APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 1 Doppler Estimation and Correction for Shallow Underwater Acoustic Communications Kenneth A. Perrine*, Karl F. Nieman*, Terry Henderson*, Keith Lent*, Terry J. Brudner*, and Brian L. Evans † *Applied Research Laboratories: The University of Texas at Austin † Dept. of Electrical & Computer Eng., University of Texas at Austin Asilomar Conference on Signals, Systems, and Computers Nov. 9, 2010

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 2 Users Access Point UUVs Seafloor Datalink Buoys Divers Underwater Acoustic Network

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 3 Underwater Acoustic Channel Propagation speed 200,000x slower vs. RF in air Lowpass (bandwidth decreases with range) Wideband communication relative to carrier Shallow water case Time-varying Doppler Channel reverberation High energy Long time constant Measured shallow water channel impulse responses Range is 30m for position 1 and 1260m for position 3.

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 4 Underwater Acoustic Channel Doppler effects for received QPSK signal Results from linear bulk Doppler correction Decision regions

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 5 Proposed Contributions Shallow underwater acoustic communications One-element transmitter (stationary and moving cases) Quadrature phase shift keying (QPSK) Carrier frequency 62.5 kHz and kHz bandwidth Transmit kbps at distances of 30 to 1285 m One-element receiver (anchored on floating platform) 1.Evaluate SNR performance of three Doppler estimation methods 2.Evaluate static and adaptive equalizers

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 6 Bulk Doppler Estimation Approach 1: Self-referenced correlation Transmit two copies of training sequence Use phase in cross-correlation of received symbols Rep. 1Rep. 2 Payload… Calculate phase offset in decoded symbols Symbols P. Moose, “A technique for orthogonal frequency division multiplexing frequency offset correction,” IEEE Transactions on Communications, vol. 42, no. 10, pp , Oct. 1994

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 7 Bulk Doppler Estimation Approach 2: Carrier recovery Observe peak FFT frequency of squared samples (in binary phase shift keying (BPSK) case) Compare observed frequency with expected center frequency (without Doppler)

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 8 Bulk Doppler Estimation Approach 2: Carrier recovery Variation: slice packet into “windows” Rough adaptation to time-varying Doppler effects

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 9 Bulk Doppler Estimation Approach 3: Pilot tone Encode pure tone outside of data band Average over all measured pilot frequencies to estimate deviation from transmitted frequencies 87 kHz tone +/- Doppler Data: 62.5 kHz center; kHz BW

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 10 Bulk Doppler Estimation Approach 3: Pilot tone Variation: slice packet into “windows”: 87 kHz tone +/- Doppler Data: 62.5 kHz center; kHz BW

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 11 Windowing Tradeoffs QPSK decoding 250 ms125 ms each62.5 ms each31.25 ms each

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 12 Packet Structure Linear frequency modulated (LFM) chirp Resistant to Doppler Training – 128 symbols 4 length-13 Barker sequences 76 symbols for equalizer training Symbol rate of kHz Payload – 3968 symbols Guard interval at end 100 ms for reverberation analysis Pilot tones at 45 and 87 kHz Packet Structure Packet Spectrum

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 13 Experimental Setup Applied Research Laboratories Lake Travis Test Facility Lake 37 m depth Former riverbed Nearby dam Transmitter on research vessel Receiver on barge at test station

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 14 Data Collection Points 1: 15m docked 2: m floating 3: m floating 4: m vertical motion 5: m towing at ~3 kts

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 15 Static Equalizer Σ Feedforward taps Feedback taps x[m]x[m]y[m]y[m] Decision 5 feedforward taps 3 feedback taps

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 16 Fully Adaptive Equalizer Σ Feedforward taps Feedback taps x[m]x[m]y[m]y[m] Decision 5 feedforward taps 3 feedback taps 0.01 learning rate Update – Update: O(N) per symbol (N = total # of taps)

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 17 Issues with Windowing Support for Doppler estimation accuracy decreased Smaller samples are subject to more noise Discontinuities (even when smoothed) can lead the adaptive decision feedback equalizer (DFE) astray Windowing mostly benefits static equalizer Successful operation Problematic

