Transmission Energy Allocation for Over-the-Air Computation with Energy HarvestingOA
Transmission Energy Allocation for Over-the-Air Computation with Energy Harvesting
Over-the-air computation(AirComp)has re-cently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs).This paper investigates an energy harvesting(EH)AirComp system,in which multiple EH-powered single-antenna WDs simultaneously send wireless signals to a single-antenna access point(AP)with conventional energy supply for WDA via AirComp.Under this setup,we minimize the average computation mean square error(MSE)over a particular time period,by jointly optimizing the transmit energy allocation at the WDs and the AirComp denoising factors at the AP over time,subject to the energy causality constraints at individual WDs.First,we consider the offline scenario by assuming that the energy state information(ESI)and channel state information(CSI)are non-causally known at the beginning of the period,in which the formulated average MSE minimization corresponds to a non-convex optimization problem.We present a high-quality con-verged solution by using the techniques of alternating optimization and convex optimization.It is shown that for each WD,if the EH rate is sufficiently high,then the channel inversion power allocation is adopted;while if the EH rate is low,then all the harvested energy should be used up for transmission with proper energy allocation over time.Next,we consider the online scenario with causal ESI and CSI,in which the MSE minimization be-comes a stochastic optimization problem.In this scenario,we present an offline-inspired online algorithm to obtain efficient online energy allocation designs by utilizing the obtained offline solutions.Finally,numerical results show that the proposed designs significantly outperform two benchmark schemes with power-halving and full-power transmission,respectively.
Siyao Zhang;Zixiang Ren;Xinmin Li;Yin Long;Jie Xu;Shuguang Cui
School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang 621010,China||Shen-zhen Future Network of Intelligence Institute(FNii-Shenzhen),The Chinese University of Hong Kong,Shenzhen,Guangdong 518172,ChinaSchool of Information Science and Technology,University of Science and Technology of China,Hefei 230027,China||FNii-Shenzhen,The Chinese University of Hong Kong,Shenzhen,Guangdong 518172,ChinaCollege of Computer Science,Chengdu University,Chengdu 610100,ChinaSchool of Computer Science and Technology,Southwest Uni-versity of Science and Technology,Mianyang 621010,ChinaSchool of Science and Engineering,FNii-Shenzhen,and Guangdong Provincial Key Laboratory of Future Networks of Intelligence,The Chinese University of Hong Kong,Shenzhen,Guangdong 518172,China
over-the-air computation(AirComp)power allocationenergy harvesting(EH)optimization
《通信与信息网络学报（英文）》 2024 (002)
126-136 / 11
The work was supported by the National Science Foundation of China under Grant 62101467,the Basic Research Project under Grant HZQB-KCZYZ-2021067 of Hetao Shenzhen-HK S&T Cooperation Zone,the National Natural Science Foundation of China under Grants U2001208,92267202,and 62293482,Shenzhen Fundamental Research Program un-der Grant JCYJ20210324133405015,the National Key Research and Devel-opment Program of China under Grant 2018YFB1800800,Shenzhen Out-standing Talents Training Fund under Grant 202002,Guangdong Research Projects under Grants 2017ZT07X152 and 2019CX01X104,Guangdong Provincial Key Laboratory of Future Networks of Intelligence under Grant 2022B1212010001,Shenzhen Key Laboratory of Big Data and Artificial In-telligence under Grant ZDSYS201707251409055,Guangdong Major Project of Basic and Applied Basic Research under Grant 2023B0303000001.
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