# -*- 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.pyplot as plt from sklearn import linear_model class Model: """Calculates new coefficients using the measurement and the old coefficients""" def __init__(self, c, SA, MER, coefs_am, coefs_pm, learning_rate_am=1., learning_rate_pm=1., plot=False): self.c = c self.SA = SA self.MER = MER self.learning_rate_am = learning_rate_am self.learning_rate_pm = learning_rate_pm self.coefs_am = coefs_am self.coefs_am_history = [coefs_am, ] self.mses_am = [] self.errs_am = [] self.tx_mers = [] self.rx_mers = [] self.coefs_pm = coefs_pm self.coefs_pm_history = [coefs_pm, ] self.errs_pm = [] self.plot = plot def sample_uniformly(self, tx_dpd, rx_received, n_bins=5): """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""" mask = np.logical_and((np.abs(tx_dpd) > 0.01), (np.abs(rx_received) > 0.01)) tx_dpd = tx_dpd[mask] rx_received = rx_received[mask] 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] assert isinstance(rx_choice[0], np.complex64), \ "rx_choice is not complex64 but {}".format(rx_choice[0].dtype) assert isinstance(tx_choice[0], np.complex64), \ "tx_choice is not complex64 but {}".format(tx_choice[0].dtype) return tx_choice, rx_choice def dpd_amplitude(self, sig, coefs=None): if coefs is None: coefs = self.coefs_am assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) sig_abs = np.abs(sig) A_sig = np.vstack([np.ones(sig_abs.shape), sig_abs ** 1, sig_abs ** 2, sig_abs ** 3, sig_abs ** 4, ]).T sig_dpd = sig * np.sum(A_sig * coefs, axis=1) return sig_dpd, A_sig def dpd_phase(self, sig, coefs=None): if coefs is None: coefs = self.coefs_pm assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) sig_abs = np.abs(sig) A_phase = np.vstack([np.ones(sig_abs.shape), sig_abs ** 1, sig_abs ** 2, sig_abs ** 3, sig_abs ** 4, ]).T phase_diff_est = np.sum(A_phase * coefs, axis=1) return phase_diff_est, A_phase def _next_am_coefficent(self, tx_choice, rx_choice): """Calculate new coefficients for AM/AM correction""" rx_dpd, rx_A = self.dpd_amplitude(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) mse = np.mean(np.abs((rx_dpd - tx_choice) ** 2)) self.mses_am.append(mse) self.errs_am.append(np.mean(err**2)) reg = linear_model.Ridge(alpha=0.00001) reg.fit(rx_A, err) a_delta = reg.coef_ new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0]) return new_coefs_am def _next_pm_coefficent(self, tx_choice, rx_choice): """Calculate new coefficients for AM/PM correction Assuming deviations smaller than pi/2""" phase_diff_choice = np.angle( (rx_choice * tx_choice.conjugate()) / (np.abs(rx_choice) * np.abs(tx_choice)) ) plt.hist(phase_diff_choice) plt.savefig('/tmp/hist_' + str(np.random.randint(0,1000)) + '.svg') plt.clf() phase_diff_est, phase_A = self.dpd_phase(rx_choice) err_phase = phase_diff_est - phase_diff_choice self.errs_pm.append(np.mean(np.abs(err_phase ** 2))) reg = linear_model.Ridge(alpha=0.00001) reg.fit(phase_A, err_phase) p_delta = reg.coef_ new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta return new_coefs_pm, phase_diff_choice def get_next_coefs(self, tx_dpd, rx_received): # Check data type assert tx_dpd[0].dtype == np.complex64, \ "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype) assert rx_received[0].dtype == np.complex64, \ "rx_received is not complex64 but {}".format(rx_received[0].dtype) # Check if signals have same normalization 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) new_coefs_am = self._next_am_coefficent(tx_choice, rx_choice) new_coefs_pm, phase_diff_choice = self._next_pm_coefficent(tx_choice, rx_choice) logging.debug('txframe: min {:.2f}, max {:.2f}, ' \ 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \ 'median {:.2f}; new coefs_am {};' \ 'new_coefs_pm {}'.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)), new_coefs_am, new_coefs_pm)) if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: off = self.SA.calc_offset(tx_dpd) tx_mer = self.MER.calc_mer(tx_dpd[off:off + self.c.T_U]) rx_mer = self.MER.calc_mer(rx_received[off:off + self.c.T_U], debug=True) self.tx_mers.append(tx_mer) self.rx_mers.append(rx_mer) 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=(2 * 6, 2 * 6)) i_sub = 1 ax = plt.subplot(4, 2, i_sub) i_sub += 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_am_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(4, 2, i_sub) i_sub += 1 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(4, 2, i_sub) i_sub += 1 ax.semilogy(np.array(self.mses_am) + 1e-10, label="log(MSE)") ax.semilogy(np.array(self.errs_am) + 1e-10, label="log(ERR)") ax.legend(loc=4) ax.set_title("MSE History") ax.set_xlabel("Iterations") ax.set_ylabel("MSE") ax = plt.subplot(4, 2, i_sub) i_sub += 1 ax.plot(self.tx_mers, label="TX MER") ax.plot(self.rx_mers, label="RX MER") ax.legend(loc=4) ax.set_title("MER History") ax.set_xlabel("Iterations") ax.set_ylabel("MER") ax = plt.subplot(4, 2, i_sub) rx_range = np.linspace(0, 1, num=100, dtype=np.complex64) rx_range_dpd = self.dpd_amplitude(rx_range)[0] rx_range_dpd_new = self.dpd_amplitude(rx_range, new_coefs_am)[0] i_sub += 1 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, label="current") ax.plot(rx_range_dpd_new / self.coefs_am[0], rx_range, linewidth=0.25, label="next") ax.set_ylim(0, 1) ax.set_xlim(0, 1) ax.legend() ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") ax = plt.subplot(4, 2, i_sub) i_sub += 1 coefs_am_history = np.array(self.coefs_am_history) for idx, coef_hist in enumerate(coefs_am_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") phase_range = np.linspace(0, 1, num=100, dtype=np.complex64) phase_range_dpd = self.dpd_phase(phase_range)[0] phase_range_dpd_new = self.dpd_phase(phase_range, coefs=new_coefs_pm)[0] ax = plt.subplot(4, 2, i_sub) i_sub += 1 ax.scatter( np.abs(tx_choice), np.rad2deg(phase_diff_choice), s=0.1) ax.plot( np.abs(phase_range), np.rad2deg(phase_range_dpd), linewidth=0.25, label="current") ax.plot( np.abs(phase_range), np.rad2deg(phase_range_dpd_new), linewidth=0.25, label="next") ax.set_ylim(-60, 60) ax.set_xlim(0, 1) ax.legend() ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("Phase Difference") ax = plt.subplot(4, 2, i_sub) i_sub += 1 coefs_pm_history = np.array(self.coefs_pm_history) for idx, coef_phase_hist in enumerate(coefs_pm_history.T): ax.plot(coef_phase_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") fig.tight_layout() fig.savefig(fig_path) fig.clf() self.coefs_am = new_coefs_am self.coefs_am_history.append(self.coefs_am) self.coefs_pm = new_coefs_pm self.coefs_pm_history.append(self.coefs_pm) 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.