942 lines
28 KiB
C++
942 lines
28 KiB
C++
/*
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* Copyright 2013, 2014 Range Networks, Inc.
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Affero General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU Affero General Public License for more details.
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*
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* You should have received a copy of the GNU Affero General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*
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* This use of this software may be subject to additional restrictions.
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* See the LEGAL file in the main directory for details.
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*/
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#ifndef _AMRCODER_H_
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#define _AMRCODER_H_
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#include <stdint.h>
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#include "BitVector.h"
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#include "Viterbi.h"
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/**
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Class to represent recursive systematic convolutional coders/decoders of rate 1/2, memory length 4.
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*/
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class ViterbiTCH_AFS12_2 : public ViterbiBase {
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private:
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/**name Lots of precomputed elements so the compiler can optimize like hell. */
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//@{
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/**@name Core values. */
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//@{
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static const unsigned mIRate = 2; ///< reciprocal of rate
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static const unsigned mOrder = 4; ///< memory length of generators
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//@}
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/**@name Derived values. */
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//@{
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static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
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static const uint32_t mSMask = mIStates-1; ///< survivor mask
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static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
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static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
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static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
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static const unsigned mDeferral = 6*mOrder; ///< deferral to be used
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//@}
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//@}
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/** Precomputed tables. */
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//@{
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uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
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uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
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uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
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uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
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//@}
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public:
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/**
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A candidate sequence in a Viterbi decoder.
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The 32-bit state register can support a deferral of 6 with a 4th-order coder.
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*/
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typedef struct candStruct {
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uint32_t iState; ///< encoder input associated with this candidate
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uint32_t oState; ///< encoder output associated with this candidate
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char rState[mIRate];///< real states of encoders associated with this candidate
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float cost; ///< cost (metric value), float to support soft inputs
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} vCand;
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/** Clear a structure. */
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void vitClear(vCand& v)
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{
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v.iState=0;
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v.oState=0;
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v.cost=0;
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for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
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}
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private:
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/**@name Survivors and candidates. */
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//@{
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vCand mSurvivors[mIStates]; ///< current survivor pool
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vCand mCandidates[2*mIStates]; ///< current candidate pool
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//@}
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public:
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unsigned iRate() const { return mIRate; }
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uint32_t cMask() const { return mCMask; }
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uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
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unsigned deferral() const { return mDeferral; }
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ViterbiTCH_AFS12_2();
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/** Set all cost metrics to zero. */
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void initializeStates();
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/**
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Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
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@return reference to minimum-cost candidate.
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*/
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const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
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private:
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/** Branch survivors into new candidates. */
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void branchCandidates();
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/** Compute cost metrics for soft-inputs. */
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void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
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/** Select survivors from the candidate set. */
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void pruneCandidates();
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/** Find the minimum cost survivor. */
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const vCand& minCost() const;
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/**
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Precompute the state tables.
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@param g Generator index 0..((1/rate)-1)
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*/
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void computeStateTables(unsigned g);
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/**
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Precompute the generator outputs.
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mCoeffs must be defined first.
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*/
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void computeGeneratorTable();
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void encode(const BitVector &in, BitVector& target) const;
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void decode(const SoftVector &in, BitVector& target);
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};
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/**
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Class to represent recursive systematic convolutional coders/decoders of rate 1/3, memory length 4.
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*/
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class ViterbiTCH_AFS10_2 : public ViterbiBase {
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private:
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/**name Lots of precomputed elements so the compiler can optimize like hell. */
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//@{
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/**@name Core values. */
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//@{
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static const unsigned mIRate = 3; ///< reciprocal of rate
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static const unsigned mOrder = 4; ///< memory length of generators
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//@}
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/**@name Derived values. */
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//@{
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static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
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static const uint32_t mSMask = mIStates-1; ///< survivor mask
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static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
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static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
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static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
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static const unsigned mDeferral = 6*mOrder; ///< deferral to be used
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//@}
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//@}
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/** Precomputed tables. */
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//@{
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uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
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uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
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uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
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uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
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//@}
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public:
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/**
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A candidate sequence in a Viterbi decoder.
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The 32-bit state register can support a deferral of 6 with a 4th-order coder.
