lms_dd_equalizer_cc.h

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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 <gnuradio/digital/api.h>
#include <gnuradio/digital/constellation.h>
#include <gnuradio/sync_decimator.h>

namespace gr {
namespace digital {

/*!
 * \brief Least-Mean-Square Decision Directed Equalizer (complex in/out)
 * \ingroup equalizers_blk
 *
 * \details
 * 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 the 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 usually 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 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 Number 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 */