diff options
| -rwxr-xr-x | dpd/main.py | 35 | ||||
| -rw-r--r-- | dpd/src/Const.py | 84 |
2 files changed, 106 insertions, 13 deletions
diff --git a/dpd/main.py b/dpd/main.py index ddbbfc4..921afb2 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -12,8 +12,6 @@ predistortion module of ODR-DabMod.""" import datetime import os -import time -import sys import matplotlib matplotlib.use('GTKAgg') @@ -46,10 +44,10 @@ import src.ExtractStatistic as ExtractStatistic import src.Adapt as Adapt import src.Agc as Agc import src.TX_Agc as TX_Agc -import src.Symbol_align -import src.const -import src.MER -import src.Measure_Shoulders +from src.Symbol_align import Symbol_align +from src.Const import Const +from src.MER import MER +from src.Measure_Shoulders import Measure_Shoulders import argparse parser = argparse.ArgumentParser( @@ -91,6 +89,13 @@ parser.add_argument('-i', '--iterations', default=1, type=int, parser.add_argument('-L', '--lut', help='Use lookup table instead of polynomial predistorter', action="store_true") +parser.add_argument('--n_bins', default='64', type=int, + required=False) +parser.add_argument('--n_per_bin', default='128', type=int, + required=False) +parser.add_argument('--n_meas', default='20', type=int, + help='Number of samples to request from ODR-DabMod', + required=False) cli_args = parser.parse_args() logging.info(cli_args) @@ -106,10 +111,14 @@ target_median = cli_args.target_median rxgain = cli_args.rxgain txgain = cli_args.txgain -c = src.const.const(samplerate, target_median) -SA = src.Symbol_align.Symbol_align(c) -MER = src.MER.MER(c) -MS = src.Measure_Shoulders.Measure_Shoulder(c) +n_bins = cli_args.n_bins +n_per_bin = cli_args.n_per_bin +n_meas = cli_args.n_meas + +c = Const(samplerate, target_median, n_bins, n_per_bin, n_meas) +SA = Symbol_align(c) +MER = MER(c) +MS = Measure_Shoulders(c) meas = Measure.Measure(samplerate, port, num_req) extStat = ExtractStatistic.ExtractStatistic(c) @@ -181,7 +190,7 @@ while i < num_iter: tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) - if extStat.n_meas >= 100: + if extStat.n_meas >= c.n_meas: state = "model" else: state = "measure" @@ -212,8 +221,8 @@ while i < num_iter: rx_gain = adapt.get_rxgain() digital_gain = adapt.get_digital_gain() tx_median = np.median(np.abs(txframe_aligned)) - rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned) - tx_shoulder_tuple = MS.average_shoulders(txframe_aligned) + rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned) if c.MS_enable else None + tx_shoulder_tuple = MS.average_shoulders(txframe_aligned) if c.MS_enable else None logging.info(list((name, eval(name)) for name in ['i', 'tx_mer', 'tx_shoulder_tuple', 'rx_mer', diff --git a/dpd/src/Const.py b/dpd/src/Const.py new file mode 100644 index 0000000..2504c1e --- /dev/null +++ b/dpd/src/Const.py @@ -0,0 +1,84 @@ +# 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, n_bins, n_per_bin, n_meas): + self.sample_rate = sample_rate + self.n_meas = n_meas + + # 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 = False + self.ES_start = 0.0 + self.ES_end = 1.0 + self.ES_n_bins = n_bins + self.ES_n_per_bin = n_per_bin + + # 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 = False + + # Constants for MER + self.MER_plot = False + + # Constants for Model_PM + self.MPM_tx_min = 0.1 + + # Constants for Measure_Shoulder + self.MS_enable = False + self.MS_plot = False + 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 |