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*/
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typedef struct candStruct {
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uint32_t iState; ///< encoder input associated with this candidate
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uint32_t oState; ///< encoder output associated with this candidate
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char rState[mIRate];///< real states of encoders associated with this candidate
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float cost; ///< cost (metric value), float to support soft inputs
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} vCand;
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/** Clear a structure. */
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void vitClear(vCand& v)
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{
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v.iState=0;
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v.oState=0;
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v.cost=0;
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for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
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}
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private:
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/**@name Survivors and candidates. */
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//@{
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vCand mSurvivors[mIStates]; ///< current survivor pool
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vCand mCandidates[2*mIStates]; ///< current candidate pool
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//@}
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public:
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unsigned iRate() const { return mIRate; }
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uint32_t cMask() const { return mCMask; }
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uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
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unsigned deferral() const { return mDeferral; }
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ViterbiTCH_AFS10_2();
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/** Set all cost metrics to zero. */
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void initializeStates();
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/**
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Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
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@return reference to minimum-cost candidate.
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*/
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const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
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private:
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/** Branch survivors into new candidates. */
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void branchCandidates();
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/** Compute cost metrics for soft-inputs. */
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void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
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/** Select survivors from the candidate set. */
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void pruneCandidates();
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/** Find the minimum cost survivor. */
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const vCand& minCost() const;
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/**
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Precompute the state tables.
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@param g Generator index 0..((1/rate)-1)
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*/
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void computeStateTables(unsigned g);
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/**
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Precompute the generator outputs.
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mCoeffs must be defined first.
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*/
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void computeGeneratorTable();
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void encode(const BitVector &in, BitVector& target) const;
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void decode(const SoftVector &in, BitVector& target);
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};
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/**
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Class to represent recursive systematic convolutional coders/decoders of rate 1/3, memory length 6.
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*/
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class ViterbiTCH_AFS7_95 : public ViterbiBase {
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private:
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/**name Lots of precomputed elements so the compiler can optimize like hell. */
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//@{
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/**@name Core values. */
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//@{
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static const unsigned mIRate = 3; ///< reciprocal of rate
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static const unsigned mOrder = 6; ///< memory length of generators
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//@}
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/**@name Derived values. */
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//@{
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static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
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static const uint32_t mSMask = mIStates-1; ///< survivor mask
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static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
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static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
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static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
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static const unsigned mDeferral = 5*mOrder; ///< deferral to be used
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//@}
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//@}
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/** Precomputed tables. */
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//@{
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uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
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uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
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uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
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uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
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//@}
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public:
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/**
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A candidate sequence in a Viterbi decoder.
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The 32-bit state register can support a deferral of 5*order with a 6th-order coder.
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*/
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typedef struct candStruct {
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uint32_t iState; ///< encoder input associated with this candidate
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uint32_t oState; ///< encoder output associated with this candidate
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char rState[mIRate];///< real states of encoders associated with this candidate
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float cost; ///< cost (metric value), float to support soft inputs
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} vCand;
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/** Clear a structure. */
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void vitClear(vCand& v)
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{
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v.iState=0;
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v.oState=0;
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v.cost=0;
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for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
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}
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private:
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/**@name Survivors and candidates. */
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//@{
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vCand mSurvivors[mIStates]; ///< current survivor pool
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vCand mCandidates[2*mIStates]; ///< current candidate pool
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//@}
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public:
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unsigned iRate() const { return mIRate; }
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uint32_t cMask() const { return mCMask; }
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uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
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unsigned deferral() const { return mDeferral; }
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ViterbiTCH_AFS7_95();
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/** Set all cost metrics to zero. */
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void initializeStates();
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/**
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Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
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@return reference to minimum-cost candidate.
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*/
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const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
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private:
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/** Branch survivors into new candidates. */
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void branchCandidates();
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/** Compute cost metrics for soft-inputs. */
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void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
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/** Select survivors from the candidate set. */
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void pruneCandidates();
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/** Find the minimum cost survivor. */
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const vCand& minCost() const;
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/**
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Precompute the state tables.
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@param g Generator index 0..((1/rate)-1)
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*/
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void computeStateTables(unsigned g);
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/**
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Precompute the generator outputs.
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mCoeffs must be defined first.
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*/
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void computeGeneratorTable();
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void encode(const BitVector &in, BitVector& target) const;
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void decode(const SoftVector &in, BitVector& target);
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};
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/**
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Class to represent recursive systematic convolutional coders/decoders of rate 1/3, memory length 4.
