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Soft Channel Estimation

Soft Channel Estimation. Ballard Blair MIT/WHOI Joint Program January 3, 2007. Typical Scattering Function. Depth = 15m Distance = 225m. Preisig, SPACE02. Underwater Signal Paths. Dynamic Channel. Wave Height. Signal Estimation Error (SER). Impulse Response. Turbo Equalization. Data.

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Soft Channel Estimation

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  1. Soft Channel Estimation Ballard Blair MIT/WHOI Joint Program January 3, 2007

  2. Typical Scattering Function Depth = 15m Distance = 225m Preisig, SPACE02

  3. Underwater Signal Paths

  4. Dynamic Channel Wave Height Signal Estimation Error (SER) Impulse Response

  5. Turbo Equalization Data MAP Equalizer MAP Decoder Interleaver Soft Channel Model Deinterleaver

  6. Channel Model • Finite impulse response (FIR) channel Typical channel length, M≈50 for 12kHz carrier, 100m depth, 4kbps data rate

  7. Soft Channel Model (Song2004) • Data Model: • Cost Function: • Solution:

  8. Soft RLS Algorithm

  9. Matching Pursuit Other variations: Orthogonal MP (OMP), Order Recursive Least Squares MP, etc.

  10. Problems • RLS does not take advantage of sparsity • Matching pursuit not adapted for soft data • Errors in matching pursuit “dictionary” still an open problem

  11. Ideas • Use two step approach • Identify energetic taps (maybe matching pursuit) • Use RLS technique with only those taps • Use weighted matching pursuit • Directly adapt matching pursuit to handle soft data

  12. More ideas? • Compressed sensing? • Does not use all of our knowledge about channel, but still might be good? • Something else? • Need technique that uses soft data and sparse channel model

  13. Questions?

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