SLIVER: A SLIPT-enabled IRS-assisted VLC System for Energy Optimization

This paper deals with a simultaneous lightwave power transfer (SLIPT) indoor visible light communication system, supported by intelligent reflective surfaces (IRS). The proposed system, namely SLIVER, aims to reduce the energy consumption, by means of an optimization problem formulated to minimize the average transmit power from luminaries under data rate and energy harvesting constraints at users, and exploiting mirror-based IRS. The complexity in this problem is due to the large combinations from IRS element rotations. To this end, the problem is transformed into an IRS assignment and spot-finding problem. To solve this, a blockwise efficient artificial neural network is proposed. The proposed architecture can predict the user positions and receiver orientations and accordingly find the optimal beamforming matrix, average transmit powers at luminaries, and IRS assignments. Results reveal that SLIVER system is helpful to achieve better performance under a wide range of user mobility, receiver orientations, and blockage conditions. Specifically, up to 60% of the transmit power can be reduced with the use of the IRS, as compared to traditional approaches (i.e., no IRS) and without complex algorithms.