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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. Contents. Introduction Basic idea of Probes and CIVA Practical Algorithms Probes design CIVA Simulations
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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
Contents • Introduction • Basic idea of Probes and CIVA • Practical Algorithms • Probes design • CIVA • Simulations • Conclusion
Analogy From DNA Array • Probes: all possible DNA segments • Probes are put on an array (chip) • DNA sample binds to a unique probe
H = + x h s v n n n Basic Idea of CIVA: Testing Vector • Communication System Model • Testing vectors
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
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,
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?
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.
Trellis Search With Probes • Metric calculation • Trellis optimization
Trellis Search with Probes • Metric updating along trellis • An example:
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
How to Determine Optimal N? • In noiseless case, • A large magnitude change in • Optimal value can be determined.
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.
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
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
Simulations: Experiment 1 • Channel • Symbol matrix, probe • Testing vectors
Simulations: Experiment 2 • Random Channel • Index Ratio • Determine N independent of channel
Simulations: Experiment 2 • Comparison • CIVA • MLSE • VA w/ training • MMSE training • Blind:VA+blind channel. est. • 500 samples • CIVA: 3 dB from MLSE
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.
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