diff options
| -rwxr-xr-x | dpd/main.py | 13 | ||||
| -rw-r--r-- | dpd/src/Model.py | 65 |
2 files changed, 53 insertions, 25 deletions
diff --git a/dpd/main.py b/dpd/main.py index 853f669..19460dd 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -23,6 +23,7 @@ logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message filemode='w', level=logging.DEBUG) +import traceback import src.Measure as Measure import src.Model as Model import src.Adapt as Adapt @@ -91,10 +92,14 @@ logging.info( ) for i in range(num_iter): - txframe_aligned, tx_ts, rxframe_aligned, rx_ts = meas.get_samples() - logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) - coefs_am, coefs_pm = model.get_next_coefs(txframe_aligned, rxframe_aligned) - adapt.set_coefs(coefs_am, coefs_pm) + try: + txframe_aligned, tx_ts, rxframe_aligned, rx_ts = meas.get_samples() + logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) + coefs_am, coefs_pm = model.get_next_coefs(txframe_aligned, rxframe_aligned) + adapt.set_coefs(coefs_am, coefs_pm) + except Exception as e: + logging.info("Iteration {} failed.".format(i)) + logging.info(traceback.format_exc()) # The MIT License (MIT) # diff --git a/dpd/src/Model.py b/dpd/src/Model.py index 64c8857..e0f9c62 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -26,9 +26,32 @@ class Model: 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)) + tx_choice = np.zeros(tx_hist[-1] * n_bins, dtype=np.complex64) + rx_choice = np.zeros(tx_hist[-1] * n_bins, dtype=np.complex64) + n_choise = tx_hist[-1] + for idx, bin in enumerate(tx_hist[:-1]): + indices = np.where((txframe_aligned >= ccdf_edges[idx]) & + (txframe_aligned <= 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(rxframe_aligned) + rx_abs = np.abs(rx_choice) rx_A = np.vstack([rx_abs, rx_abs ** 3, rx_abs ** 5, @@ -37,9 +60,9 @@ class Model: ]).T rx_dpd = np.sum(rx_A * self.coefs_am, axis=1) rx_dpd = rx_dpd * ( - np.median(np.abs(txframe_aligned)) / np.median(np.abs(rx_dpd))) + np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd))) - err = rx_dpd - np.abs(txframe_aligned) + 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] @@ -50,13 +73,13 @@ class Model: # Calculate new coefficients for AM/PM correction phase_diff_rad = (( - (np.angle(txframe_aligned) - - np.angle(rxframe_aligned) + + (np.angle(tx_choice) - + np.angle(rx_choice) + np.pi) % (2 * np.pi)) - np.pi ) - tx_abs = np.abs(txframe_aligned) + tx_abs = np.abs(tx_choice) tx_abs_A = np.vstack([tx_abs, tx_abs ** 2, tx_abs ** 3, @@ -86,7 +109,7 @@ class Model: tx_range = np.linspace(0, 2) phase_range_dpd = dpd_phase(tx_range) - tx_abs = np.abs(rxframe_aligned) + tx_abs = np.abs(rx_choice) tx_A = np.vstack([tx_abs, tx_abs ** 3, tx_abs ** 5, @@ -96,19 +119,19 @@ class Model: 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))) + np.median(np.abs(tx_choice)) / 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, + 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(txframe_aligned)) / + np.median(np.abs(tx_choice)) / np.median(np.abs(rx_post_distored))) - mse = np.mean(np.abs((txframe_aligned - rx_post_distored) ** 2)) + mse = np.mean(np.abs((tx_choice - rx_post_distored) ** 2)) logging.debug("MSE: {}".format(mse)) self.mses.append(mse) @@ -136,9 +159,9 @@ class Model: )) logging.debug("rxframe: min %f, max %f, median %f" % - (np.min(np.abs(rxframe_aligned)), - np.max(np.abs(rxframe_aligned)), - np.median(np.abs(rxframe_aligned)) + (np.min(np.abs(rx_choice)), + np.max(np.abs(rx_choice)), + np.median(np.abs(rx_choice)) )) dt = datetime.datetime.now().isoformat() @@ -199,8 +222,8 @@ class Model: ax = plt.subplot(3,3,4) ax.scatter( - np.abs(txframe_aligned[:1024]), - np.abs(rxframe_aligned[:1024]), + 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") @@ -209,7 +232,7 @@ class Model: ax = plt.subplot(3,3,5) ax.scatter( - np.abs(txframe_aligned[:1024]), + np.abs(tx_choice[:1024]), phase_diff_rad[:1024] * 180 / np.pi, s=0.1 ) |