diff options
| -rwxr-xr-x | dpd/main.py | 81 | ||||
| -rw-r--r-- | dpd/src/Adapt.py | 30 | ||||
| -rw-r--r-- | dpd/src/Agc.py | 6 | ||||
| -rw-r--r-- | dpd/src/Dab_Util.py | 25 | ||||
| -rw-r--r-- | dpd/src/MER.py | 5 | ||||
| -rw-r--r-- | dpd/src/Model.py | 481 | ||||
| -rw-r--r-- | dpd/src/TX_Agc.py | 109 | ||||
| -rw-r--r-- | dpd/src/phase_align.py | 8 | ||||
| -rwxr-xr-x | dpd/src/subsample_align.py | 6 |
9 files changed, 465 insertions, 286 deletions
diff --git a/dpd/main.py b/dpd/main.py index cfd8126..5e67c90 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -12,10 +12,15 @@ predistortion module of ODR-DabMod.""" import datetime import os +import time + +import matplotlib +matplotlib.use('GTKAgg') import logging + dt = datetime.datetime.now().isoformat() -logging_path = "/tmp/dpd_{}".format(dt).replace(".","_").replace(":","-") +logging_path = "/tmp/dpd_{}".format(dt).replace(".", "_").replace(":", "-") os.makedirs(logging_path) logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S', @@ -33,27 +38,33 @@ console.setFormatter(formatter) # add the handler to the root logger logging.getLogger('').addHandler(console) +import numpy as np import traceback import src.Measure as Measure import src.Model as Model import src.Adapt as Adapt import src.Agc as Agc +import src.TX_Agc as TX_Agc +import src.Symbol_align +import src.const +import src.MER import argparse -parser = argparse.ArgumentParser(description="DPD Computation Engine for ODR-DabMod") +parser = argparse.ArgumentParser( + description="DPD Computation Engine for ODR-DabMod") parser.add_argument('--port', default='50055', - help='port of DPD server to connect to (default: 50055)', - required=False) + help='port of DPD server to connect to (default: 50055)', + required=False) parser.add_argument('--rc-port', default='9400', - help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)', - required=False) + help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)', + required=False) parser.add_argument('--samplerate', default='8192000', - help='Sample rate', - required=False) + help='Sample rate', + required=False) parser.add_argument('--coefs', default='poly.coef', - help='File with DPD coefficients, which will be read by ODR-DabMod', - required=False) -parser.add_argument('--txgain', default=65, + help='File with DPD coefficients, which will be read by ODR-DabMod', + required=False) +parser.add_argument('--txgain', default=71, help='TX Gain', required=False, type=int) @@ -61,47 +72,62 @@ parser.add_argument('--rxgain', default=30, help='TX Gain', required=False, type=int) -parser.add_argument('--samps', default='20480', - help='Number of samples to request from ODR-DabMod', - required=False) +parser.add_argument('--digital_gain', default=1, + help='Digital Gain', + required=False, + type=float) +parser.add_argument('--samps', default='81920', + help='Number of samples to request from ODR-DabMod', + required=False) parser.add_argument('-i', '--iterations', default='1', - help='Number of iterations to run', - required=False) + help='Number of iterations to run', + required=False) parser.add_argument('-l', '--load-poly', - help='Load existing polynomial', - action="store_true") + help='Load existing polynomial', + action="store_true") cli_args = parser.parse_args() port = int(cli_args.port) port_rc = int(cli_args.rc_port) coef_path = cli_args.coefs +digital_gain = cli_args.digital_gain txgain = cli_args.txgain rxgain = cli_args.rxgain num_req = int(cli_args.samps) samplerate = int(cli_args.samplerate) num_iter = int(cli_args.iterations) +SA = src.Symbol_align.Symbol_align(samplerate) +MER = src.MER.MER(samplerate) +c = src.const.const(samplerate) + meas = Measure.Measure(samplerate, port, num_req) adapt = Adapt.Adapt(port_rc, coef_path) coefs_am, coefs_pm = adapt.get_coefs() if cli_args.load_poly: - model = Model.Model(coefs_am, coefs_pm) + model = Model.Model(c, SA, MER, coefs_am, coefs_pm, plot=True) else: - model = Model.Model([1, 0, 0, 0, 0], [0, 0, 0, 0, 0]) + model = Model.Model(c, SA, MER, [1.0, 0, 0, 0, 0], [0, 0, 0, 0, 0], plot=True) +adapt.set_coefs(model.coefs_am, model.coefs_pm) +adapt.set_digital_gain(digital_gain) adapt.set_txgain(txgain) adapt.set_rxgain(rxgain) tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() +digital_gain = adapt.get_digital_gain() dpd_coefs_am, dpd_coefs_pm = adapt.get_coefs() logging.info( - "TX gain {}, RX gain {}, dpd_coefs_am {}, dpd_coefs_pm {}".format( - tx_gain, rx_gain, dpd_coefs_am, dpd_coefs_pm + "TX gain {}, RX gain {}, dpd_coefs_am {}," + " dpd_coefs_pm {}, digital_gain {}".format( + tx_gain, rx_gain, dpd_coefs_am, dpd_coefs_pm, digital_gain ) ) +tx_agc = TX_Agc.TX_Agc(adapt) + # Automatic Gain Control agc = Agc.Agc(meas, adapt) agc.run() @@ -110,8 +136,19 @@ for i in range(num_iter): try: txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) + assert tx_ts - rx_ts < 1e-5, "Time stamps do not match." + + if tx_agc.adapt_if_necessary(txframe_aligned): + continue + coefs_am, coefs_pm = model.get_next_coefs(txframe_aligned, rxframe_aligned) adapt.set_coefs(coefs_am, coefs_pm) + + off = SA.calc_offset(txframe_aligned) + tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U]) + rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug=True) + logging.info("MER with lag in it. {}: TX {}, RX {}". + format(i, tx_mer, rx_mer)) except Exception as e: logging.warning("Iteration {} failed.".format(i)) logging.warning(traceback.format_exc()) diff --git a/dpd/src/Adapt.py b/dpd/src/Adapt.py index 2fb596f..b4042d6 100644 --- a/dpd/src/Adapt.py +++ b/dpd/src/Adapt.py @@ -24,7 +24,7 @@ class Adapt: """ def __init__(self, port, coef_path): - logging.info("Instantiate Adapt object") + logging.debug("Instantiate Adapt object") self.port = port self.coef_path = coef_path self.host = "localhost" @@ -60,7 +60,7 @@ class Adapt: The message string that will be sent to the receiver. """ sock = self._connect() - logging.info("Send message: %s" % message) + logging.debug("Send message: %s" % message) msg_parts = message.split(" ") for i, part in enumerate(msg_parts): if i == len(msg_parts) - 1: @@ -71,7 +71,7 @@ class Adapt: sock.send(part.encode(), flags=f) data = [el.decode() for el in sock.recv_multipart()] - logging.info("Received message: %s" % message) + logging.debug("Received message: %s" % message) return data def set_txgain(self, gain): @@ -85,12 +85,12 @@ class Adapt: # TODO this is specific to the B200 if gain < 0 or gain > 89: raise ValueError("Gain has to be in [0,89]") - return self.send_receive("set uhd txgain %d" % gain) + return self.send_receive("set uhd txgain %.4f" % float(gain)) def get_txgain(self): """Get the txgain value in dB for the ODR-DabMod.""" # TODO handle failure - return self.send_receive("get uhd txgain") + return float(self.send_receive("get uhd txgain")[0]) def set_rxgain(self, gain): """Set a new rxgain for the ODR-DabMod. @@ -103,12 +103,28 @@ class Adapt: # TODO this is specific to the B200 if gain < 0 or gain > 89: raise ValueError("Gain has to be in [0,89]") - return self.send_receive("set uhd rxgain %d" % gain) + return self.send_receive("set uhd rxgain %.4f" % float(gain)) def get_rxgain(self): """Get the rxgain value in dB for the ODR-DabMod.""" # TODO handle failure - return self.send_receive("get uhd rxgain") + return float(self.send_receive("get uhd rxgain")[0]) + + def set_digital_gain(self, gain): + """Set a new rxgain for the ODR-DabMod. + + Parameters + ---------- + gain : int + new RX gain, in the same format as ODR-DabMod's config file + """ + msg = "set gain digital %.5f" % gain + return self.send_receive(msg) + + def get_digital_gain(self): + """Get the rxgain value in dB for the ODR-DabMod.""" + # TODO handle failure + return float(self.send_receive("get gain digital")[0]) def _read_coef_file(self, path): """Load the coefficients from the file in the format given in the README, diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py index 1fd11c8..978b607 100644 --- a/dpd/src/Agc.py +++ b/dpd/src/Agc.py @@ -47,7 +47,7 @@ class Agc: def run(self): self.adapt.set_rxgain(self.rxgain) - for i in range(3): + for i in range(2): # Measure txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median= \ self.measure.get_samples() @@ -65,7 +65,7 @@ class Agc: )) self.adapt.set_rxgain(self.rxgain) - time.sleep(1) + time.sleep(0.5) def plot_estimates(self): """Plots the estimate of for Max, Median, Mean