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/* -*- c++ -*- */
/*
* Copyright 2011 Free Software Foundation, Inc.
*
* This file is part of GNU Radio
*
* GNU Radio is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3, or (at your option)
* any later version.
*
* GNU Radio is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with GNU Radio; see the file COPYING. If not, write to
* the Free Software Foundation, Inc., 51 Franklin Street,
* Boston, MA 02110-1301, USA.
*/
#ifndef INCLUDED_DIGITAL_LMS_DD_EQUALIZER_CC_H
#define INCLUDED_DIGITAL_LMS_DD_EQUALIZER_CC_H
#include <gr_adaptive_fir_ccc.h>
#include <digital_constellation.h>
class digital_lms_dd_equalizer_cc;
typedef boost::shared_ptr<digital_lms_dd_equalizer_cc> digital_lms_dd_equalizer_cc_sptr;
digital_lms_dd_equalizer_cc_sptr digital_make_lms_dd_equalizer_cc (int num_taps,
float mu, int sps,
digital_constellation_sptr cnst);
/*!
* \brief Least-Mean-Square Decision Directed Equalizer (complex in/out)
* \ingroup eq_blk
*
* This block implements an LMS-based decision-directed equalizer.
* It uses a set of weights, w, to correlate against the inputs, u,
* and a decisions is then made from this output. The error
* in the decision is used to update teh weight vector.
*
* y[n] = conj(w[n]) u[n]
* d[n] = decision(y[n])
* e[n] = d[n] - y[n]
* w[n+1] = w[n] + mu u[n] conj(e[n])
*
* Where mu is a gain value (between 0 and 1 and usualy small,
* around 0.001 - 0.01.
*
* This block uses the digital_constellation object for making
* the decision from y[n]. Create the constellation object for
* whatever constellation is to be used and pass in the object.
* In Python, you can use something like:
* self.constellation = digital.constellation_qpsk()
* To create a QPSK constellation (see the digital_constellation
* block for more details as to what constellations are available
* or how to create your own). You then pass the object to this
* block as an sptr, or using "self.constellation.base()".
*
* The theory for this algorithm can be found in Chapter 9 of:
* S. Haykin, Adaptive Filter Theory, Upper Saddle River, NJ:
* Prentice Hall, 1996.
*
*/
class digital_lms_dd_equalizer_cc : public gr_adaptive_fir_ccc
{
private:
friend digital_lms_dd_equalizer_cc_sptr digital_make_lms_dd_equalizer_cc (int num_taps,
float mu, int sps,
digital_constellation_sptr cnst);
float d_mu;
std::vector<gr_complex> d_taps;
digital_constellation_sptr d_cnst;
digital_lms_dd_equalizer_cc (int num_taps,
float mu, int sps,
digital_constellation_sptr cnst);
protected:
virtual gr_complex error(const gr_complex &out)
{
gr_complex decision, error;
d_cnst->map_to_points(d_cnst->decision_maker(&out), &decision);
error = decision - out;
return error;
}
virtual void update_tap(gr_complex &tap, const gr_complex &in)
{
tap += d_mu*conj(in)*d_error;
}
public:
float get_gain()
{
return d_mu;
}
void set_gain(float mu)
{
if(mu < 0.0f || mu > 1.0f) {
throw std::out_of_range("digital_lms_dd_equalizer::set_mu: Gain value must in [0, 1]");
}
else {
d_mu = mu;
}
}
};
#endif
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