MUST-SEE: MUltichannel Sample-by-sampleTurbo reSampling, Equalization and dEcoding

Thomas Riedl and Andrew SingerUniversity of Illinois at Urbana

Champaign

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

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

Underwater Acoustic Communications

• Spreading loss• Absorption• Multipath • Rapid fluctuation• Motion/Doppler• Wide bandwidth

• ~ 1

• Noise

wind noise

Effects on CommunicationsChallenge Operational limits / effects

Spreading loss Limits operational distance

Absorption loss Limits operational distance and severely limits available bandwidth

Multipath Limits operational environment and achievable data rate (capacity)

Temporal fluctuation Limits operational environment and achievable data rate

Motion-induced Doppler Dramatically limits achievable data rate, limits operational use

Noise Limits achievable data rate

Mitigation Methods In Use TodayChallenge Operational limits / effects

Spreading loss Acoustic arrays, directional transducers

Absorption loss Restrict bandwidth of operation (LF/MF)Multipath Signal processing: arrays, equalization,

orthogonal frequency signaling (FSK/OFDM)

Temporal fluctuation Adaptive equalization, phase tracking (PLL)

Motion-induced Doppler

Gross Doppler correction, or avoidance using FSK

Noise Forward 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.

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What’s Possible in Short Range?

100 – 700 kHz

Some Comparisons

Note: only data rates achievable at BER of 1E-6 are considered

Competing Short-range Technologies

Motion: time-varying temporal scaling

System Description: motion

Recursive, sample-by-sample resampling algorithm

Turbo-resampling equalization and decoding

MIMO DA-TEQ Structure

16

2x3 MIMO CIRs s

Underwater AcousticCommunications

17

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

System Model as Turbo Code

P

h[n]

wn

map

xn

cn

yn

Code 1

Code 2

Receiver Strategies

• Traditional approach (seperate det/dec)• BER optimal

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 , P product code with product trellis• with P, infeasible, regardless of channel and modulation

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

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

Performance of Linear TEQ

• Linear TEQ Trellis-based TEQ

• BPSK, block length 1024 (K=512), R = ½

• 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

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)

Overall Performance Can Be Improved

• Pre-coding creates recursive inner code, improves distance spectrum

EXIT Charts [ten Brink]

Navy Field Tests: various locations

16QAM

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