for different @@ -74,7 +74,7 @@ class Agc: time.sleep(1) dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_agc.pdf" + fig_path = logging_path + "/" + dt + "_agc.svg" fig, axs = plt.subplots(2, 2, figsize=(3*6,1*6)) axs = axs.ravel() diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py index b0b3ce3..e3dbfe3 100644 --- a/dpd/src/Dab_Util.py +++ b/dpd/src/Dab_Util.py @@ -23,7 +23,7 @@ class Dab_Util: """Collection of methods that can be applied to an array complex IQ samples of a DAB signal """ - def __init__(self, sample_rate): + def __init__(self, sample_rate, plot=False): """ :param sample_rate: sample rate [sample/sec] to use for calculations """ @@ -31,6 +31,8 @@ class Dab_Util: self.dab_bandwidth = 1536000 #Bandwidth of a dab signal self.frame_ms = 96 #Duration of a Dab frame + self.plot=plot + def lag(self, sig_orig, sig_rec): """ Find lag between two signals @@ -41,9 +43,9 @@ class Dab_Util: off = sig_rec.shape[0] c = np.abs(signal.correlate(sig_orig, sig_rec)) - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() - corr_path = (logging_path + "/" + dt + "_tx_rx_corr.pdf") + corr_path = (logging_path + "/" + dt + "_tx_rx_corr.svg") plt.plot(c, label="corr") plt.legend() plt.savefig(corr_path) @@ -95,9 +97,9 @@ class Dab_Util: Returns an aligned version of sig_tx and sig_rx by cropping and subsample alignment """ - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_sync_raw.pdf" + fig_path = logging_path + "/" + dt + "_sync_raw.svg" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame") @@ -139,9 +141,9 @@ class Dab_Util: sig_tx = sig_tx[:-1] sig_rx = sig_rx[:-1] - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_sync_sample_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_sample_aligned.svg" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame") @@ -161,12 +163,11 @@ class Dab_Util: fig.savefig(fig_path) fig.clf() - sig_rx = sa.subsample_align(sig_rx, sig_tx) - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.svg" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame") @@ -188,9 +189,9 @@ class Dab_Util: sig_rx = pa.phase_align(sig_rx, sig_tx) - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_sync_phase_aligned.pdf" + fig_path = logging_path + "/" + dt + "_sync_phase_aligned.svg" fig, axs = plt.subplots(2) axs[0].plot(np.abs(sig_tx[:128]), label="TX Frame") diff --git a/dpd/src/MER.py b/dpd/src/MER.py index 54ef6c6..00fcc23 100644 --- a/dpd/src/MER.py +++ b/dpd/src/MER.py @@ -75,8 +75,9 @@ class MER: spectrum = self._calc_spectrum(tx) - dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_MER.pdf" + if debug: + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_MER.svg" MERs = [] for i, (x, y) in enumerate(self._split_in_carrier( diff --git a/dpd/src/Model.py b/dpd/src/Model.py index 2aa9feb..827027a 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -8,262 +8,221 @@ import datetime import os import logging + logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) import numpy as np -import matplotlib -matplotlib.use('agg') import matplotlib.pyplot as plt -from sklearn.linear_model import Ridge +from sklearn import linear_model class Model: """Calculates new coefficients using the measurement and the old coefficients""" - def __init__(self, coefs_am, coefs_pm): + def __init__(self, + c, + SA, + MER, + coefs_am, + coefs_pm, + learning_rate_am=1., + learning_rate_pm=1., + plot=False): + self.c = c + self.SA = SA + self.MER = MER + + self.learning_rate_am = learning_rate_am + self.learning_rate_pm = learning_rate_pm + self.coefs_am = coefs_am - self.coefs_history = [coefs_am, ] - self.mses = [0, ] - self.errs = [0, ] + self.coefs_am_history = [coefs_am, ] + self.mses_am = [] + self.errs_am = [] + + self.tx_mers = [] + self.rx_mers = [] self.coefs_pm = coefs_pm self.coefs_pm_history = [coefs_pm, ] - self.errs_phase = [0, ] + self.errs_pm = [] + + self.plot = plot - def sample_uniformly(self, txframe_aligned, rxframe_aligned, n_bins=4): + def sample_uniformly(self, tx_dpd, rx_received, n_bins=5): """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""" + mask = np.logical_and((np.abs(tx_dpd) > 0.01), (np.abs(rx_received) > 0.01)) + tx_dpd = tx_dpd[mask] + rx_received = rx_received[mask] + + 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, bins=n_bins, range=(ccdf_min, ccdf_max)) - n_choise = np.min(tx_hist) + 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] + (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] = \ + rx_received[indices_choise] + tx_choice[idx * n_choise:(idx + 1) * n_choise] = \ + tx_dpd[indices_choise] + + assert isinstance(rx_choice[0], np.complex64), \ + "rx_choice is not complex64 but {}".format(rx_choice[0].dtype) + assert isinstance(tx_choice[0], np.complex64), \ + "tx_choice is not complex64 but {}".format(tx_choice[0].dtype) + 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, + def dpd_amplitude(self, sig, coefs=None): + if coefs is None: + coefs = self.coefs_am + assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) + sig_abs = np.abs(sig) + A_sig = np.vstack([np.ones(sig_abs.shape), + sig_abs ** 1, + sig_abs ** 2, + sig_abs ** 3, + sig_abs ** 4, + ]).T + sig_dpd = sig * np.sum(A_sig * coefs, axis=1) + return sig_dpd, A_sig + + def dpd_phase(self, sig, coefs=None): + if coefs is None: + coefs = self.coefs_pm + assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) + sig_abs = np.abs(sig) + A_phase = np.vstack([np.ones(sig_abs.shape), + sig_abs ** 1, + sig_abs ** 2, + sig_abs ** 3, + sig_abs ** 4, ]).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)) - )) + phase_diff_est = np.sum(A_phase * coefs, axis=1) + return phase_diff_est, A_phase + def _next_am_coefficent(self, tx_choice, rx_choice): + """Calculate new coefficients for AM/AM correction""" + rx_dpd, rx_A = self.dpd_amplitude(rx_choice) + rx_dpd = rx_dpd * ( + np.median(np.abs(tx_choice)) / + np.median(np.abs(rx_dpd))) + err = np.abs(rx_dpd) - np.abs(tx_choice) + mse = np.mean(np.abs((rx_dpd - tx_choice) ** 2)) + self.mses_am.append(mse) + self.errs_am.append(np.mean(err**2)) + + reg = linear_model.Ridge(alpha=0.00001) + reg.fit(rx_A, err) + a_delta = reg.coef_ + new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta + new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0]) + return new_coefs_am + + def _next_pm_coefficent(self, tx_choice, rx_choice): + """Calculate new coefficients for AM/PM correction + Assuming deviations smaller than pi/2""" + phase_diff_choice = np.angle( + (rx_choice * tx_choice.conjugate()) / + (np.abs(rx_choice) * np.abs(tx_choice)) + ) + plt.hist(phase_diff_choice) + plt.savefig('/tmp/hist_' + str(np.random.randint(0,1000)) + '.svg') + plt.clf() + phase_diff_est, phase_A = self.dpd_phase(rx_choice) + err_phase = phase_diff_est - phase_diff_choice + self.errs_pm.append(np.mean(np.abs(err_phase ** 2))) + + reg = linear_model.Ridge(alpha=0.00001) + reg.fit(phase_A, err_phase) + p_delta = reg.coef_ + new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta + + return new_coefs_pm, phase_diff_choice + + def get_next_coefs(self, tx_dpd, rx_received): + # Check data type + assert tx_dpd[0].dtype == np.complex64, \ + "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype) + assert rx_received[0].dtype == np.complex64, \ + "rx_received is not complex64 but {}".format(rx_received[0].dtype) + # Check if signals have same normalization + 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) + new_coefs_am = self._next_am_coefficent(tx_choice, rx_choice) + new_coefs_pm, phase_diff_choice = self._next_pm_coefficent(tx_choice, rx_choice) + + logging.debug('txframe: min {:.2f}, max {:.2f}, ' \ + 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \ + 'median {:.2f}; new coefs_am {};' \ + 'new_coefs_pm {}'.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)), + new_coefs_am, + new_coefs_pm)) + + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: + off = self.SA.calc_offset(tx_dpd) + tx_mer = self.MER.calc_mer(tx_dpd[off:off + self.c.T_U]) + rx_mer = self.MER.calc_mer(rx_received[off:off + self.c.T_U], debug=True) + self.tx_mers.append(tx_mer) + self.rx_mers.append(rx_mer) + + 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=(2 * 6, 2 * 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) + i_sub = 1 - ax = plt.subplot(3,3,2) - ax.plot(np.real(txframe_aligned[:128]), + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 + ax.plot(np.abs(tx_dpd[:128]), label="TX sent", linestyle=":") - ax.plot(np.real(rxframe_aligned[:128]), + ax.plot(np.abs(rx_received[: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_title("Synchronized Signals of Iteration {}" + .format(len(self.coefs_am_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(tx_dpd)), + np.mean(np.abs(tx_dpd)), + np.median(np.abs(tx_dpd)) + ), size=8) + ax.legend(loc=4) - ax = plt.subplot(3,3,4) - 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) + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 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) + 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) ccdf = 1.0 - np.cumsum(tx_hist_normalized) ax.semilogy(ccdf_edges[:-1], ccdf, label="CCDF") ax.semilogy(ccdf_edges[:-1], @@ -271,14 +230,51 @@ class Model: label="Histogram", drawstyle='steps') ax.legend(loc=4) - ax.set_ylim(1e-5,2) + 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 = plt.subplot(4, 2, i_sub) + i_sub += 1 + ax.semilogy(np.array(self.mses_am) + 1e-10, label="log(MSE)") + ax.semilogy(np.array(self.errs_am) + 1e-10, label="log(ERR)") + ax.legend(loc=4) + ax.set_title("MSE History") + ax.set_xlabel("Iterations") + ax.set_ylabel("MSE") + + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 + ax.plot(self.tx_mers, label="TX MER") + ax.plot(self.rx_mers, label="RX MER") + ax.legend(loc=4) + ax.set_title("MER History") + ax.set_xlabel("Iterations") + ax.set_ylabel("MER") + + ax = plt.subplot(4, 2, i_sub) + rx_range = np.linspace(0, 1, num=100, dtype=np.complex64) + rx_range_dpd = self.dpd_amplitude(rx_range)[0] + rx_range_dpd_new = self.dpd_amplitude(rx_range, new_coefs_am)[0] + i_sub += 1 + ax.scatter( + 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, label="current") + ax.plot(rx_range_dpd_new / self.coefs_am[0], rx_range, linewidth=0.25, label="next") + ax.set_ylim(0, 1) + ax.set_xlim(0, 1) + ax.legend() + ax.set_title("Amplifier Characteristic") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("RX Amplitude") + + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 + coefs_am_history = np.array(self.coefs_am_history) + for idx, coef_hist in enumerate(coefs_am_history.T): ax.plot(coef_hist, label="Coef {}".format(idx), linewidth=0.5) @@ -287,10 +283,38 @@ class Model: 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, + phase_range = np.linspace(0, 1, num=100, dtype=np.complex64) + phase_range_dpd = self.dpd_phase(phase_range)[0] + phase_range_dpd_new = self.dpd_phase(phase_range, + coefs=new_coefs_pm)[0] + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 + ax.scatter( + np.abs(tx_choice), + np.rad2deg(phase_diff_choice), + s=0.1) + ax.plot( + np.abs(phase_range), + np.rad2deg(phase_range_dpd), + linewidth=0.25, + label="current") + ax.plot( + np.abs(phase_range), + np.rad2deg(phase_range_dpd_new), + linewidth=0.25, + label="next") + ax.set_ylim(-60, 60) + ax.set_xlim(0, 1) + ax.legend() + ax.set_title("Amplifier Characteristic") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Phase Difference") + + ax = plt.subplot(4, 2, i_sub) + i_sub += 1 + coefs_pm_history = np.array(self.coefs_pm_history) + for idx, coef_phase_hist in enumerate(coefs_pm_history.T): + ax.plot(coef_phase_hist, label="Coef {}".format(idx), linewidth=0.5) ax.legend(loc=4) @@ -298,21 +322,12 @@ 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.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_am = new_coefs_am + self.coefs_am_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 diff --git a/dpd/src/TX_Agc.py b/dpd/src/TX_Agc.py new file mode 100644 index 0000000..9274612 --- /dev/null +++ b/dpd/src/TX_Agc.py @@ -0,0 +1,109 @@ +# -*- coding: utf-8 -*- +# +# Automatic Gain Control +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import datetime +import os +import logging +import time + +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +import numpy as np +import matplotlib + +matplotlib.use('agg') +import matplotlib.pyplot as plt + +import src.Adapt as Adapt + +#TODO fix for float tx_gain +class TX_Agc: + def __init__(self, + adapt, + max_txgain=89, + tx_median_target=0.1, + tx_median_threshold_max=0.12, + tx_median_threshold_min=0.08): + """ + In order to