1. Soft Channel Estimation Ballard Blair MIT/WHOI Joint Program January 3, 2007 1/3/20081

2. Typical Scattering Function 1/3/20082 Depth = 15m Distance = 225m Preisig, SPACE02

3. Underwater Signal Paths 1/3/20083

4. Dynamic Channel 1/3/20084 Wave Height Signal Estimation Error (SER) Impulse Response

5. Turbo Equalization 1/3/20085 MAP Equalizer Interleaver MAP Decoder Deinterleaver Data

6. Channel Model Finite impulse response (FIR) channel Typical channel length, M≈50 for 12kHz carrier, 100m depth, 4kbps data rate 1/3/20086

7. Soft Channel Model (Song2004) Data Model: Cost Function: Solution: 1/3/20087

8. Soft RLS Algorithm 1/3/20088

9. Matching Pursuit 1/3/20089 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 1/3/200810

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 1/3/200811

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 1/3/200812

13. Questions? 1/3/200813