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/* -*- c++ -*- */
/*
* Copyright 2011,2012 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 <digital/api.h>
#include <gr_sync_decimator.h>
#include <digital/constellation.h>
namespace gr {
namespace digital {
/*!
* \brief Least-Mean-Square Decision Directed Equalizer (complex in/out)
* \ingroup eq_blk
* \ingroup digital
*
* 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_API lms_dd_equalizer_cc :
virtual public gr_sync_decimator
{
protected:
virtual gr_complex error(const gr_complex &out) = 0;
virtual void update_tap(gr_complex &tap, const gr_complex &in) = 0;
public:
// gr::digital::lms_dd_equalizer_cc::sptr
typedef boost::shared_ptr<lms_dd_equalizer_cc> sptr;
/*!
* Make an LMS decision-directed equalizer
*
* \param num_taps Numer of taps in the equalizer (channel size)
* \param mu Gain of the update loop
* \param sps Number of samples per symbol of the input signal
* \param cnst A constellation derived from class
* 'constellation'. Use base() method to get a shared pointer to
* this base class type.
*/
static sptr make(int num_taps,
float mu, int sps,
constellation_sptr cnst);
virtual void set_taps(const std::vector<gr_complex> &taps) = 0;
virtual std::vector<gr_complex> taps() const = 0;
virtual float gain() const = 0;
virtual void set_gain(float mu) = 0;
};
} /* namespace digital */
} /* namespace gr */
#endif /* INCLUDED_DIGITAL_LMS_DD_EQUALIZER_CC_H */
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