期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 16, 页码 12983-12998出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3064150
关键词
Energy harvesting (EH); multiuser (MU) scenario; neural networks (NNs); power control; single-user (SU) scenario; solar-powered communications
资金
- Ministry of Science and Technology of Taiwan [MOST 108-2221-E-008-018, MOST 109-2221-E-008-056]
In this article, neural network-based transmit power control prediction schemes are designed for solar-powered energy harvesting communications, achieving satisfactory performance in both single-user and multi-user scenarios.
In this article, we design neural network (NN)-based transmit power control prediction for solar-powered energy harvesting (EH) communications under single-user (SU) and multiuser (MU) scenarios with real solar data. Although the directional water filling (DWF) is known as the optimal scheme for the SU case, it necessitates the full (past and future) channel state information (CSI) and energy state information (ESI) in realizing the optimal solution over a time period. To conquer this impracticality, an SU-GreenPCNet, which only requires the past short-term CSI and ESI for predicting the SU transmit power, is proposed and trained with the historical solar data. For the MU case, two iterative algorithms, namely, weighted-sum minimum mean-square error (WMMSE) and iterative DWF (IDWF), are investigated to tackle the original nonconvex power control problem when the full state knowledge is assumed to be known in advance. The solutions with the historical solar data are then served as benchmarks for designing two MU-GreenPCNets. As an extension of the SU-GreenPCNet, a centralized scheme is proposed at the central controller for jointly determining the MU transmit power values based on the past short-term state knowledge of all users. A distributed scheme is further investigated to reduce the signaling overhead, in which each transmitter merely utilizes the past short-term CSI, ESI, and MU interference (MUI) knowledge associated with its user pair. The simulation results show that the proposed power control prediction schemes can pragmatically achieve satisfied sum rate performance in both SU and MU scenarios, as compared with the benchmark schemes.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据