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*/
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class ViterbiTCH_AFS7_4 : public ViterbiBase {
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private:
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/**name Lots of precomputed elements so the compiler can optimize like hell. */
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//@{
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/**@name Core values. */
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//@{
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static const unsigned mIRate = 3; ///< reciprocal of rate
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static const unsigned mOrder = 4; ///< memory length of generators
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//@}
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/**@name Derived values. */
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//@{
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static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
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static const uint32_t mSMask = mIStates-1; ///< survivor mask
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static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
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static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
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static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
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static const unsigned mDeferral = 6*mOrder; ///< deferral to be used
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//@}
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//@}
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/** Precomputed tables. */
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//@{
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uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
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uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
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uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
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uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
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//@}
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public:
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/**
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A candidate sequence in a Viterbi decoder.
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The 32-bit state register can support a deferral of 6 with a 4th-order coder.
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|
*/
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typedef struct candStruct {
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uint32_t iState; ///< encoder input associated with this candidate
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uint32_t oState; ///< encoder output associated with this candidate
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char rState[mIRate];///< real states of encoders associated with this candidate
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float cost; ///< cost (metric value), float to support soft inputs
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} vCand;
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/** Clear a structure. */
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void vitClear(vCand& v)
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{
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v.iState=0;
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v.oState=0;
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v.cost=0;
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for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
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}
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private:
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/**@name Survivors and candidates. */
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//@{
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vCand mSurvivors[mIStates]; ///< current survivor pool
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vCand mCandidates[2*mIStates]; ///< current candidate pool
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|
//@}
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public:
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unsigned iRate() const { return mIRate; }
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uint32_t cMask() const { return mCMask; }
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uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
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unsigned deferral() const { return mDeferral; }
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ViterbiTCH_AFS7_4();
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/** Set all cost metrics to zero. */
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void initializeStates();
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/**
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|
Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
|
|
@return reference to minimum-cost candidate.
|
|
*/
|
|
const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
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|
private:
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/** Branch survivors into new candidates. */
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|
void branchCandidates();
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/** Compute cost metrics for soft-inputs. */
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|
void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
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/** Select survivors from the candidate set. */
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void pruneCandidates();
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/** Find the minimum cost survivor. */
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const vCand& minCost() const;
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/**
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|
Precompute the state tables.
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|
@param g Generator index 0..((1/rate)-1)
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|
*/
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|
void computeStateTables(unsigned g);
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/**
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|
Precompute the generator outputs.
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|
mCoeffs must be defined first.
|
|
*/
|
|
void computeGeneratorTable();
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|
void encode(const BitVector &in, BitVector& target) const;
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|
void decode(const SoftVector &in, BitVector& target);
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};
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/**
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Class to represent recursive systematic convolutional coders/decoders of rate 1/4, memory length 4.
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|
*/
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|
class ViterbiTCH_AFS6_7 : public ViterbiBase {
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private:
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|
/**name Lots of precomputed elements so the compiler can optimize like hell. */
|
|
//@{
|
|
/**@name Core values. */
|
|
//@{
|
|
static const unsigned mIRate = 4; ///< reciprocal of rate
|
|
static const unsigned mOrder = 4; ///< memory length of generators
|
|
//@}
|
|
/**@name Derived values. */
|
|
//@{
|
|
static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
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|
static const uint32_t mSMask = mIStates-1; ///< survivor mask
|
|
static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
|
|
static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
|
|
static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
|
|
static const unsigned mDeferral = 6*mOrder; ///< deferral to be used
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|
//@}
|
|
//@}
|
|
|
|
/** Precomputed tables. */
|
|
//@{
|
|
uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
|
|
uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
|
|
uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
|
|
uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
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|
//@}
|
|
|
|
public:
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|
|
|
/**
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|
A candidate sequence in a Viterbi decoder.
|
|
The 32-bit state register can support a deferral of 6 with a 4th-order coder.
|
|
*/
|
|
typedef struct candStruct {
|
|
uint32_t iState; ///< encoder input associated with this candidate
|
|
uint32_t oState; ///< encoder output associated with this candidate
|
|
char rState[mIRate];///< real states of encoders associated with this candidate
|
|
float cost; ///< cost (metric value), float to support soft inputs
|
|
} vCand;
|
|
|
|
/** Clear a structure. */
|
|
void vitClear(vCand& v)
|
|
{
|
|
v.iState=0;
|
|
v.oState=0;
|
|
v.cost=0;
|
|
for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
|
|
}
|
|
|
|
|
|
private:
|
|
|
|
/**@name Survivors and candidates. */
|
|
//@{
|
|
vCand mSurvivors[mIStates]; ///< current survivor pool
|
|
vCand mCandidates[2*mIStates]; ///< current candidate pool
|
|
//@}
|
|
|
|
public:
|
|
|
|
unsigned iRate() const { return mIRate; }
|
|
uint32_t cMask() const { return mCMask; }
|
|
uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
|
|
unsigned deferral() const { return mDeferral; }
|
|
|
|
|
|
ViterbiTCH_AFS6_7();
|
|
|
|
/** Set all cost metrics to zero. */
|
|
void initializeStates();
|
|
|
|
/**
|
|
Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
|
|
@return reference to minimum-cost candidate.
