A machine learning approach to array-based free-space optical communications

James Miller; Paul Keeley; Peter Ateshian; Jerome Nilmeier; Nur Dwijayanto; Gurminder Singh

doi:10.1117/12.2579049

5 March 2021 A machine learning approach to array-based free-space optical communications

James Miller, Paul Keeley, Peter Ateshian, Jerome Nilmeier, Nur Dwijayanto, Gurminder Singh

Proceedings Volume 11703, AI and Optical Data Sciences II; 117031F (2021) https://doi.org/10.1117/12.2579049
Event: SPIE OPTO, 2021, Online Only

Abstract

This report on research in progress demonstrates a machine learning (ML) approach to array-based free-space optical communication using mobile devices. Spatial codes are transmitted using arrays of lasers or light emitting diodes for increased resilience and throughput, and ML models are trained on the channel alphabet to provide efficient decoding at the receiver. Various ML models, transmission array configurations, and spatial codes are compared for performance, and a proof-of-concept system is demonstrated. ML decoding of spatial symbols under noisy/perturbed channel conditions was successfully accomplished, however significant challenges are identified with throughput on mobile devices. Future experimentation is outlined to incorporate testing over greater distances under more realistic conditions.

Conference Presentation

Citation Download Citation

James Miller, Paul Keeley, Peter Ateshian, Jerome Nilmeier, Nur Dwijayanto, and Gurminder Singh "A machine learning approach to array-based free-space optical communications", Proc. SPIE 11703, AI and Optical Data Sciences II, 117031F (5 March 2021); https://doi.org/10.1117/12.2579049

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