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In this paper, we study an amplify-and-forward (AF) relay network with energy harvesting (EH) source and relay nodes. Both nodes can continuously harvest energy from the environment and store it in batteries with finite capacity. Additionally, the source node is capable of transferring a portion of its energy to the relay node through a dedicated channel. The network performance depends on not only the energy arrival profiles at EH nodes but also the energy cooperation between them. We jointly design power control and transfer for maximizing the sum rate over finite time duration, subject to energy causality and battery storage constraints. By introducing auxiliary variables to confine the accumulated power expenditure, this non-convex problem is solved via a successive convex approximation (SCA) approach, and the local optimum solutions are obtained through dual decomposition. Also when channels are quasi-static and the power control values of the source (relay) node are preset to a constant, a monotonically increasing power control structure with the time is revealed for the relay (source) node with infinite battery capacity. Computer simulations are used to validate the theoretical findings and to quantify the impact of various factors such as EH intensity at nodes and relay position on the sum rate performance.

The millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems communicate at the extremely high-frequency band. In the extremely high band, the channel state information (CSI) from channel estimation will be outdated quickly, and herein, seriously degrading the system performance. In this paper, we focus on the channel prediction to obtain prior CSI in mmWave MIMO-OFDM systems. First, the mmWave MIMO-OFDM channel is categorized and represented in four domains: the array-frequency, array-time, angle-frequency, as well as angle-time. Then, for the above four domains, we investigate the effects of the channel representations on channel prediction, and analyze the mean-squared error performance as well as the computational complexity of the investigated prediction methods. We derive that the angle-time-domain prediction method achieves higher accuracy than the other three prediction techniques. In addition, we propose an enhanced angle-timedomain channel predictor by exploiting the spatial-time sparsity of the MIMO-OFDM channel to further improve the prediction accuracy. Finally, the simulation results confirm the statistical analysis and verify the superiority of the proposed predictors.INDEX TERMS Channel prediction, channel representations, millimeter wave, sparse channel, MIMO-OFDM systems.

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