diff options
| -rwxr-xr-x | dpd/main.py | 4 | ||||
| -rw-r--r-- | dpd/src/Model.py | 160 |
2 files changed, 126 insertions, 38 deletions
diff --git a/dpd/main.py b/dpd/main.py index 72026dc..b0b7a87 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -59,7 +59,7 @@ adapt = Adapt.Adapt(port_rc, coef_path) coefs = adapt.get_coefs() #model = Model.Model(coefs) model = Model.Model([2.2, 0, 0, 0, 0]) -adapt.set_txgain(82) +adapt.set_txgain(84) tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() @@ -70,7 +70,7 @@ logging.info( ) ) -for i in range(10): +for i in range(500): txframe_aligned, tx_ts, rxframe_aligned, rx_ts = meas.get_samples() logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) coefs = model.get_next_coefs(txframe_aligned, rxframe_aligned) diff --git a/dpd/src/Model.py b/dpd/src/Model.py index 7664dd3..aef112c 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -5,6 +5,7 @@ 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') @@ -17,32 +18,71 @@ 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): rx_abs = np.abs(rxframe_aligned) - A = np.vstack([rx_abs, + rx_A = np.vstack([rx_abs, rx_abs**3, rx_abs**5, rx_abs**7, rx_abs**9, ]).T - y = np.abs(txframe_aligned) - - clf = Ridge(alpha=10) - clf.fit(A, y) - sol = clf.coef_ - - rx_range = np.linspace(0,1,50) - A_range = np.vstack([ - rx_range, - rx_range**3, - rx_range**5, - rx_range**7, - rx_range**9, - ]).T - y_est = np.sum(A_range * sol, axis=1) - - logging.debug("New coefficents {}".format(sol)) + 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 + 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" % @@ -60,19 +100,33 @@ class Model: dt = datetime.datetime.now().isoformat() 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") @@ -82,13 +136,7 @@ class Model: ax.scatter( np.abs(txframe_aligned[:1024]), np.abs(rxframe_aligned[:1024]), - s = 0.1 - ) - ax.plot( - y_est, - rx_range, - linewidth=0.25 - ) + s = 0.1) ax.set_title("Amplifier Characteristic") ax.set_xlabel("TX Amplitude") ax.set_ylabel("RX Amplitude") @@ -109,15 +157,55 @@ 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)) - - sol = sol * 1.7/sol[0] - return sol + self.coefs = new_coefs + self.coefs_history.append(self.coefs) + return self.coefs # The MIT License (MIT) # |