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 18 Experimental Results Average estimated SNR for bulk Doppler detection/correction and equalization Carrier recovery (BCDE) provides highest SNR. Adaptive equalizer has best increase in SNR overall A: Self-referenced correlation B, C, D, E: Carrier recovery (1, 2, 4, 8 windows) F, G, H, I: Pilot tone (1, 2, 4, 8 windows) Bulk Doppler Detection Method

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 19 Experimental Results Self-referenced correlation (A) performs poorly Represents tiny packet sample Pilot tone tracking (FGHI) performs poorly in motion case (Pos. 2) Carrier recovery with any number of windows (BCDE) performs best

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 20 Conclusions Windowing for Doppler detection benefits static equalization Pilot tone method was not reliable Best configuration over entire dataset Single window carrier recovery method Adaptive equalization Σ Update –

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 21 Underwater Acoustic Comm. Dataset Experimental Setup 1-element transmitter BPSK, QPSK, 4-QAM, 16-QAM and 256-QAM Symbol rates of 3.9 and 15.6 kHz With and without pilot tones Ranges 10m to 1285 m 5-element receiver array in L shape Raw data in MATLAB format underwater/datasets/index.html

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 22

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 23 Publications and Presentations Conferece Proceedings K. F. Nieman, K. A. Perrine, T. L. Henderson, K. H. Lent, T. J. Brudner and B. L. Evans, “Wideband Monopulse Spatial Filtering for Large Array Receivers for Reverberant Underwater Communication Channels”, Proc. IEEE OCEANS, Sep , 2010 Seattle, WA K. F. Nieman, K. A. Perrine, K. H. Lent, T. L. Henderson, T. J. Brudner and B. L. Evans, “Multi-stage And Sparse Equalizer Design For Communication Systems In Reverberant Underwater Channels”, Proc. IEEE Int. Workshop on Signal Processing Systems, Oct. 6-8, 2010, Cupertino, CA K. A. Perrine, K. F. Nieman, T. L. Henderson, K. H. Lent, T. J. Brudner and B. L. Evans, “Doppler Estimation and Correction for Shallow Underwater Acoustic Communications”, Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 7-10, 2010, Pacific Grove, CA Released Dataset “The University of Texas at Austin Applied Research Laboratories Nov Five-Element Acoustic Underwater Dataset”, Version 1.0, element samples of BPSK, QPSK, 16QAM, 64QAM, and 256QAM signals Up to 1300 yard range, up to 63 kbit/sec data rate

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 24 Data Collection Transmitter Omnidirectional transducer Submerged between 1-8m Receiver 4.6m depth Five directional hydrophones Half-power beamwidths Horizontal: ~45° Vertical: ~10° Sampling rate: 500 kHz Transmitting Transducer Sensitivity at 1m

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 25 Software Receiver Frame synchronizer Identify LFM chirps via cross-correlation Bulk Doppler detection Bulk Doppler correction Linear interpolation of oversampled basebanded signal Decision feedback equalizer (DFE) Static Decision-directed adaptive w/ learning rate of 0.01

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 26 July Raytracing Severe thermocline: Receiver R can’t directly see transmitters A or B Surface Lakebed

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 27 Channel Impulse Response Fig. 4. Channel impulse responses (CIR) for near and far ranges. Position 1 range is 30 m and Position 3 range is ~1260 m.

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 28 Experimental Results A: Self-referenced correlation B, C, D, E: Carrier recovery (1, 2, 4, 8 windows) F, G, H, I: Pilot tone (1, 2, 4, 8 windows) Pos. 1: 15m, docked Pos. 2: m, free floating

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 29 Experimental Results A: Self-referenced correlation B, C, D, E: Carrier recovery (1, 2, 4, 8 windows) F, G, H, I: Pilot tone (1, 2, 4, 8 windows) Pos. 4: m, vertical motion Pos. 5: m, towing at ~3kts

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 30 Experimental Results A BER of ~0.5 indicates catastrophic failure in decoding. 4 or 8 windows significantly helps the static EQ; However, adaptive EQ yields better results overall.

APPLIED RESEARCH LABORATORIES THE UNIVERSITY OF TEXAS AT AUSTIN 31 Experimental Results Pilot tone approach was not be reliable Multipath interference caused selective fading Pilot tone was too narrow in bandwidth