digital: moved header files from lib to include in gr-digital.

1 files changed, 116 insertions, 0 deletions

diff --git a/gr-digital/include/digital_lms_dd_equalizer_cc.h b/gr-digital/include/digital_lms_dd_equalizer_cc.h
new file mode 100644
index 000000000..e3ad4bf4a
--- /dev/null
+++ b/gr-digital/include/digital_lms_dd_equalizer_cc.h
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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