We propose a deep learning based channel estimation technique for wireless energy transfer. Specifically, we develop a channel learning scheme using the deep autoencoder, which learns the channel state information (CSI) at the energy transmitter based on the harvested energy feedback from the energy receiver, in the sense of minimizing the mean square error (MSE) of the channel estimation. Numerical results demonstrate that the proposed scheme learns the CSI very well and significantly outperforms the conventional scheme in terms of the channel estimation MSE as well as the harvested energy.

Nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) are two key enablers for 5G systems. In this article, considering the practical issue that successive interference cancellation (SIC) decoding is imperfect in the real-world NOMA system, we propose a novel scheme for the downlink of the MIMO-NOMA system based on deep learning. In this scheme, both precoding and SIC decoding of the MIMO-NOMA system are jointly optimized (or learned) in the sense of minimizing total mean square error of the users’ signals. To this end, we construct the precoder and SIC decoders using deep neural networks such that the transmitted signals intended to multiple users can be properly precoded at the transmitter based on the superposition coding technique and the received signals are accurately decodable at the users by the SIC decoding. Numerical results demonstrate the effectiveness and superior performance of the proposed scheme.

In this letter, we propose a joint pilot design and channel estimation scheme based on the deep learning (DL) technique for multiuser multiple-input multiple output (MIMO) channels. To this end, we construct a pilot designer using two-layer neural networks (TNNs) and a channel estimator using deep neural networks (DNNs), which are jointly trained to minimize the mean square error (MSE) of channel estimation. To effectively reduce the interference among the multiple users, we also use the successive interference cancellation (SIC) technique in the channel estimation process. The numerical results demonstrate that the proposed scheme considerably outperforms the linear minimum mean square error (LMMSE) based channel estimation scheme.

This article studies a joint resource allocation for an unmanned aerial vehicle (UAV) enabled simultaneous wireless information and power transfer (SWIPT) system in a multicasting scenario, where a UAV is dispatched to simultaneously send common information and wireless power to multiple users on the ground while the users decode information and harvest energy based on the power splitting (PS) receiver architecture. In this system, we aim to maximize the minimum achievable rate among all users under their harvested energy constraints by optimizing trajectory and transmit power of the UAV jointly with PS ratios of the users. To solve this challenging nonconvex problem, we first convert it into a more tractable form by exploiting the quadratic transform technique. Then, we propose an efficient iterative algorithm for the joint optimization. Numerical results show that the proposed scheme outperforms baseline schemes.

This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R² = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score ($F{q}^{var}$) (10 ≤ Strong, 5 ≤ Ambiguous < 10, and Weak < 5), according to their influence. As a result, the models considering the input variables of the Strong + Ambiguous group (R² = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R² = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p < 0.05 or 0.01). There were no statistically significant results between the Strong group and the Strong + Ambiguous group (R²: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group ($F{q}^{var}$: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption.

In this paper, we propose a new adaptive rate and energy harvesting interval control scheme to maximize the throughout subject to the average energy constraint in the multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system. We consider the realistic scenario of time-varying fading channel. In order to maximize the throughput and simultaneously to maintain the average energy required at the receiver, we first formulate a problem of jointly optimizing the rate and energy harvesting interval based on Markov decision process (MDP) by using a regularization parameter. However, this MDP problem is difficult to directly solve because the channel transition probabilities (i.e., the model or the environment) are challenging to estimate in the practical systems. Thus, we propose an adaptive rate and energy harvesting interval control algorithm based on the model-free reinforcement learning technique. Numerical results demonstrate that the proposed scheme significantly outperforms the conventional scheme.