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

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.