|
|
*/
|
|
const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
private:
|
|
|
|
/** Branch survivors into new candidates. */
|
|
void branchCandidates();
|
|
|
|
/** Compute cost metrics for soft-inputs. */
|
|
void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
/** Select survivors from the candidate set. */
|
|
void pruneCandidates();
|
|
|
|
/** Find the minimum cost survivor. */
|
|
const vCand& minCost() const;
|
|
|
|
/**
|
|
Precompute the state tables.
|
|
@param g Generator index 0..((1/rate)-1)
|
|
*/
|
|
void computeStateTables(unsigned g);
|
|
|
|
/**
|
|
Precompute the generator outputs.
|
|
mCoeffs must be defined first.
|
|
*/
|
|
void computeGeneratorTable();
|
|
void encode(const BitVector &in, BitVector& target) const;
|
|
void decode(const SoftVector &in, BitVector& target);
|
|
|
|
};
|
|
|
|
|
|
|
|
/**
|
|
Class to represent recursive systematic convolutional coders/decoders of rate 1/4, memory length 6.
|
|
*/
|
|
class ViterbiTCH_AFS5_9 : public ViterbiBase {
|
|
|
|
private:
|
|
/**name Lots of precomputed elements so the compiler can optimize like hell. */
|
|
//@{
|
|
/**@name Core values. */
|
|
//@{
|
|
static const unsigned mIRate = 4; ///< reciprocal of rate
|
|
static const unsigned mOrder = 6; ///< memory length of generators
|
|
//@}
|
|
/**@name Derived values. */
|
|
//@{
|
|
static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
|
|
static const uint32_t mSMask = mIStates-1; ///< survivor mask
|
|
static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
|
|
static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
|
|
static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
|
|
static const unsigned mDeferral = 5*mOrder; ///< deferral to be used
|
|
//@}
|
|
//@}
|
|
|
|
/** Precomputed tables. */
|
|
//@{
|
|
uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
|
|
uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
|
|
uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
|
|
uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
|
|
//@}
|
|
|
|
public:
|
|
|
|
/**
|
|
A candidate sequence in a Viterbi decoder.
|
|
The 32-bit state register can support a deferral of 5*order with a 6th-order coder.
|
|
*/
|
|
typedef struct candStruct {
|
|
uint32_t iState; ///< encoder input associated with this candidate
|
|
uint32_t oState; ///< encoder output associated with this candidate
|
|
char rState[mIRate];///< real states of encoders associated with this candidate
|
|
float cost; ///< cost (metric value), float to support soft inputs
|
|
} vCand;
|
|
|
|
/** Clear a structure. */
|
|
void vitClear(vCand& v)
|
|
{
|
|
v.iState=0;
|
|
v.oState=0;
|
|
v.cost=0;
|
|
for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
|
|
}
|
|
|
|
|
|
private:
|
|
|
|
/**@name Survivors and candidates. */
|
|
//@{
|
|
vCand mSurvivors[mIStates]; ///< current survivor pool
|
|
vCand mCandidates[2*mIStates]; ///< current candidate pool
|
|
//@}
|
|
|
|
public:
|
|
|
|
unsigned iRate() const { return mIRate; }
|
|
uint32_t cMask() const { return mCMask; }
|
|
uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
|
|
unsigned deferral() const { return mDeferral; }
|
|
|
|
|
|
ViterbiTCH_AFS5_9();
|
|
|
|
/** Set all cost metrics to zero. */
|
|
void initializeStates();
|
|
|
|
/**
|
|
Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
|
|
@return reference to minimum-cost candidate.
