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# DAB Frame constants
# Sources:
# - etsi_EN_300_401_v010401p p145
# - Measured with USRP B200
import numpy as np
class Const:
def __init__(self, sample_rate, target_median, plot):
self.sample_rate = sample_rate
self.tx_gain_max = 89
# Time domain
self.T_F = sample_rate / 2048000 * 196608 # Transmission frame duration
self.T_NULL = sample_rate / 2048000 * 2656 # Null symbol duration
self.T_S = sample_rate / 2048000 * 2552 # Duration of OFDM symbols of indices l = 1, 2, 3,... L;
self.T_U = sample_rate / 2048000 * 2048 # Inverse of carrier spacing
self.T_C = sample_rate / 2048000 * 504 # Duration of cyclic prefix
# Frequency Domain
# example: np.delete(fft[3328:4865], 768)
self.FFT_delete = 768
self.FFT_delta = 1536 # Number of carrier frequencies
if sample_rate == 2048000:
self.FFT_start = 256
self.FFT_end = 1793
elif sample_rate == 8192000:
self.FFT_start = 3328
self.FFT_end = 4865
else:
raise RuntimeError("Sample Rate '{}' not supported".format(
sample_rate
))
# Calculate sample offset from phase rotation
# time per sample = 1 / sample_rate
# frequency per bin = 1kHz
# phase difference per sample offset = delta_t * 2 * pi * delta_freq
self.phase_offset_per_sample = 1. / sample_rate * 2 * np.pi * 1000
# Constants for ExtractStatistic
self.ES_plot = plot
self.ES_start = 0.0
self.ES_end = 1.0
self.ES_n_bins = 64
self.ES_n_per_bin = 128
# Constants for TX_Agc
self.TAGC_max_txgain = 89
self.TAGC_tx_median_target = target_median
self.TAGC_tx_median_max = self.TAGC_tx_median_target*1.4
self.TAGC_tx_median_min = self.TAGC_tx_median_target/1.4
self.RAGC_min_rxgain = 25
self.RAGC_rx_median_target = self.TAGC_tx_median_target
# Constants for Model
self.MDL_plot = True or plot # Override default
# Constants for MER
self.MER_plot = plot
# Constants for Model_PM
self.MPM_tx_min = 0.1
# Constants for Measure_Shoulder
self.MS_enable = False
self.MS_plot = plot
assert sample_rate==8192000
meas_offset = 976 # Offset from center frequency to measure shoulder [kHz]
meas_width = 100 # Size of frequency delta to measure shoulder [kHz]
shoulder_offset_edge = np.abs(meas_offset - self.FFT_delta)
self.MS_shoulder_left_start = self.FFT_start - shoulder_offset_edge - meas_width / 2
self.MS_shoulder_left_end = self.FFT_start - shoulder_offset_edge + meas_width / 2
self.MS_shoulder_right_start = self.FFT_end + shoulder_offset_edge - meas_width / 2
self.MS_shoulder_right_end = self.FFT_end + shoulder_offset_edge + meas_width / 2
self.MS_peak_start = self.FFT_start + 100 # Ignore region near edges
self.MS_peak_end = self.FFT_end - 100
self.MS_FFT_size = 8192
self.MS_averaging_size = 4 * self.MS_FFT_size
self.MS_n_averaging = 40
self.MS_n_proc = 4
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