Joint Channel Resources Allocation and Beamforming in Energy Harvesting Systems

Abstract

We studies a multiple-input single-output (MISO) energy harvesting (EH) system, where the receiver has the capability to harvest energy from an energy source with a deterministic energy profile. Assuming the practical assumptions of imperfect channel state information (CSI) and limited-rate feedback channel, the minimum mean square error (MMSE) and the random vector quantized (RVQ) schemes are resorted to implement the CSI estimation and feedback, respectively. Considering the characteristic of the EH system, a power and time allocation strategy is proposed for the CSI training and feedback processes with the aim of maximizing the downlink data rate. Due to the intractability of this rate, we derive its convex upper bound, using this bound as the new objective function. Applying tools from the optimization theory, the optimal resources allocation and the RVQ beamforming vector are obtained together with the optimal subset of training antennas. The complexity analysis of the proposed method is provided. Numerical results verify the tightness of the upper bound and significant advantages of the proposed scheme over other approaches.