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-rw-r--r--dpd/src/Model.py336
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diff --git a/dpd/src/Model.py b/dpd/src/Model.py
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+# -*- coding: utf-8 -*-
+
+import datetime
+import os
+import logging
+logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
+
+from pynverse import inversefunc
+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):
+ 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, ]
+
+ def sample_uniformly(self, txframe_aligned, rxframe_aligned, 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 txframe_aligned amplitudes"""
+ txframe_aligned_abs = np.abs(txframe_aligned)
+ 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] = rxframe_aligned[indices_choise]
+ tx_choice[idx*n_choise:(idx+1)*n_choise] = txframe_aligned[indices_choise]
+ return tx_choice, rx_choice
+
+ def get_next_coefs(self, txframe_aligned, rxframe_aligned):
+ tx_choice, rx_choice = self.sample_uniformly(txframe_aligned, rxframe_aligned)
+
+ # Calculate new coefficients for AM/AM correction
+ rx_abs = np.abs(rx_choice)
+ rx_A = np.vstack([rx_abs,
+ rx_abs ** 3,
+ rx_abs ** 5,
+ rx_abs ** 7,
+ rx_abs ** 9,
+ ]).T
+ rx_dpd = np.sum(rx_A * self.coefs_am, axis=1)
+ rx_dpd = rx_dpd * (
+ np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd)))
+
+ err = rx_dpd - np.abs(tx_choice)
+ self.errs.append(np.mean(np.abs(err ** 2)))
+
+ 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])
+ logging.debug("a_delta {}".format(a_delta))
+ logging.debug("new coefs_am {}".format(new_coefs))
+
+ # Calculate new coefficients for AM/PM correction
+ phase_diff_rad = ((
+ (np.angle(tx_choice) -
+ np.angle(rx_choice) +
+ np.pi) % (2 * np.pi)) -
+ np.pi
+ )
+
+ tx_abs = np.abs(tx_choice)
+ tx_abs_A = np.vstack([tx_abs,
+ tx_abs ** 2,
+ tx_abs ** 3,
+ tx_abs ** 4,
+ tx_abs ** 5,
+ ]).T
+ phase_dpd = np.sum(tx_abs_A * self.coefs_pm, axis=1)
+
+ err_phase = phase_dpd - phase_diff_rad
+ self.errs_phase.append(np.mean(np.abs(err_phase ** 2)))
+ a_delta = np.linalg.lstsq(tx_abs_A, err_phase)[0]
+ new_coefs_pm = self.coefs_pm - 0.1 * a_delta
+ logging.debug("a_delta {}".format(a_delta))
+ logging.debug("new new_coefs_pm {}".format(new_coefs_pm))
+
+ def dpd_phase(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
+
+ tx_range = np.linspace(0, 2)
+ phase_range_dpd = dpd_phase(tx_range)
+
+ rx_A_complex = np.vstack([rx_choice,
+ rx_choice * rx_abs ** 2,
+ rx_choice * rx_abs ** 4,
+ rx_choice * rx_abs ** 6,
+ rx_choice * rx_abs ** 8,
+ ]).T
+ rx_post_distored = np.sum(rx_A_complex * self.coefs_am, axis=1)
+ rx_post_distored = rx_post_distored * (
+ np.median(np.abs(tx_choice)) /
+ np.median(np.abs(rx_post_distored)))
+ mse = np.mean(np.abs((tx_choice - rx_post_distored) ** 2))
+ logging.debug("MSE: {}".format(mse))
+ self.mses.append(mse)
+
+ def dpd(tx):
+ tx_abs = np.abs(tx)
+ tx_A_complex = np.vstack([tx,
+ tx * tx_abs ** 2,
+ tx * tx_abs ** 4,
+ tx * tx_abs ** 6,
+ tx * tx_abs ** 8,
+ ]).T
+ tx_dpd = np.sum(tx_A_complex * self.coefs_am, axis=1)
+ return tx_dpd
+
+ rx_range = np.linspace(0, 1, num=100)
+ rx_range_dpd = dpd(rx_range)
