mpsk_snr_est.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.
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 * 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
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 * the Free Software Foundation, Inc., 51 Franklin Street,
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 */

#ifndef INCLUDED_DIGITAL_MPSK_SNR_EST_H
#define INCLUDED_DIGITAL_MPSK_SNR_EST_H

#include <gnuradio/digital/api.h>
#include <gnuradio/gr_complex.h>

namespace gr {
namespace digital {

/*!
 * \brief A block for computing SNR of a signal.
 * \ingroup measurement_tools_blk
 *
 * \details
 * Below are some ROUGH estimates of what values of SNR each of
 * these types of estimators is good for. In general, these offer
 * a trade-off between accuracy and performance.
 *
 * \li SNR_EST_SIMPLE:  Simple estimator (>= 7 dB)
 * \li SNR_EST_SKEW:    Skewness-base est (>= 5 dB)
 * \li SNR_EST_M2M4:    2nd & 4th moment est (>= 1 dB)
 * \li SNR_EST_SVR:     SVR-based est (>= 0dB)
 */
typedef enum {
    SNR_EST_SIMPLE = 0, // Simple estimator (>= 7 dB)
    SNR_EST_SKEW,       // Skewness-base est (>= 5 dB)
    SNR_EST_M2M4,       // 2nd & 4th moment est (>= 1 dB)
    SNR_EST_SVR         // SVR-based est (>= 0dB)
} snr_est_type_t;

/*! \brief A parent class for SNR estimators, specifically for
 *  M-PSK signals in AWGN channels.
 *  \ingroup snr_blk
 */
class DIGITAL_API mpsk_snr_est
{
protected:
    double d_alpha, d_beta;
    double d_signal, d_noise;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     */
    mpsk_snr_est(double alpha);
    virtual ~mpsk_snr_est();

    //! Get the running-average coefficient
    double alpha() const;

    //! Set the running-average coefficient
    void set_alpha(double alpha);

    //! Update the current registers
    virtual int update(int noutput_items, const gr_complex* input);

    //! Use the register values to compute a new estimate
    virtual double snr();

    //! Returns the signal power estimate
    virtual double signal();

    //! Returns the noise power estimate
    virtual double noise();
};


//! \brief SNR Estimator using simple mean/variance estimates.
/*! \ingroup snr_blk
 *
 *  A very simple SNR estimator that just uses mean and variance
 *  estimates of an M-PSK constellation. This estimator is quick
 *  and cheap and accurate for high SNR (above 7 dB or so) but
 *  quickly starts to overestimate the SNR at low SNR.
 */
class DIGITAL_API mpsk_snr_est_simple : public mpsk_snr_est
{
private:
    double d_y1, d_y2;
    double d_counter;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     */
    mpsk_snr_est_simple(double alpha);
    ~mpsk_snr_est_simple() {}

    int update(int noutput_items, const gr_complex* input);
    double snr();
};


//! \brief SNR Estimator using skewness correction.
/*!  \ingroup snr_blk
 *
 *  This is an estimator that came from a discussion between Tom
 *  Rondeau and fred harris with no known paper reference. The
 *  idea is that at low SNR, the variance estimations will be
 *  affected because of fold-over around the decision boundaries,
 *  which results in a skewness to the samples. We estimate the
 *  skewness and use this as a correcting term.
 *
 *  This algorithm only appears to work well for BPSK signals.
 */
class DIGITAL_API mpsk_snr_est_skew : public mpsk_snr_est
{
private:
    double d_y1, d_y2, d_y3;
    double d_counter;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     */
    mpsk_snr_est_skew(double alpha);
    ~mpsk_snr_est_skew() {}

    int update(int noutput_items, const gr_complex* input);
    double snr();
};


