LAUSR

We investigate power optimization for throughput maximization in massive multiple-input multiple-output (MIMO) downlink networks with hybrid energy harvesting transmitter. In particular, a finite-capacity energy harvester is employed at the base station in conjunction with the fixed power grid for system energy supply. With availability of the noncausal knowledge about the channel state information (CSI) and the energy harvesting process, the throughput maximization is first formulated as a typical convex optimization problem, subject to a set of battery storage and power constraints. Especifically, this optimization is also featured by taking into account the circuit energy consumption that can not be neglected in massive MIMO systems with a large quality of radio frequency chains. Mathematically, Lagrange dual decomposition technique can be used to derive the optimal power variables. However, due to its offline calculation characteristics, the proposed approach is hard to implement in practice and can only serve as a performance upper bound. To facilitate real world application, we then heuristically present an online scheme that merely utilizes the causal information of the CSI and the statistics of the energy harvesting process, without significantly degrading the performance. Finally, simulation results are shown to verify the effectiveness of the proposed algorithms.