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-rw-r--r--dpd/src/Model.py249
1 files changed, 69 insertions, 180 deletions
diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index dc526c5..bf52287 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -32,11 +32,11 @@ class Model:
self.plot=plot
- def sample_uniformly(self, txframe_aligned, rxframe_aligned, n_bins=4):
+ def sample_uniformly(self, tx_dpd, rx_received, 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)
+ distribution with respect to the tx_dpd amplitudes"""
+ 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,
@@ -50,219 +50,111 @@ class Model:
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]
+ 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]
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)
+ def amplitude_predistortion(self, sig):
+ sig_abs = np.abs(sig)
+ A_sig = np.vstack([np.ones(sig_abs.shape),
+ sig_abs ** 2,
+ sig_abs ** 4,
+ sig_abs ** 6,
+ sig_abs ** 8,
+ ]).T
+ sig_dpd = sig * np.sum(A_sig * self.coefs_am, axis=1)
+ return sig_dpd, A_sig
+
+ def dpd_phase(self, 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
+
+ def get_next_coefs(self, tx_dpd, rx_received):
+ 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)
# 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_A = self.amplitude_predistortion(rx_choice)
rx_dpd = rx_dpd * (
- np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd)))
+ np.median(np.abs(tx_choice)) /
+ np.median(np.abs(rx_dpd)))
- err = rx_dpd - np.abs(tx_choice)
+ err = np.abs(rx_dpd) - np.abs(tx_choice)
self.errs.append(np.mean(np.abs(err ** 2)))
+ mse = np.mean(np.abs((rx_dpd - tx_choice)**2))
+ self.mses.append(mse)
+
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])
+ assert np.abs(self.coefs_am[0] / new_coefs[0] - 1) < 0.1, \
+ "Too large change in first " \
+ "coefficient. {}, {}".format(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_dpd = self.amplitude_predistortion(rx_range)[0]
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)]
- 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))
- ))
+ logging.debug('txframe: min {:.2f}, max {:.2f}, ' \
+ 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \
+ 'median {:.2f}; a_delta {}; new coefs_am {}'.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)),
+ a_delta,
+ new_coefs))
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
dt = datetime.datetime.now().isoformat()
- fig_path = logging_path + "/" + dt + "_Model.pdf"
+ fig_path = logging_path + "/" + dt + "_Model.svg"
- fig = plt.figure(figsize=(3*6, 1.5 * 6))
+ fig = plt.figure(figsize=(3*6, 6))
- ax = plt.subplot(3,3,1)
- ax.plot(np.abs(txframe_aligned[:128]),
+ ax = plt.subplot(2,3,1)
+ ax.plot(np.abs(tx_dpd[:128]),
label="TX sent",
linestyle=":")
- ax.plot(np.abs(rxframe_aligned[:128]),
+ ax.plot(np.abs(rx_received[: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))
+ 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(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 = plt.subplot(2,3,2)
ax.scatter(
- np.abs(tx_choice[:1024]),
- np.abs(rx_choice[:1024]),
+ 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)
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)
+ ax = plt.subplot(2,3,3)
ccdf_min, ccdf_max = 0, 1
- tx_hist, ccdf_edges = np.histogram(np.abs(txframe_aligned),
+ 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)
@@ -278,7 +170,7 @@ class Model:
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("Ratio of Samples larger than x")
- ax = plt.subplot(3,3,7)
+ ax = plt.subplot(2,3,4)
coefs_history = np.array(self.coefs_history)
for idx, coef_hist in enumerate(coefs_history.T):
ax.plot(coef_hist,
@@ -289,7 +181,7 @@ class Model:
ax.set_xlabel("Iterations")
ax.set_ylabel("Coefficient Value")
- ax = plt.subplot(3,3,8)
+ ax = plt.subplot(2,3,5)
coefs_history = np.array(self.coefs_pm_history)
for idx, coef_hist in enumerate(coefs_history.T):
ax.plot(coef_hist,
@@ -300,8 +192,7 @@ class Model:
ax.set_xlabel("Iterations")
ax.set_ylabel("Coefficient Value")
- ax = plt.subplot(3,3,9)
- coefs_history = np.array(self.coefs_history)
+ ax = plt.subplot(2,3,6)
ax.plot(self.mses, label="MSE")
ax.plot(self.errs, label="ERR")
ax.legend(loc=4)
@@ -315,8 +206,6 @@ class Model:
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)
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-15 08:47:22 +0000