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In this paper, we study the problem of maximizing the sum throughput of K users in a hybrid heterogeneous visible light communication (VLC)/radio frequency (RF) wireless communication system. This is done by optimizing the transmission time intervals of a time division multiple access (TDMA) scheme allocated to the users in two different downlink (DL) scenarios. In both scenarios, using the harvested energy, each user transmits its signal in an optimal allocated time interval over the uplink (UL) channel. In the first scenario, a lightwave powered communication network (LPCN)-based VLC system is used to radiate only optical energy to be harvested by the users over DL channel. Specifically, UL sum-rate maximization problem is formulated by optimizing UL time allocations. In the second scenario, a time switching-based simultaneous lightwave information and power transfer (TS-based SLIPT) is considered where the light-emitting diode (LED) transmitter sends both information and power simultaneously over DL channel. Specifically, we propose a multi-objective optimization problem (MOOP) to study the trade-off between the UL and DL sum-rates. The non-convex MOOP framework is then transformed into an equivalent form, which yields a set of Pareto optimal resource allocation policies. The effectiveness of the proposed approaches is illustrated through numerical results.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS’s energy beamformers, IRS’s passive beamformers, and the receivers’ PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.