diff options
Diffstat (limited to 'dpd/src/Model.py')
| -rw-r--r-- | dpd/src/Model.py | 153 |
1 files changed, 140 insertions, 13 deletions
diff --git a/dpd/src/Model.py b/dpd/src/Model.py index f66ba8f..8df3925 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -1,11 +1,16 @@ # -*- coding: utf-8 -*- -import numpy as np 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 @@ -13,8 +18,76 @@ class Model: def __init__(self, coefs): self.coefs = coefs + self.coefs_history = [coefs,] + self.mses = [0,] + self.errs = [0,] def get_next_coefs(self, txframe_aligned, rxframe_aligned): + dt = datetime.datetime.now().isoformat() + txframe_aligned.tofile(logging_path + "/txframe_" + dt + ".iq") + rxframe_aligned.tofile(logging_path + "/rxframe_" + dt + ".iq") + 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, axis=1) + rx_dpd = rx_dpd * ( + np.median(np.abs(txframe_aligned)) / np.median(np.abs(rx_dpd))) + + err = rx_dpd - np.abs(txframe_aligned) + self.errs.append(np.mean(np.abs(err**2))) + + a_delta = np.linalg.lstsq(rx_A, err)[0] + new_coefs = self.coefs - 0.1 * a_delta + new_coefs = new_coefs * (self.coefs[0] / new_coefs[0]) + logging.debug("a_delta {}".format(a_delta)) + logging.debug("new coefs {}".format(new_coefs)) + + tx_abs = np.abs(rxframe_aligned) + 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(txframe_aligned)) / np.median(np.abs(tx_dpd))) + + rx_A_complex = np.vstack([rxframe_aligned, + rxframe_aligned * rx_abs**2, + rxframe_aligned * rx_abs**4, + rxframe_aligned * rx_abs**6, + rxframe_aligned * rx_abs**8, + ]).T + rx_post_distored = np.sum(rx_A_complex * self.coefs, axis=1) + rx_post_distored = rx_post_distored * ( + np.median(np.abs(txframe_aligned)) / + np.median(np.abs(rx_post_distored))) + mse = np.mean(np.abs((txframe_aligned - 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, axis=1) + return tx_dpd + tx_inverse_dpd = inversefunc(dpd, y_values=txframe_aligned[:128]) + tx_inverse_dpd = tx_inverse_dpd * ( + np.median(np.abs(txframe_aligned)) / + np.median(np.abs(tx_inverse_dpd)) + ) + if logging.getLogger().getEffectiveLevel() == logging.DEBUG: logging.debug("txframe: min %f, max %f, median %f" % (np.min(np.abs(txframe_aligned)), @@ -29,21 +102,35 @@ class Model: )) dt = datetime.datetime.now().isoformat() - fig_path = "/tmp/" + dt + "_Model.pdf" + fig_path = logging_path + "/" + dt + "_Model.pdf" - fig, axs = plt.subplots(4, figsize=(6,2*6)) + fig, axs = plt.subplots(7, figsize=(6,3*6)) ax = axs[0] - ax.plot(np.abs(txframe_aligned[:128]), label="TX Frame") - ax.plot(np.abs(rxframe_aligned[:128]), label="RX Frame") - ax.set_title("Synchronized Signals") + ax.plot(np.abs(txframe_aligned[:128]), + label="TX sent", + linestyle=":") + ax.plot(np.abs(tx_inverse_dpd[:128]), + label="TX inverse dpd", + color="green") + 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.legend(loc=4) ax = axs[1] - ax.plot(np.real(txframe_aligned[:128]), label="TX Frame") - ax.plot(np.real(rxframe_aligned[:128]), label="RX Frame") + ax.plot(np.real(txframe_aligned[:128]), + label="TX sent", + linestyle=":") + ax.plot(np.real(tx_inverse_dpd[:128]), + label="TX inverse dpd", + color="green") + 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") @@ -53,8 +140,7 @@ class Model: ax.scatter( np.abs(txframe_aligned[:1024]), np.abs(rxframe_aligned[:1024]), - s = 0.1 - ) + s = 0.1) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") @@ -75,13 +161,54 @@ class Model: ax.set_xlabel("TX Amplitude") ax.set_ylabel("Phase Difference [deg]") + ax = axs[4] + 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) + ax.plot(np.abs(rx_dpd[:128]), + label="RX DPD Frame", + linestyle="-.", + linewidth=0.5) + 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 = axs[5] + 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("Coefficient History") + ax.set_xlabel("Iterations") + ax.set_ylabel("Coefficient Value") + + ax = axs[6] + 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() - mse = np.mean(np.abs(np.square(txframe_aligned[:1024] - rxframe_aligned[:1024]))) - logging.debug("MSE: {}".format(mse)) - + self.coefs = new_coefs + self.coefs_history.append(self.coefs) return self.coefs # The MIT License (MIT) |