# -*- coding: utf-8 -*- # # DPD Calculation Engine, model implementation. # # http://www.opendigitalradio.org # Licence: The MIT License, see notice at the end of this file import datetime import os import logging 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 from sklearn.linear_model import Ridge class Model: """Calculates new coefficients using the measurement and the old coefficients""" def __init__(self, coefs_am, coefs_pm, plot=False): self.coefs_am = coefs_am self.coefs_history = [coefs_am, ] self.mses = [0, ] self.errs = [0, ] self.coefs_pm = coefs_pm self.coefs_pm_history = [coefs_pm, ] self.errs_phase = [0, ] self.plot=plot def sample_uniformly(self, tx_dpd, rx_received, n_bins=4): """This function returns tx and rx samples in a way that the tx amplitudes have an approximate uniform distribution with respect to the tx_dpd amplitudes""" txframe_aligned_abs = np.abs(tx_dpd) ccdf_min = 0 ccdf_max = np.max(txframe_aligned_abs) tx_hist, ccdf_edges = np.histogram(txframe_aligned_abs, bins=n_bins, range=(ccdf_min, ccdf_max)) n_choise = np.min(tx_hist) tx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) rx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) for idx, bin in enumerate(tx_hist): indices = np.where((txframe_aligned_abs >= ccdf_edges[idx]) & (txframe_aligned_abs <= ccdf_edges[idx+1]))[0] indices_choise = np.random.choice(indices, n_choise, replace=False) rx_choice[idx*n_choise:(idx+1)*n_choise] = rx_received[indices_choise] tx_choice[idx*n_choise:(idx+1)*n_choise] = tx_dpd[indices_choise] return tx_choice, rx_choice def amplitude_predistortion(self, sig): sig_abs = np.abs(sig) A_sig = np.vstack([np.ones(sig_abs.shape), sig_abs ** 2, sig_abs ** 4, sig_abs ** 6, sig_abs ** 8, ]).T sig_dpd = sig * np.sum(A_sig * self.coefs_am, axis=1) return sig_dpd, A_sig def dpd_phase(self, tx): tx_abs = np.abs(tx) tx_A_complex = np.vstack([tx, tx * tx_abs ** 1, tx * tx_abs ** 2, tx * tx_abs ** 3, tx * tx_abs ** 4, ]).T tx_dpd = np.sum(tx_A_complex * self.coefs_pm, axis=1) return tx_dpd def get_next_coefs(self, tx_dpd, rx_received): normalization_error = np.abs(np.median(np.abs(tx_dpd)) - np.median(np.abs(rx_received)))/(np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received))) assert normalization_error < 0.01, "Non normalized signals" tx_choice, rx_choice = self.sample_uniformly(tx_dpd, rx_received) # Calculate new coefficients for AM/AM correction rx_dpd, rx_A = self.amplitude_predistortion(rx_choice) rx_dpd = rx_dpd * ( np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd))) err = np.abs(rx_dpd) - np.abs(tx_choice) self.errs.append(np.mean(np.abs(err ** 2))) mse = np.mean(np.abs((rx_dpd - tx_choice)**2)) self.mses.append(mse) a_delta = np.linalg.lstsq(rx_A, err)[0] new_coefs = self.coefs_am - 0.1 * a_delta new_coefs = new_coefs * (self.coefs_am[0] / new_coefs[0]) assert np.abs(self.coefs_am[0] / new_coefs[0] - 1) < 0.1, \ "Too large change in first " \ "coefficient. {}, {}".format(self.coefs_am[0], new_coefs[0]) logging.debug("a_delta {}".format(a_delta)) logging.debug("new coefs_am {}".format(new_coefs)) rx_range = np.linspace(0, 1, num=100) rx_range_dpd = self.amplitude_predistortion(rx_range)[0] rx_range = rx_range[(rx_range_dpd > 0) & (rx_range_dpd < 2)] rx_range_dpd = rx_range_dpd[(rx_range_dpd > 0) & (rx_range_dpd < 2)] logging.debug('txframe: min {:.2f}, max {:.2f}, ' \ 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \ 'median {:.2f}; a_delta {}; new coefs_am {}'.format( np.min(np.abs(tx_dpd)), np.max(np.abs(tx_dpd)), np.median(np.abs(tx_dpd)), np.min(np.abs(rx_choice)), np.max(np.abs(rx_choice)), np.median(np.abs(rx_choice)), a_delta, new_coefs)) if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() fig_path = logging_path + "/" + dt + "_Model.svg" fig = plt.figure(figsize=(3*6, 6)) ax = plt.subplot(2,3,1) ax.plot(np.abs(tx_dpd[:128]), label="TX sent", linestyle=":") ax.plot(np.abs(rx_received[:128]), label="RX received", color="red") ax.set_title("Synchronized Signals of Iteration {}".format(len(self.coefs_history))) ax.set_xlabel("Samples") ax.set_ylabel("Amplitude") ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, median {:01.3f})".format( np.max(np.abs(tx_dpd)), np.mean(np.abs(tx_dpd)), np.median(np.abs(tx_dpd)) ), size = 8) ax.legend(loc=4) ax = plt.subplot(2,3,2) ax.scatter( np.abs(tx_choice), np.abs(rx_choice), s=0.1) ax.plot(rx_range_dpd / self.coefs_am[0], rx_range, linewidth=0.25) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") ax = plt.subplot(2,3,3) ccdf_min, ccdf_max = 0, 1 tx_hist, ccdf_edges = np.histogram(np.abs(tx_dpd), bins=60, range=(ccdf_min, ccdf_max)) tx_hist_normalized = tx_hist.astype(float)/np.sum(tx_hist) ccdf = 1.0 - np.cumsum(tx_hist_normalized) ax.semilogy(ccdf_edges[:-1], ccdf, label="CCDF") ax.semilogy(ccdf_edges[:-1], tx_hist_normalized, label="Histogram", drawstyle='steps') ax.legend(loc=4) ax.set_ylim(1e-5,2) ax.set_title("Complementary Cumulative Distribution Function") ax.set_xlabel("TX Amplitude") ax.set_ylabel("Ratio of Samples larger than x") ax = plt.subplot(2,3,4) coefs_history = np.array(self.coefs_history) for idx, coef_hist in enumerate(coefs_history.T): ax.plot(coef_hist, label="Coef {}".format(idx), linewidth=0.5) ax.legend(loc=4) ax.set_title("AM/AM Coefficient History") ax.set_xlabel("Iterations") ax.set_ylabel("Coefficient Value") ax = plt.subplot(2,3,5) coefs_history = np.array(self.coefs_pm_history) for idx, coef_hist in enumerate(coefs_history.T): ax.plot(coef_hist, label="Coef {}".format(idx), linewidth=0.5) ax.legend(loc=4) ax.set_title("AM/PM Coefficient History") ax.set_xlabel("Iterations") ax.set_ylabel("Coefficient Value") ax = plt.subplot(2,3,6) ax.plot(self.mses, label="MSE") ax.plot(self.errs, label="ERR") ax.legend(loc=4) ax.set_title("MSE History") ax.set_xlabel("Iterations") ax.set_ylabel("MSE") fig.tight_layout() fig.savefig(fig_path) fig.clf() self.coefs_am = new_coefs self.coefs_history.append(self.coefs_am) return self.coefs_am, self.coefs_pm # 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.