Most studies of reflecting intelligent surfaces (RISs)-assisted visible light communication (VLC) systems have focused on the integration of RISs in the channel to combat the line-of-sight (LoS) blockage and to enhance the corresponding achievable data rate. Some recent efforts have investigated the integration of liquid crystal (LC)-RIS in the VLC receiver to also improve the corresponding achievable data rate. To jointly benefit from the previously mentioned appealing capabilities of the RIS technology in both the channel and the receiver, in this work, we propose a novel indoor VLC system that is jointly assisted by a mirror array-based RIS in the channel and an LC-based RIS aided-VLC receiver. To illustrate the performance of the proposed system, a rate maximization problem is formulated, solved, and evaluated. This maximization problem jointly optimizes the roll and yaw angles of the mirror array-based RIS as well as the refractive index of the LC-based RIS VLC receiver. Moreover, this maximization problem considers practical assumptions, such as the presence of non-users blockers in the LoS path between the transmitter-receiver pair and the user's random device orientation (i.e., the user's self-blockage). Due to the non-convexity of the formulated optimization problem, a low-complexity algorithm is utilized to get the global optimal solution. A multi-user scenario of the proposed scheme is also presented. Furthermore, the energy efficiency of the proposed system is also investigated. Simulation results are provided, confirming that the proposed system yields a noteworthy improvement in data rate and energy efficiency performances compared to several baseline schemes.

翻译：大多数关于可重构智能表面辅助可见光通信系统的研究都集中在将RIS集成到信道中以应对视距链路阻塞并提高相应的可达数据速率。近期一些研究探索了在VLC接收机中集成液晶RIS以进一步提升可达数据速率。为了同时利用RIS技术在信道和接收机中的上述优势，本文提出了一种新型室内VLC系统，该系统联合使用信道中的镜面阵列型RIS和基于液晶的RIS辅助VLC接收机。为展示所提系统的性能，建立了一个速率最大化问题，并对其求解与评估。该最大化问题联合优化了基于镜面阵列的RIS的横滚角和偏航角以及基于液晶的RIS VLC接收机的折射率。此外，该最大化问题考虑了实际假设，例如发射机-接收机对视距路径中存在非用户阻塞物以及用户随机设备方向（即用户自阻塞）。由于所构造的优化问题具有非凸性，采用低复杂度算法获取全局最优解。本文还介绍了所提方案的多用户场景，并进一步研究了系统的能量效率。仿真结果表明，与几种基准方案相比，所提系统在数据速率和能量效率性能上均实现了显著改善。