)
# Get the generated impulse response
impulse_response1 = tdlchannel1.get_last_impulse_response()
impulse_response2 = tdlchannel2.get_last_impulse_response()
# Get the corresponding frequency response
freq_resp_1 = impulse_response1.get_freq_response(Nsc)
H1 = freq_resp_1[:, 0]
freq_resp_2 = impulse_response2.get_freq_response(Nsc)
H2 = freq_resp_2[:, 0]
# Sequence of the users
r1 = user1_seq.seq_array()
r2 = user2_seq.seq_array()
# Received signal (in frequency domain) of user 1
Y1 = H1 * r1
Y2 = H2 * r2
Y = Y1 + Y2
# Calculate expected estimated channel for user 1
y1 = np.fft.ifft(np.conj(r1) * Y, size)
tilde_h1 = y1[0:16]
tilde_H1 = np.fft.fft(tilde_h1, Nsc)
# Test the CazacBasedChannelEstimator estimation
np.testing.assert_array_almost_equal(
ue1_channel_estimator.estimate_channel_freq_domain(Y, 15),
tilde_H1)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H1[50:-50] - tilde_H1[50:-50])
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.size),
decimal=2)
def test_estimate_channel_with_srs(self):
Nsc = 300 # 300 subcarriers
size = Nsc // 2
Nzc = 139
user1_seq = SrsUeSequence(RootSequence(root_index=25,
size=size,
Nzc=Nzc),
1,
normalize=True)
user2_seq = SrsUeSequence(RootSequence(root_index=25,
size=size,
Nzc=Nzc),
4,
normalize=True)
ue1_channel_estimator = CazacBasedChannelEstimator(user1_seq)
ue2_channel_estimator = CazacBasedChannelEstimator(user2_seq)
speed_terminal = 3 / 3.6 # Speed in m/s
fcDbl = 2.6e9 # Central carrier frequency (in Hz)
subcarrier_bandwidth = 15e3 # Subcarrier bandwidth (in Hz)
wave_length = 3e8 / fcDbl # Carrier wave length
Fd = speed_terminal / wave_length # Doppler Frequency
Ts = 1. / (Nsc * subcarrier_bandwidth) # Sampling interval
L = 16 # Number of jakes taps
jakes1 = JakesSampleGenerator(Fd, Ts, L)
jakes2 = JakesSampleGenerator(Fd, Ts, L)
# Create a TDL channel object for each user
tdlchannel1 = TdlChannel(jakes1, channel_profile=COST259_TUx)
tdlchannel2 = TdlChannel(jakes2, channel_profile=COST259_TUx)
# Generate channel that would corrupt the transmit signal.
tdlchannel1.generate_impulse_response(1)
tdlchannel2.generate_impulse_response(1)
# Get the generated impulse response
impulse_response1 = tdlchannel1.get_last_impulse_response()
impulse_response2 = tdlchannel2.get_last_impulse_response()
# Get the corresponding frequency response
freq_resp_1 = impulse_response1.get_freq_response(Nsc)
H1 = freq_resp_1[:, 0]
freq_resp_2 = impulse_response2.get_freq_response(Nsc)
H2 = freq_resp_2[:, 0]
# Sequence of the users
r1 = user1_seq.seq_array()
r2 = user2_seq.seq_array()
# Received signal (in frequency domain) of user 1
comb_indexes = np.arange(0, Nsc, 2)
Y1 = H1[comb_indexes] * r1
Y2 = H2[comb_indexes] * r2
Y = Y1 + Y2
# xxxxxxxxxx USER 1 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Calculate expected estimated channel for user 1
y1 = np.fft.ifft(r1.size * np.conj(r1) * Y, size)
tilde_h1 = y1[0:16]
tilde_H1 = np.fft.fft(tilde_h1, Nsc)
# Test the CazacBasedChannelEstimator estimation
np.testing.assert_array_almost_equal(
ue1_channel_estimator.estimate_channel_freq_domain(Y, 15),
tilde_H1)
# Check that the estimated channel and the True channel have similar
# norms
self.assertAlmostEqual(np.linalg.norm(
ue1_channel_estimator.estimate_channel_freq_domain(Y, 15)),
np.linalg.norm(H1),
delta=0.5)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H1[50:-50] - tilde_H1[50:-50])
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.size),
decimal=2)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx USER 2 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Calculate expected estimated channel for user 2
y2 = np.fft.ifft(r2.size * np.conj(r2) * Y, size)
tilde_h2 = y2[0:16]
tilde_H2 = np.fft.fft(tilde_h2, Nsc)
# Test the CazacBasedChannelEstimator estimation
np.testing.assert_array_almost_equal(
ue2_channel_estimator.estimate_channel_freq_domain(Y, 15),
tilde_H2)
# Check that the estimated channel and the True channel have similar
# norms
self.assertAlmostEqual(np.linalg.norm(
ue2_channel_estimator.estimate_channel_freq_domain(Y, 15)),
np.linalg.norm(H2),
delta=0.5)
# Test if true channel and estimated channel are similar. Since the
# channel estimation error is higher at the first and last
# subcarriers we will test only the inner 200 subcarriers
error = np.abs(H2[50:-50] - tilde_H2[50:-50])
":type: np.ndarray"
np.testing.assert_almost_equal(error / 2.,
np.zeros(error.size),
decimal=2)