|
|
*/
|
|
const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
private:
|
|
|
|
/** Branch survivors into new candidates. */
|
|
void branchCandidates();
|
|
|
|
/** Compute cost metrics for soft-inputs. */
|
|
void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
/** Select survivors from the candidate set. */
|
|
void pruneCandidates();
|
|
|
|
/** Find the minimum cost survivor. */
|
|
const vCand& minCost() const;
|
|
|
|
/**
|
|
Precompute the state tables.
|
|
@param g Generator index 0..((1/rate)-1)
|
|
*/
|
|
void computeStateTables(unsigned g);
|
|
|
|
/**
|
|
Precompute the generator outputs.
|
|
mCoeffs must be defined first.
|
|
*/
|
|
void computeGeneratorTable();
|
|
void encode(const BitVector &in, BitVector& target) const;
|
|
void decode(const SoftVector &in, BitVector& target);
|
|
|
|
};
|
|
|
|
|
|
|
|
/**
|
|
Class to represent recursive systematic convolutional coders/decoders of rate 1/5, memory length 4.
|
|
*/
|
|
class ViterbiTCH_AFS5_15 : public ViterbiBase {
|
|
|
|
private:
|
|
/**name Lots of precomputed elements so the compiler can optimize like hell. */
|
|
//@{
|
|
/**@name Core values. */
|
|
//@{
|
|
static const unsigned mIRate = 5; ///< reciprocal of rate
|
|
static const unsigned mOrder = 4; ///< memory length of generators
|
|
//@}
|
|
/**@name Derived values. */
|
|
//@{
|
|
static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
|
|
static const uint32_t mSMask = mIStates-1; ///< survivor mask
|
|
static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
|
|
static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
|
|
static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
|
|
static const unsigned mDeferral = 6*mOrder; ///< deferral to be used
|
|
//@}
|
|
//@}
|
|
|
|
/** Precomputed tables. */
|
|
//@{
|
|
uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
|
|
uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
|
|
uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
|
|
uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
|
|
//@}
|
|
|
|
public:
|
|
|
|
/**
|
|
A candidate sequence in a Viterbi decoder.
|
|
The 32-bit state register can support a deferral of 6 with a 4th-order coder.
|
|
*/
|
|
typedef struct candStruct {
|
|
uint32_t iState; ///< encoder input associated with this candidate
|
|
uint32_t oState; ///< encoder output associated with this candidate
|
|
char rState[mIRate];///< real states of encoders associated with this candidate
|
|
float cost; ///< cost (metric value), float to support soft inputs
|
|
} vCand;
|
|
|
|
/** Clear a structure. */
|
|
void vitClear(vCand& v)
|
|
{
|
|
v.iState=0;
|
|
v.oState=0;
|
|
v.cost=0;
|
|
for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
|
|
}
|
|
|
|
|
|
private:
|
|
|
|
/**@name Survivors and candidates. */
|
|
//@{
|
|
vCand mSurvivors[mIStates]; ///< current survivor pool
|
|
vCand mCandidates[2*mIStates]; ///< current candidate pool
|
|
//@}
|
|
|
|
public:
|
|
|
|
unsigned iRate() const { return mIRate; }
|
|
uint32_t cMask() const { return mCMask; }
|
|
uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
|
|
unsigned deferral() const { return mDeferral; }
|
|
|
|
|
|
ViterbiTCH_AFS5_15();
|
|
|
|
/** Set all cost metrics to zero. */
|
|
void initializeStates();
|
|
|
|
/**
|
|
Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
|
|
@return reference to minimum-cost candidate.
|
|
*/
|
|
const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
private:
|
|
|
|
/** Branch survivors into new candidates. */
|
|
void branchCandidates();
|
|
|
|
/** Compute cost metrics for soft-inputs. */
|
|
void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
/** Select survivors from the candidate set. */
|
|
void pruneCandidates();
|
|
|
|
/** Find the minimum cost survivor. */
|
|
const vCand& minCost() const;
|
|
|
|
/**
|
|
Precompute the state tables.
|
|
@param g Generator index 0..((1/rate)-1)
|
|
*/
|
|
void computeStateTables(unsigned g);
|
|
|
|
/**
|
|
Precompute the generator outputs.
|
|
mCoeffs must be defined first.
|
|
*/
|
|
void computeGeneratorTable();
|
|
void encode(const BitVector &in, BitVector& target) const;
|
|
void decode(const SoftVector &in, BitVector& target);
|
|
|
|
};
|
|
|
|
|
|
|
|
/**
|
|
Class to represent recursive systematic convolutional coders/decoders of rate 1/5, memory length 6.