+ 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)]
+
+ if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+ logging.debug("txframe: min %f, max %f, median %f" %
+ (np.min(np.abs(txframe_aligned)),
+ np.max(np.abs(txframe_aligned)),
+ np.median(np.abs(txframe_aligned))
+ ))
+
+ logging.debug("rxframe: min %f, max %f, median %f" %
+ (np.min(np.abs(rx_choice)),
+ np.max(np.abs(rx_choice)),
+ np.median(np.abs(rx_choice))
+ ))
+
+ dt = datetime.datetime.now().isoformat()
+ fig_path = logging_path + "/" + dt + "_Model.pdf"
+
+ fig = plt.figure(figsize=(3*6, 1.5 * 6))
+
+ ax = plt.subplot(3,3,1)
+ ax.plot(np.abs(txframe_aligned[:128]),
+ label="TX sent",
+ linestyle=":")
+ ax.plot(np.abs(rxframe_aligned[: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(txframe_aligned)),
+ np.mean(np.abs(txframe_aligned)),
+ np.median(np.abs(txframe_aligned))
+ ), size = 8)
+ ax.legend(loc=4)
+
+ ax = plt.subplot(3,3,2)
+ ax.plot(np.real(txframe_aligned[:128]),
+ label="TX sent",
+ linestyle=":")
+ ax.plot(np.real(rxframe_aligned[:128]),
+ label="RX received",
+ color="red")
+ ax.set_title("Synchronized Signals")
+ ax.set_xlabel("Samples")
+ ax.set_ylabel("Real Part")
+ ax.legend(loc=4)
+
+ ax = plt.subplot(3,3,3)
+ ax.plot(np.abs(txframe_aligned[:128]),
+ label="TX Frame",
+ linestyle=":",
+ linewidth=0.5)
+ ax.plot(np.abs(rxframe_aligned[:128]),
+ label="RX Frame",
+ linestyle="--",
+ linewidth=0.5)
+
+ rx_abs = np.abs(rxframe_aligned)
+ rx_A = np.vstack([rx_abs,
+ rx_abs ** 3,
+ rx_abs ** 5,
+ rx_abs ** 7,
+ rx_abs ** 9,
+ ]).T
+ rx_dpd = np.sum(rx_A * self.coefs_am, axis=1)
+ rx_dpd = rx_dpd * (
+ np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd)))
+
+ ax.plot(np.abs(rx_dpd[:128]),
+ label="RX DPD Frame",
+ linestyle="-.",
+ linewidth=0.5)
+
+ tx_abs = np.abs(np.abs(txframe_aligned[:128]))
+ tx_A = np.vstack([tx_abs,
+ tx_abs ** 3,
+ tx_abs ** 5,
+ tx_abs ** 7,
+ tx_abs ** 9,
+ ]).T
+ tx_dpd = np.sum(tx_A * new_coefs, axis=1)
+ tx_dpd_norm = tx_dpd * (
+ np.median(np.abs(tx_choice)) / np.median(np.abs(tx_dpd)))
+
+ ax.plot(np.abs(tx_dpd_norm[:128]),
+ label="TX DPD Frame Norm",
+ linestyle="-.",
+ linewidth=0.5)
+ ax.legend(loc=4)
+ ax.set_title("RX DPD")
+ ax.set_xlabel("Samples")
+ ax.set_ylabel("Amplitude")
+
+ ax = plt.subplot(3,3,4)
+ ax.scatter(
+ np.abs(tx_choice[:1024]),
+ np.abs(rx_choice[:1024]),
+ 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(3,3,5)
+ ax.scatter(
+ np.abs(tx_choice[:1024]),
+ phase_diff_rad[:1024] * 180 / np.pi,
+ s=0.1
+ )
+ ax.plot(tx_range, phase_range_dpd * 180 / np.pi, linewidth=0.25)
+ ax.set_title("Amplifier Characteristic")
+ ax.set_xlabel("TX Amplitude")
+ ax.set_ylabel("Phase Difference [deg]")
+
+ ax = plt.subplot(3,3,6)
+ ccdf_min, ccdf_max = 0, 1
+ tx_hist, ccdf_edges = np.histogram(np.abs(txframe_aligned),
+ 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(3,3,7)
+ 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(3,3,8)
+ 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(3,3,9)
+ coefs_history = np.array(self.coefs_history)
+ 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)
+ 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.
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