Predictive Strategy for Energy Harvesting MIMO Systems With Finite Alphabet Inputs and Limited Feedback

Abstract

Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the aid of context information, by constructing a precoding codebook offline at both the transmitter and receiver, to relax the time pressure for the online strategy. Two schemes were provided to accelerate the codebook construction. One is to estimate IMI during optimization by using a reference function. The other is by starting the optimization of each precoder from an adjacent precoder. With the channel distribution information (CDI), the feedback overhead is reduced by minimizing the codebook size. By the methods above, the building speed of the precoding codebook can be up to two orders of magnitude faster than no accelerating strategies. Meanwhile, through topology analysis, we propose a higher layer predictive strategy, which determines the index of allocated energy at each slot within a frame. The higher layer strategy takes full advantage of the constructed precoding codebook and context information when estimating the future throughput. Simulation results demonstrate the effectiveness and practicality of the proposed two-layer strategy.