Abstract

In the rapidly evolving landscape of wireless communication, visible light communication (VLC) stands out for its potential to redefine high-speed data exchange. Recently, VLC has utilized waveforms that combine multiple bitstreams in a unified physical layer, allowing for high-speed data exchange, precise localization, and robust control simultaneously. Particularly, the demodulation tasks of beacon position modulation (BPM) and beacon phase shift keying (BePSK) are central to decoding of such waveforms and pose significant computational challenges. This paper explores the application of multi-task learning (MTL) to these demodulation processes and aims at reducing the complexityn associated with these tasks. By systematically developing and optimizing MTL architectures, this study introduces a sequence of models, culminating in a cross-stitch (CS) model that significantly enhances the performance and computational complexity over traditional single-task learning (STL) approaches for the demodulation of VLC waveforms. The CS model demonstrates substantial reductions in model complexity which showcase the potential of VLC waveforms in resource-limited and cost-effective applications, such as Internet-of-Things (IoT) devices. These are quantified as a 26% decrease in trainable parameters and a 10% reduction in FLOPs, compared to STL models. These advancements highlight the potential of MTL to improve the scalability and operational feasibility of VLC systems.