diff options
| -rwxr-xr-x | dpd/main.py | 15 | ||||
| -rw-r--r-- | dpd/src/Agc.py | 165 |
2 files changed, 175 insertions, 5 deletions
diff --git a/dpd/main.py b/dpd/main.py index 19460dd..8af6700 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -27,6 +27,7 @@ import traceback import src.Measure as Measure import src.Model as Model import src.Adapt as Adapt +import src.Agc as Agc import argparse parser = argparse.ArgumentParser(description="DPD Computation Engine for ODR-DabMod") @@ -82,8 +83,8 @@ else: adapt.set_txgain(txgain) adapt.set_rxgain(rxgain) -tx_gain = adapt.get_txgain() -rx_gain = adapt.get_rxgain() +tx_gain = adapt.get_txgain() +rx_gain = adapt.get_rxgain() dpd_coefs_am, dpd_coefs_pm = adapt.get_coefs() logging.info( "TX gain {}, RX gain {}, dpd_coefs_am {}, dpd_coefs_pm {}".format( @@ -91,15 +92,19 @@ logging.info( ) ) +# Automatic Gain Control +agc = Agc.Agc(meas, adapt) +agc.run() + for i in range(num_iter): try: - txframe_aligned, tx_ts, rxframe_aligned, rx_ts = meas.get_samples() + txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) coefs_am, coefs_pm = model.get_next_coefs(txframe_aligned, rxframe_aligned) adapt.set_coefs(coefs_am, coefs_pm) except Exception as e: - logging.info("Iteration {} failed.".format(i)) - logging.info(traceback.format_exc()) + logging.warning("Iteration {} failed.".format(i)) + logging.warning(traceback.format_exc()) # The MIT License (MIT) # diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py new file mode 100644 index 0000000..1fd11c8 --- /dev/null +++ b/dpd/src/Agc.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- +# +# Automatic Gain Control +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import datetime +import os +import logging +import time +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +import numpy as np +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt + +import src.Adapt as Adapt +import src.Measure as Measure + +class Agc: + """The goal of the automatic gain control is to set the + RX gain to a value at which all received amplitudes can + be detected. This means that the maximum possible amplitude + should be quantized at the highest possible digital value. + + A problem we have to face, is that the estimation of the + maximum amplitude by applying the max() function is very + unstable. This is due to the maximum’s rareness. Therefore + we estimate a far more robust value, such as the median, + and then approximate the maximum amplitude from it. + + Given this, we tune the RX gain in such a way, that the + received signal fulfills our desired property, of having + all amplitudes properly quantized.""" + + def __init__(self, measure, adapt, min_rxgain=25, peak_to_median=20): + assert isinstance(measure, Measure.Measure) + assert isinstance(adapt, Adapt.Adapt) + self.measure = measure + self.adapt = adapt + self.min_rxgain = min_rxgain + self.rxgain = self.min_rxgain + self.peak_to_median = peak_to_median + + def run(self): + self.adapt.set_rxgain(self.rxgain) + + for i in range(3): + # Measure + txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median= \ + self.measure.get_samples() + + # Estimate Maximum + rx_peak = self.peak_to_median * rx_median + correction_factor = 20*np.log10(1/rx_peak) + self.rxgain = self.rxgain + correction_factor + + assert self.min_rxgain <= self.rxgain, ("Desired RX Gain is {} which is smaller than the minimum of {}".format( + self.rxgain, self.min_rxgain)) + + logging.info("RX Median {:1.4f}, estimated peak {:1.4f}, correction factor {:1.4f}, new RX gain {:1.4f}".format( + rx_median, rx_peak, correction_factor, self.rxgain + )) + + self.adapt.set_rxgain(self.rxgain) + time.sleep(1) + + def plot_estimates(self): + """Plots the estimate of for Max, Median, Mean for different + number of samples.""" + self.adapt.set_rxgain(self.min_rxgain) + time.sleep(1) + + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_agc.pdf" + fig, axs = plt.subplots(2, 2, figsize=(3*6,1*6)) + axs = axs.ravel() + + for j in range(5): + txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median =\ + self.measure.get_samples() + + rxframe_aligned_abs = np.abs(rxframe_aligned) + + x = np.arange(100, rxframe_aligned_abs.shape[0], dtype=int) + rx_max_until = [] + rx_median_until = [] + rx_mean_until = [] + for i in x: + rx_max_until.append(np.max(rxframe_aligned_abs[:i])) + rx_median_until.append(np.median(rxframe_aligned_abs[:i])) + rx_mean_until.append(np.mean(rxframe_aligned_abs[:i])) + + axs[0].plot(x, + rx_max_until, + label="Run {}".format(j+1), + color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.8,0.7)), + linestyle="-", linewidth=0.25) + axs[0].set_xlabel("Samples") + axs[0].set_ylabel("Amplitude") + axs[0].set_title("Estimation for Maximum RX Amplitude") + axs[0].legend() + + axs[1].plot(x, + rx_median_until, + label="Run {}".format(j+1), + color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.9,0.7)), + linestyle="-", linewidth=0.25) + axs[1].set_xlabel("Samples") + axs[1].set_ylabel("Amplitude") + axs[1].set_title("Estimation for Median RX Amplitude") + axs[1].legend() + ylim_1 = axs[1].get_ylim() + + axs[2].plot(x, + rx_mean_until, + label="Run {}".format(j+1), + color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.9,0.7)), + linestyle="-", linewidth=0.25) + axs[2].set_xlabel("Samples") + axs[2].set_ylabel("Amplitude") + axs[2].set_title("Estimation for Mean RX Amplitude") + ylim_2 = axs[2].get_ylim() + axs[2].legend() + + axs[1].set_ylim(min(ylim_1[0], ylim_2[0]), + max(ylim_1[1], ylim_2[1])) + + fig.tight_layout() + fig.savefig(fig_path) + + axs[3].hist(rxframe_aligned_abs, bins=60) + axs[3].set_xlabel("Amplitude") + axs[3].set_ylabel("Frequency") + axs[3].set_title("Histogram of Amplitudes") + axs[3].legend() + + fig.tight_layout() + fig.savefig(fig_path) + fig.clf() + + +# The MIT License (MIT) +# +# Copyright (c) 2017 Andreas Steger +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. |