avoid digital clipping, this class increases the + TX gain and reduces the digital gain. Digital clipping happens + when the digital analog converter receives values greater than + it's maximal output. This class solves that problem by adapting + the TX gain in a way that the peaks of the TX signal are in a + specified range. The TX gain is adapted accordingly. The TX peaks + are approximated by estimating it based on the signal median. + + :param adapt: Instance of Adapt Class to update + txgain and coefficients + :param max_txgain: limit for TX gain + :param tx_median_threshold_max: if the median of TX is larger + than this value, then the digital gain is reduced + :param tx_median_threshold_min: if the median of TX is smaller + than this value, then the digital gain is increased + :param tx_median_target: The digital gain is reduced in a way that + the median TX value is expected to be lower than this value. + """ + assert isinstance(adapt, Adapt.Adapt) + self.adapt = adapt + self.max_txgain = max_txgain + self.txgain = self.max_txgain + + assert tx_median_threshold_max > tx_median_target,\ + "The tolerated tx_median has to be larger then the goal tx_median" + self.tx_median_threshold_tolerate_max = tx_median_threshold_max + self.tx_median_threshold_tolerate_min = tx_median_threshold_min + self.tx_median_target = tx_median_target + + def adapt_if_necessary(self, tx): + tx_median = np.median(np.abs(tx)) + if tx_median > self.tx_median_threshold_tolerate_max or\ + tx_median < self.tx_median_threshold_tolerate_min: + delta_db = 20 * np.log10(self.tx_median_target / tx_median) + new_txgain = self.adapt.get_txgain() - delta_db + assert new_txgain < self.max_txgain,\ + "TX_Agc failed. New TX gain of {} is too large.".format( + new_txgain + ) + self.adapt.set_txgain(new_txgain) + txgain = self.adapt.get_txgain() + + digital_gain_factor = 10 ** (delta_db / 20.) + digital_gain = self.adapt.get_digital_gain() * digital_gain_factor + self.adapt.set_digital_gain(digital_gain) + + logging.info( + "digital_gain = {}, txgain_new = {}, "\ + "delta_db = {}, tx_median {}, "\ + "digital_gain_factor = {}". + format(digital_gain, txgain, delta_db, + tx_median, digital_gain_factor)) + + time.sleep(1) + return True + return False + +# 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. diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py index 7045586..7f82392 100644 --- a/dpd/src/phase_align.py +++ b/dpd/src/phase_align.py @@ -15,7 +15,7 @@ import sys import matplotlib.pyplot as plt -def phase_align(sig, ref_sig): +def phase_align(sig, ref_sig, plot=False): """Do phase alignment for sig relative to the reference signal ref_sig. @@ -26,9 +26,9 @@ def phase_align(sig, ref_sig): real_diffs = np.cos(angle_diff) imag_diffs = np.sin(angle_diff) - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and plot: dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_phase_align.pdf" + fig_path = logging_path + "/" + dt + "_phase_align.svg" plt.subplot(511) plt.hist(angle_diff, bins=60, label="Angle Diff") @@ -66,7 +66,7 @@ def phase_align(sig, ref_sig): )) sig = sig * np.exp(1j * -angle) - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and plot: plt.subplot(515) plt.plot(np.angle(ref_sig[:128]), label="ref_sig") plt.plot(np.angle(sig[:128]), label="sig") diff --git a/dpd/src/subsample_align.py b/dpd/src/subsample_align.py index d0f3dba..6d1cd2a 100755 --- a/dpd/src/subsample_align.py +++ b/dpd/src/subsample_align.py @@ -29,7 +29,7 @@ def gen_omega(length): return omega -def subsample_align(sig, ref_sig): +def subsample_align(sig, ref_sig, plot=False): """Do subsample alignment for sig relative to the reference signal ref_sig. The delay between the two must be less than sample @@ -68,13 +68,13 @@ def subsample_align(sig, ref_sig): if optim_result.success: best_tau = optim_result.x - if 1: + if plot: corr = np.vectorize(correlate_for_delay) ixs = np.linspace(-1, 1, 100) taus = corr(ixs) dt = datetime.datetime.now().isoformat() - tau_path = (logging_path + "/" + dt + "_tau.pdf") + tau_path = (logging_path + "/" + dt + "_tau.svg") plt.plot(ixs, taus) plt.title("Subsample correlation, minimum is best: {}".format(best_tau)) plt.savefig(tau_path) |