|
|
*/
|
|
class ViterbiTCH_AFS4_75 : public ViterbiBase {
|
|
|
|
private:
|
|
/**name Lots of precomputed elements so the compiler can optimize like hell. */
|
|
//@{
|
|
/**@name Core values. */
|
|
//@{
|
|
static const unsigned mIRate = 5; ///< reciprocal of rate
|
|
static const unsigned mOrder = 6; ///< memory length of generators
|
|
//@}
|
|
/**@name Derived values. */
|
|
//@{
|
|
static const unsigned mIStates = 0x01 << mOrder; ///< number of states, number of survivors
|
|
static const uint32_t mSMask = mIStates-1; ///< survivor mask
|
|
static const uint32_t mCMask = (mSMask<<1) | 0x01; ///< candidate mask
|
|
static const uint32_t mOMask = (0x01<<mIRate)-1; ///< ouput mask, all iRate low bits set
|
|
static const unsigned mNumCands = mIStates*2; ///< number of candidates to generate during branching
|
|
static const unsigned mDeferral = 5*mOrder; ///< deferral to be used
|
|
//@}
|
|
//@}
|
|
|
|
/** Precomputed tables. */
|
|
//@{
|
|
uint32_t mCoeffs[mIRate]; ///< output polynomial for each generator
|
|
uint32_t mCoeffsFB[mIRate]; ///< feedback polynomial for each generator
|
|
uint32_t mStateTable[mIRate][2*mIStates]; ///< precomputed generator output tables
|
|
uint32_t mGeneratorTable[2*mIStates]; ///< precomputed coder output table
|
|
//@}
|
|
|
|
public:
|
|
|
|
/**
|
|
A candidate sequence in a Viterbi decoder.
|
|
The 32-bit state register can support a deferral of 5*order with a 6th-order coder.
|
|
*/
|
|
typedef struct candStruct {
|
|
uint32_t iState; ///< encoder input associated with this candidate
|
|
uint32_t oState; ///< encoder output associated with this candidate
|
|
char rState[mIRate];///< real states of encoders associated with this candidate
|
|
float cost; ///< cost (metric value), float to support soft inputs
|
|
} vCand;
|
|
|
|
/** Clear a structure. */
|
|
void vitClear(vCand& v)
|
|
{
|
|
v.iState=0;
|
|
v.oState=0;
|
|
v.cost=0;
|
|
for (unsigned i = 0; i < mIRate; i++) v.rState[i] = 0;
|
|
}
|
|
|
|
|
|
private:
|
|
|
|
/**@name Survivors and candidates. */
|
|
//@{
|
|
vCand mSurvivors[mIStates]; ///< current survivor pool
|
|
vCand mCandidates[2*mIStates]; ///< current candidate pool
|
|
//@}
|
|
|
|
public:
|
|
|
|
unsigned iRate() const { return mIRate; }
|
|
uint32_t cMask() const { return mCMask; }
|
|
uint32_t stateTable(unsigned g, unsigned i) const { return mStateTable[g][i]; }
|
|
unsigned deferral() const { return mDeferral; }
|
|
|
|
|
|
ViterbiTCH_AFS4_75();
|
|
|
|
/** Set all cost metrics to zero. */
|
|
void initializeStates();
|
|
|
|
/**
|
|
Full cycle of the Viterbi algorithm: branch, metrics, prune, select.
|
|
@return reference to minimum-cost candidate.
|
|
*/
|
|
const vCand& step(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
private:
|
|
|
|
/** Branch survivors into new candidates. */
|
|
void branchCandidates();
|
|
|
|
/** Compute cost metrics for soft-inputs. */
|
|
void getSoftCostMetrics(uint32_t inSample, const float *probs, const float *iprobs);
|
|
|
|
/** Select survivors from the candidate set. */
|
|
void pruneCandidates();
|
|
|
|
/** Find the minimum cost survivor. */
|
|
const vCand& minCost() const;
|
|
|
|
/**
|
|
Precompute the state tables.
|
|
@param g Generator index 0..((1/rate)-1)
|
|
*/
|
|
void computeStateTables(unsigned g);
|
|
|
|
/**
|
|
Precompute the generator outputs.
|
|
mCoeffs must be defined first.
|
|
*/
|
|
void computeGeneratorTable();
|
|
void encode(const BitVector &in, BitVector& target) const;
|
|
void decode(const SoftVector &in, BitVector& target);
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
#endif
|