Channel Independent Viterbi Algorithm CIVA for Blind Sequence Detection with Near MLSE Performance

1
Channel Independent Viterbi Algorithm (CIVA) for
Blind Sequence Detection with Near MLSE
Performance

• Xiaohua(Edward) Li
• State Univ. of New York at Binghamton
• xli_at_binghamton.edu

2
Contents

• Introduction
• Basic idea of Probes and CIVA
• Practical Algorithms
• Probes design
• CIVA
• Simulations
• Conclusion

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Analogy From DNA Array

• Probes all possible DNA segments
• Probes are put on an array (chip)
• DNA sample binds to a unique probe

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Basic Idea of CIVA Testing Vector

• Communication System Model
• Testing vectors

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Basic Idea of CIVA Noiseless Symbol Detection

• Find a testing vector for each possible
  symbol matrix
• Testing vector set
• Determine testing vector sequence
• Detect symbols from

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Construct Probe as Testing Vector Group

• Requirement on testing vectors not always
  satisfied
• Probe of three cases
• right null subspace different from
• right null subspace in that of
• and have the same right null
  subspace,

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Blind Sequence Detection by Probes

• If are different in the right null
  subspace, then the corresponding probes are
  different
• Blind symbol detections
• Do the probes sharing cases matter?

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Sequence Identifiability

• Assumption 1 sequences begin or terminate with
  the same symbol matrix.
• Assumption 2
• Proposition 1 Sequences
  can be determined uniquely from each other.
• Proposition 2 In noiseless case, symbols can be
  determined uniquely from data sequence and
  probes.
• If SNR is sufficiently high, then symbols can be
  determined uniquely with probability approaching
  one.
• Assumptions 1 and 2 can be relaxed in practice.

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Trellis Search With Probes

• Metric calculation
• Trellis optimization

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Trellis Search with Probes

• Metric updating along trellis
• An example

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Channel length Over-estimation in Noise

• For known channel length, Probe trellis dim
  parameters
• Use over-estimated channel length and
  
• for probe and trellis design
• Consider data matrix
• Choose proper

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How to Determine Optimal N?

• In noiseless case,
• A large magnitude change in
• Optimal value can be determined.

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Practical Algorithm I

• Probe Design Algorithm
• Many symbol matrices have more than one dim right
  null subspace optimize testing vectors
• Select/combine testing vectors based on the
  trellis diagram simplify probes design
• Further simplification each probe contains at
  most three testing vectors.
• It is off-line! Probes are independent of
  channels.

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Practical Algorithm II

• CIVA Algorithm
• Probes design with over-estimated channel length
• Form data matrix, determine the optimal
• Trellis updating
• Symbol determination
• Properties
• No channel and correlation estimation
• Fast, finite sample, global convergence
• Symbol detection within samples
• Tolerate faster time-variation index

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Computational Complexity

• High computation complexity trellis states
• May be practical for some wireless system
• Complexity reduction desirable and possible
• Parallel hardware implementation
• Apply the complexity reduction techniques of VA
• Integrated with channel decoder promising
  complexity reduction, may even lower than MLSE.
• Fast algorithms combining the repeated/redundant
  computations

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Simulations Experiment 1

• Channel
• Symbol matrix, probe
• Testing vectors

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Simulations Experiment 2

• Random Channel
• Index Ratio
• Determine N independent of channel

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Simulations Experiment 2

• Comparison
• CIVA
• MLSE
• VA w/ training
• MMSE training
• BlindVAblind channel. est.
• 500 samples
• CIVA 3 dB from MLSE

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Simulations Experiment 3

• GSM like packets
• 3-tap random ch.
• 150 DQPSK samples/running
• CIVA blind
• VA MMSE 30 training samples
• CIVA practically outperforms training methods.

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Conclusions

• CIVA blind sequence detector using probes
• Properties
• Near ML optimal performance
• May practically outperform even training methods
• Fast global convergence
• Near future complexity reductions
• Combining channel decoders
• Fast algorithm utilizing repeated structures