Over-the-air computation(AirComp)based federated learning(FL)has been a promising technique for distilling artificial intelligence(AI)at the network edge.However,the performance of AirComp-based FL is decided by the d...Over-the-air computation(AirComp)based federated learning(FL)has been a promising technique for distilling artificial intelligence(AI)at the network edge.However,the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property.More importantly,most existing work focuses on a single-cell scenario,where inter-cell interference is ignored.To overcome these shortages,a reconfigurable intelligent surface(RIS)-assisted AirComp-based FL system is proposed for multi-cell networks,where a RIS is used for enhancing the poor user signal caused by channel fading,especially for the device at the cell edge,and reducing inter-cell interference.The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived.To minimize the optimality gap,we formulate a joint uplink and downlink optimization problem.The formulated problem is then divided into two separable nonconvex subproblems.Following the successive convex approximation(SCA)method,we first approximate the nonconvex term to a linear form,and then alternately optimize the beamforming vector and phase-shift matrix for each cell.Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.展开更多
Over-the-air computation(AirComp)has recently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs). This paper investigates an energy harvestin...Over-the-air computation(AirComp)has recently 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 converged 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 becomes 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.展开更多
文摘Over-the-air computation(AirComp)based federated learning(FL)has been a promising technique for distilling artificial intelligence(AI)at the network edge.However,the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property.More importantly,most existing work focuses on a single-cell scenario,where inter-cell interference is ignored.To overcome these shortages,a reconfigurable intelligent surface(RIS)-assisted AirComp-based FL system is proposed for multi-cell networks,where a RIS is used for enhancing the poor user signal caused by channel fading,especially for the device at the cell edge,and reducing inter-cell interference.The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived.To minimize the optimality gap,we formulate a joint uplink and downlink optimization problem.The formulated problem is then divided into two separable nonconvex subproblems.Following the successive convex approximation(SCA)method,we first approximate the nonconvex term to a linear form,and then alternately optimize the beamforming vector and phase-shift matrix for each cell.Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.
基金supported by the National Science Foundation of China under Grant 62101467the Basic Research Project under Grant HZQBKCZYZ-2021067 of Hetao Shenzhen-HK S&T Cooperation Zone,the National Natural Science Foundation of China under Grants U2001208,92267202,and 62293482+6 种基金Shenzhen Fundamental Research Program under Grant JCYJ20210324133405015the National Key Research and Development Program of China under Grant 2018YFB1800800Shenzhen Outstanding Talents Training Fund under Grant 202002Guangdong Research Projects under Grants 2017ZT07X152 and 2019CX01X104Guangdong Provincial Key Laboratory of Future Networks of Intelligence under Grant 2022B1212010001Shenzhen Key Laboratory of Big Data and Artificial Intelligence under Grant ZDSYS201707251409055Guangdong Major Project of Basic and Applied Basic Research under Grant 2023B0303000001.
文摘Over-the-air computation(AirComp)has recently 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 converged 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 becomes 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.
