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Leveraging Advanced Sonar Array Processing Techniques for Underwater Acoustic Multi-Input Multi-Output Communications

Leveraging Advanced Sonar Array Processing Techniques for Underwater Acoustic Multi-Input Multi-Output Communications . Brian Stein The University of Texas at Austin. Problem Statement.

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Leveraging Advanced Sonar Array Processing Techniques for Underwater Acoustic Multi-Input Multi-Output Communications

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  1. Leveraging Advanced Sonar Array Processing Techniques for Underwater Acoustic Multi-Input Multi-Output Communications Brian Stein The University of Texas at Austin

  2. Problem Statement • High rate underwater acoustic (UWA) communication difficult to achieve - low propagation speed (1500m/s) - constrained bandwidth (~1MHz) • Solution: spatial diversity - increased capacity - increased spectral efficiency - robust to fading/interference • Leverage current deploymentof sonar arrays for comms

  3. h(1,1) TX 1 RX 1 h(2,1) h(1,2) TX 2 RX 2 h(2,2) Multiple-Input Multiple-Output (MIMO) • Source message transmitted simultaneously over multiple radiators • Receiver utilizes multiple receive elements, each with an independent look at the signal

  4. Receiver Model

  5. Simulation • Channel Model • Wide Sense Stationary with Uncorrelated Scatterers (WSSUS) • Channel fixed in both absolute time and absolute frequency • Stochastic process accounts for dispersiveness in time and Doppler • Dispersive fading model • Channel model for discrete multipath arrivals, each with independent delay, gain and phase distortion • Delays modeled using exponential distribution • Gain/phase modeled using Rayleigh distribution • Rayleigh fading • Statistical propagation model thatmodels the fluctuation of a signal overtime

  6. Adaptive Filtering • Least Mean Squares • Stochastic gradient descent method • Adaptation depends only on current error • Tradeoff: • Low complexity implementation • Slow convergence rate

  7. Simulation Results • 2x2 MIMO System • Multichannel Combiner • Signal 1 SINR: 12.7 dB • Signal 2 SINR: 13.8 dB • MIMO-DFE • Signal 1 SINR: 9.3 dB • Signal 2 SINR: 9.4 dB • Doppler “smearing” • Rotation of symbolconstellation • Future Improvements: • Doppler resampler • Phase Locked Loop Simulation Results for a 2x2 MIMO system with 5 elements/array. (a),(b) depict the multichannel combiner results for signal 1 and signal 2 respectively. (c),(d) show the same signals processed without array processing

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