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# -*- 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
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from typing import Tuple
import dpd.Adapt as Adapt
import dpd.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, c):
assert isinstance(measure, Measure.Measure)
assert isinstance(adapt, Adapt.Adapt)
self.measure = measure
self.adapt = adapt
self.min_rxgain = c.RAGC_min_rxgain
self.max_rxgain = c.RAGC_max_rxgain
self.rxgain = self.min_rxgain
self.peak_to_median = 1./c.RAGC_rx_median_target
def run(self) -> Tuple[bool, str]:
self.adapt.set_rxgain(self.rxgain)
# 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
measurements = "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)
logging.info(measurements)
if self.rxgain < self.min_rxgain:
w = "Warning: calculated RX Gain={} is lower than minimum={}. RX feedback power is too high!".format(
self.rxgain, self.min_rxgain)
logging.warning(w)
return (False, "\n".join([measurements, w]))
elif self.rxgain > self.max_rxgain:
w = "Warning: calculated RX Gain={} is higher than maximum={}. RX feedback power should be increased.".format(
self.rxgain, self.max_rxgain)
logging.warning(w)
return (False, "\n".join([measurements, w]))
else:
self.adapt.set_rxgain(self.rxgain)
time.sleep(0.5)
return (True, measurements)
def plot_estimates(self):
"""Plots the estimate of for Max, Median, Mean for different
number of samples."""
if self.c.plot_location is None:
return
self.adapt.set_rxgain(self.min_rxgain)
time.sleep(1)
dt = datetime.datetime.now().isoformat()
fig_path = self.c.plot_location + "/" + dt + "_agc.png"
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)
plt.close(fig)
# 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.
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