Abstract
To improve the performance of indoor generalized space shift keying (GSSK) visible light communication (VLC) system, multiple reconfigurable intelligent surfaces (RISs) enabled user matching and light emitting diodes (LEDs) combination selection approach is proposed. Firstly, the multiple RISs enabled GSSK-VLC system model is provided, which arranges multiple RISs to realize the full coverage of the service area in an indoor space. Then, based on received power, the special user is readily matched to the corresponding RIS. Finally, machine learning enabled LEDs combination selection techniques are exploited to reduce hardware costs and improve system bit error rate (BER) performance, wherein the machine learning approaches considered include k-nearest neighbor (k-NN), support vector machine (SVM), Naive Bayes (NB), and back propagation neural network (BPNN). Among these LEDs combination selection approaches, the BPNN has the best BER performance for the highest classification accuracy. Simulation results show that the proposed joint user-RIS matching and LEDs combination selection scheme can achieve better BER performance and reduce system complexity effectively.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.