def implementation_validation():
M = 33
K = 32
alpha = .5
overlap = 2
H = get_frequency_domain_filter('rrc', alpha, M, K, overlap)
taps = gfdm_filter_taps('rrc', alpha, M, K, 1)
A = gfdm_modulation_matrix(taps, M, K)
tests = 100
max_rel_error = 0.0
for t in range(tests):
d = get_random_samples(M * K)
xmat = A.dot(d) / np.sqrt(len(d))
D = get_data_matrix(d, K, group_by_subcarrier=True)
xfft = gfdm_modulate_block(D, H, M, K, overlap, False) / np.sqrt(
len(d))
rel_err = np.linalg.norm(xmat - xfft) / np.linalg.norm(xmat)
if rel_err > max_rel_error:
max_rel_error = rel_err
if rel_err > 1e-3:
raise RuntimeError(
'Relative error between FFT and Matrix implementation is above 1e-3!'
)
print 'maximum relative error is:', max_rel_error
def main():
np.set_printoptions(precision=2, linewidth=150)
alpha = .2
active_subcarriers = 52
timeslots = 9
fft_len = 64
cp_len = fft_len // 2
cs_len = cp_len // 2
subcarrier_map = mapping.get_subcarrier_map(fft_len,
active_subcarriers,
dc_free=True)
ref_frame, modulated_frame, x_preamble, data, freq_filter_taps = generate_integrated_frame(
timeslots, fft_len, active_subcarriers, cp_len, cs_len, alpha)
test_frame = np.concatenate(
(.001 * utils.get_random_samples(1000), ref_frame,
.001 * utils.get_random_samples(1000)))
def main():
np.set_printoptions(precision=2, linewidth=150)
alpha = .2
active_subcarriers = 52
timeslots = 9
fft_len = 64
cp_len = fft_len // 2
cs_len = cp_len // 2
subcarrier_map = mapping.get_subcarrier_map(fft_len, active_subcarriers, dc_free=True)
ref_frame, modulated_frame, x_preamble, data, freq_filter_taps = generate_integrated_frame(timeslots, fft_len, active_subcarriers, cp_len, cs_len, alpha)
test_frame = np.concatenate((.001 * utils.get_random_samples(1000), ref_frame, .001 * utils.get_random_samples(1000)))
def gr_conformity_validation():
M = 32
K = 8
alpha = .5
oversampling_factor = 1
overlap = 2
tests = 100
for t in range(tests):
# d = np.random.standard_normal(2 * M * K)
# d = np.reshape(d, (2, -1))
# d = d[0] + 1j * d[1]
d = get_random_samples(M * K)
xo = gfdm_tx_fft2(d, 'rrc', alpha, M, K, overlap, oversampling_factor)
xn = gfdm_gr_modulator(d, 'rrc', alpha, M, K, overlap)
assert np.all(np.abs(xo - xn) < 1e-4)
def gr_conformity_validation():
M = 32
K = 8
alpha = .5
oversampling_factor = 1
overlap = 2
tests = 100
for t in range(tests):
# d = np.random.standard_normal(2 * M * K)
# d = np.reshape(d, (2, -1))
# d = d[0] + 1j * d[1]
d = get_random_samples(M * K)
xo = gfdm_tx_fft2(d, 'rrc', alpha, M, K, overlap, oversampling_factor)
xn = gfdm_gr_modulator(d, 'rrc', alpha, M, K, overlap)
if not np.all(xo == xn):
raise RuntimeError('Function results deviate')
def gr_conformity_validation():
M = 32
K = 8
alpha = .5
oversampling_factor = 1
overlap = 2
tests = 100
for t in range(tests):
# d = np.random.standard_normal(2 * M * K)
# d = np.reshape(d, (2, -1))
# d = d[0] + 1j * d[1]
d = get_random_samples(M * K)
xo = gfdm_tx_fft2(d, 'rrc', alpha, M, K, overlap, oversampling_factor)
xn = gfdm_gr_modulator(d, 'rrc', alpha, M, K, overlap)
if not np.all(xo == xn):
raise RuntimeError('Function results deviate')
def implementation_validation():
M = 33
K = 32
alpha = .5
overlap = 2
H = get_frequency_domain_filter('rrc', alpha, M, K, overlap)
taps = gfdm_filter_taps('rrc', alpha, M, K, 1)
A = gfdm_modulation_matrix(taps, M, K)
tests = 100
max_rel_error = 0.0
for t in range(tests):
d = get_random_samples(M * K)
xmat = A.dot(d) / np.sqrt(len(d))
D = get_data_matrix(d, K, group_by_subcarrier=True)
xfft = gfdm_modulate_block(D, H, M, K, overlap, False) / np.sqrt(len(d))
rel_err = np.linalg.norm(xmat - xfft) / np.linalg.norm(xmat)
if rel_err > max_rel_error:
max_rel_error = rel_err
if rel_err > 1e-3:
raise RuntimeError('Relative error between FFT and Matrix implementation is above 1e-3!')
print 'maximum relative error is:', max_rel_error