Power control and antenna selection for themulti-antenna transmitter with energy harvesting

被引:0
作者
Ning X. [1 ,2 ]
Lei W. [1 ,2 ]
机构
[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
[2] Chongqing Key Lab. of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing
来源
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2022年 / 49卷 / 03期
关键词
Antenna selection; Communication technology; Energy harvesting; Lyapunov framework; Power control;
D O I
10.19665/j.issn1001-2400.2022.03.010
中图分类号
学科分类号
摘要
Aiming at the rate maximization in the MISO system with energy harvesting, we propose an online joint power control and antenna selection strategy based on the Lyapunov optimization framework.The source node is a multi-antenna node powered by an energy harvesting device.The power consumption of the radio frequency (RF) circuit for each antenna is not negligible.The more antennas are activated, the larger the gain of the antenna array, but the larger the power consumption of the RF circuit, so it is necessary to balance between the gain and the power consumption.The source node should choose the appropriate transmit antenna and transmit power according to the available energy and the channel state.Since the energy arrival and the channel fading are stochastic processes, the rate maximization problem is a stochastic optimization problem.The energy virtual queue is constructed from the power level of the rechargeable battery, the negative value of the rate is modeled as the penalty term, and then the optimization problem of power control and antenna selection is transformed into the minimization of the instantaneous queue drift-plus-penalty function by using the Lyapunov optimization framework.During each time slot, the corresponding transmit power is optimized for each active antenna number that the power stored in the battery can support, and then the number of active antennas and the transmission power with the minimum drift-plus-penalty are selected as the optimal solution.Simulation results show that compared with the three comparison algorithms, the proposed algorithm can achieve a significantly higher transmission rate.The proposed algorithm only makes decisions based on the current channel state and battery power state, and has a very low computational complexity. © 2022, Editorial Department of Journal of Xidian University. All right reserved.
引用
收藏
页码:83 / 92
页数:9
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