/* -*- 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,
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*/
#ifndef INCLUDED_DIGITAL_MPSK_SNR_EST_H
#define INCLUDED_DIGITAL_MPSK_SNR_EST_H
#include <digital/api.h>
#include <gr_complex.h>
namespace gr {
namespace digital {
//! Enum for the type of SNR estimator to select
/*! \ingroup snr_blk
* \anchor ref_snr_est_types
*
* 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;
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();
};
//! \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 esimator 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;
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.
*/
class DIGITAL_API mpsk_snr_est_skew :
public mpsk_snr_est
{
private:
double d_y1, d_y2, d_y3;
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 (k_a) and noise (k_w) to make its
* estimation. We use Beaulieu's approximations here to M-PSK
* signals and AWGN channels such that k_a=1 and k_w=2. 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 (k_a and k_w) 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 */