Create Presentation
Download Presentation

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

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

237 Views

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

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -

**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@binghamton.edu

**2. **Contents Introduction
Basic idea of Probes and CIVA
Practical Algorithms
Probes design
CIVA
Simulations
Conclusion

**3. **Analogy From DNA Array Probes: all possible DNA segments
Probes are put on an array (chip)
DNA sample binds to a unique probe

**4. **Basic Idea of CIVA: Testing Vector Communication System Model
Testing vectors

**5. **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

**6. **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,

**7. **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?

**8. **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.

**9. **Trellis Search With Probes Metric calculation
Trellis optimization

**10. **Trellis Search with Probes Metric updating along trellis
An example:

**11. **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

**12. **How to Determine Optimal N? In noiseless case,
A large magnitude change in
Optimal value can be determined.

**13. **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.

**14. **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

**15. **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

**16. **Simulations: Experiment 1 Channel
Symbol matrix, probe
Testing vectors

**17. **Simulations: Experiment 2 Random Channel
Index Ratio
Determine N independent of channel

**18. **Simulations: Experiment 2 Comparison
CIVA
MLSE
VA w/ training
MMSE training
Blind:VA+blind channel. est.
500 samples
CIVA: 3 dB from MLSE

**19. **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.

**20. **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