2. MUST-SEE: MUltichannel Sample-by-sample Turbo reSampling, Equalization and dEcoding Thomas Riedl and Andrew Singer University of Illinois at Urbana Champaign

3. Overview Thank you for the invitation! Need for high-rate acoustic communications Biggest challenges and current approaches A solution to one big remaining challenge: – Sample-by-sample Doppler compensation – Turbo Equalization (joint EQ and DEC) Some performance results

4. Growing Demand for High-BW ACOMMS Deep ocean oil and gas AUV surveys waste time collecting bad data, cannot send snapshots ROVs depend on self mounted camera, need more eyes subsea Cluttered, dangerous operational environ, tethers and cables abound Mine Counter Measures (MCM) and other missions require long- range high b/w communications Tethered ROVs are expensive, cumbersome, and require massive tethered infrastructure

6. Underwater Acoustic Communications wind noise

7. Effects on Communications ChallengeOperational limits / effects Spreading lossLimits operational distance Absorption lossLimits operational distance and severely limits available bandwidth MultipathLimits operational environment and achievable data rate (capacity) Temporal fluctuationLimits operational environment and achievable data rate Motion-induced DopplerDramatically limits achievable data rate, limits operational use NoiseLimits achievable data rate

8. Mitigation Methods In Use Today ChallengeOperational limits / effects Spreading lossAcoustic arrays, directional transducers Absorption lossRestrict bandwidth of operation (LF/MF) MultipathSignal processing: arrays, equalization, orthogonal frequency signaling (FSK/OFDM) Temporal fluctuationAdaptive equalization, phase tracking (PLL) Motion-induced Doppler Gross Doppler correction, or avoidance using FSK NoiseForward error correction These are handled poorly today, leading industry to believe that (1) long range performance is limited to very low data rates (2) short-range, high bandwidth acoustic communications are impossible and (3) that mobile platforms are restricted to extremely low data rates. ********

9. What’s Possible in Short Range? 100 – 700 kHz

10. Some Comparisons Note: only data rates achievable at BER of 1E-6 are considered

11. Competing Short-range Technologies

12. Motion: time-varying temporal scaling

13. System Description: motion

14. Recursive, sample-by-sample resampling algorithm

15. Turbo-resampling equalization and decoding

16. MIMO DA-TEQ Structure 16

17. 2x3 MIMO CIRs s Underwater Acoustic Communications 17

18. What is Turbo Equalization? …and why use it for ACOMMS? Coded data transmission over ISI channels (can) form a turbo code Code 1 Code 2

19. System Model as Turbo Code  h[n] wnwn map xnxn cncn ynyn Code 1 Code 2

20. Receiver Strategies Traditional approach (seperate det/dec) BER optimal

21. Practical? Traditional (separate eq/dec) – Channel memory low, MAP eq and MAP/ML dec – Channel memory high, LMMSE eq and MAP/ML dec – Channel unknown, Directly adapted LMS/RLS/MMSE eq and MAP/ML dec Adaptive channel estimate-based eq and MAP/ML dec BER Optimal – Channel memory and | S | low without  product code with product trellis with , infeasible, regardless of channel and modulation

22. In-between, lies turbo equalization Douillard, et al. recognized the turbo code structure Glavieux, et al. proposed a practical approach for handling complexity Others rapidly identified “turbo” processing Wang and Poor developed turbo SIC for MUD

23. Linear Turbo Equalization While others had considered using linear filters, such as: Koetter’s knowledge of graphical models, provided a key insight – Hence linear filter-based TEQ using extrinsic

24. Performance of Linear TEQ Linear TEQTrellis-based TEQ BPSK, block length 1024 (K=512), R = ½

25. Soft-information varies with time, even if the channel does not! Per symbol complexity: – Trellis-based TEQ has exponential complexity – Naïve LMMSE-based TEQ has cubic complexity – Structured time dependence enables quadratic

26. Quadratic still too high for long ISI Underwater acoustic channels have 100s of taps! Approximate methods of linear complexity – Average the time-varying extrinsic information Provides a time-invariant equalization filter Achieves linear per symbol complexity Works well in practice (Glavieux, Laot, Labat)

27. Overall Performance Can Be Improved Pre-coding creates recursive inner code, improves distance spectrum

28. EXIT Charts [ten Brink]

29. Navy Field Tests: various locations 16QAM

32. Some Conclusions Much ACOMMS research has not benefitted from the last ~2 decades of comm theory Joint design of modulation, coding, resampling, equalization, and decoding Industry misconception that < 100bps are achievable acoustically Resampling methods not only improve comms, but also yield position information