//! \brief SNR Estimator using 2nd and 4th-order moments.
/*! \ingroup snr_blk
 *
 *  An SNR estimator for M-PSK signals that uses 2nd (M2) and 4th
 *  (M4) order moments. This estimator uses knowledge of the
 *  kurtosis of the signal (\f$k_a)\f$ and noise (\f$k_w\f$) to make its
 *  estimation. We use Beaulieu's approximations here to M-PSK
 *  signals and AWGN channels such that \f$k_a=1\f$ and \f$k_w=2\f$. These
 *  approximations significantly reduce the complexity of the
 *  calculations (and computations) required.
 *
 *  Reference:
 *  D. R. Pauluzzi and N. C. Beaulieu, "A comparison of SNR
 *  estimation techniques for the AWGN channel," IEEE
 *  Trans. Communications, Vol. 48, No. 10, pp. 1681-1691, 2000.
 */
class DIGITAL_API mpsk_snr_est_m2m4 : public mpsk_snr_est
{
private:
    double d_y1, d_y2;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     */
    mpsk_snr_est_m2m4(double alpha);
    ~mpsk_snr_est_m2m4() {}

    int update(int noutput_items, const gr_complex* input);
    double snr();
};


//! \brief SNR Estimator using 2nd and 4th-order moments.
/*!  \ingroup snr_blk
 *
 *  An SNR estimator for M-PSK signals that uses 2nd (M2) and 4th
 *  (M4) order moments. This estimator uses knowledge of the
 *  kurtosis of the signal (k_a) and noise (k_w) to make its
 *  estimation. In this case, you can set your own estimations for
 *  k_a and k_w, the kurtosis of the signal and noise, to fit this
 *  estimation better to your signal and channel conditions.
 *
 *  A word of warning: this estimator has not been fully tested or
 *  proved with any amount of rigor. The estimation for M4 in
 *  particular might be ignoring effectf of when k_a and k_w are
 *  different. Use this estimator with caution and a copy of the
 *  reference on hand.
 *
 *  The digital_mpsk_snr_est_m2m4 assumes k_a and k_w to simplify
 *  the computations for M-PSK and AWGN channels. Use that
 *  estimator unless you have a way to guess or estimate these
 *  values here.
 *
 *  Original paper:
 *  R. Matzner, "An SNR estimation algorithm for complex baseband
 *  signal using higher order statistics," Facta Universitatis
 *  (Nis), no. 6, pp. 41-52, 1993.
 *
 *  Reference used in derivation:
 *  D. R. Pauluzzi and N. C. Beaulieu, "A comparison of SNR
 *  estimation techniques for the AWGN channel," IEEE
 *  Trans. Communications, Vol. 48, No. 10, pp. 1681-1691, 2000.
 */
class DIGITAL_API snr_est_m2m4 : public mpsk_snr_est
{
private:
    double d_y1, d_y2;
    double d_ka, d_kw;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     *  \param ka: estimate of the signal kurtosis (1 for PSK)
     *  \param kw: estimate of the channel noise kurtosis (2 for AWGN)
     */
    snr_est_m2m4(double alpha, double ka, double kw);
    ~snr_est_m2m4() {}

    int update(int noutput_items, const gr_complex* input);
    double snr();
};


//! \brief Signal-to-Variation Ratio SNR Estimator.
/*! \ingroup snr_blk
 *
 *  This estimator actually comes from an SNR estimator for M-PSK
 *  signals in fading channels, but this implementation is
 *  specifically for AWGN channels. The math was simplified to
 *  assume a signal and noise kurtosis (\f$k_a\f$ and \f$k_w\f$) for M-PSK
 *  signals in AWGN. These approximations significantly reduce the
 *  complexity of the calculations (and computations) required.
 *
 *  Original paper:
 *  A. L. Brandao, L. B. Lopes, and D. C. McLernon, "In-service
 *  monitoring of multipath delay and cochannel interference for
 *  indoor mobile communication systems," Proc. IEEE
 *  Int. Conf. Communications, vol. 3, pp. 1458-1462, May 1994.
 *
 *  Reference:
 *  D. R. Pauluzzi and N. C. Beaulieu, "A comparison of SNR
 *  estimation techniques for the AWGN channel," IEEE
 *  Trans. Communications, Vol. 48, No. 10, pp. 1681-1691, 2000.
 */
class DIGITAL_API mpsk_snr_est_svr : public mpsk_snr_est
{
private:
    double d_y1, d_y2;

public:
    /*! Constructor
     *
     *  Parameters:
     *  \param alpha: the update rate of internal running average
     *  calculations.
     */
    mpsk_snr_est_svr(double alpha);
    ~mpsk_snr_est_svr() {}

    int update(int noutput_items, const gr_complex* input);
    double snr();
};

} /* namespace digital */
} /* namespace gr */

#endif /* INCLUDED_DIGITAL_MPSK_SNR_EST_H */