Federated Learning: Sum Power Constraints Optimization Design

et al. 2023

Journal of Sensors

Self Cite

Traditional wireless data aggregation (WDA) technology based on the principle of separated communication and computation is difficult to achieve large-scale access under the limited spectrum resources, especially in scenarios with strict constraints on time latency. As an outstanding fast WDA technology, over-the-air computation (AirComp) can reduce transmit time while improving spectrum efficiency. Most edge devices in wireless networks are battery-powered. Therefore, optimizing the transmit power of devices could prolong the life cycle of nodes and save the system power consumption. In this research, we aim to minimize the device transmit power subject to aggregation error constraint. Additionally, to improve the harsh wireless transmission environment, we use reconfigurable intelligent surface (RIS) to assist AirComp. To solve the presented nonconvex problem, we present a two-step solution method. Specifically, we introduce matrix lifting technology to transform the original problems into semidefinite programming problems (SDP) in the first step and then propose an alternate difference-of-convex (DC) framework to solve the SDP subproblems. The numerical results show that RIS-assisted communication can greatly save system power and reduce aggregation error. And the proposed alternate DC method is superior to the alternate semidefinite relaxation (SDR) method.

Section: Alternate DCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconfigurable Intelligent Surface‐Aided AirComp for Large‐Scale IoT Networks

et al. 2023

Journal of Sensors

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“…IRS technology has attracted much attention in recent years [5][6][7]. IRS is a metasurface with a large number of reconfigurable passive components.…”

Section: Introductionmentioning

confidence: 99%

Power Optimization of IRS-Assisted D2D System Based on Imperfect Channel

et al. 2022

Journal of Sensors

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Intelligent reflecting surface (IRS) is a promising technology that can help wireless communications achieve efficient spectrum and energy efficiency. However, because of its weak signal processing ability, it is difficult to get ideal channel state information (CSI). Under the imperfect channel state information hypothesis, we investigate a device-to-device (D2D) offload network. And an IRS is used to help calculate offloading from one set of task-intensive users to another set of idle users. We aim to jointly optimize transmit beamforming and IRS phase shifts to minimize system transmit power while requiring each user’s rate to meet the minimum rate constraint in the presence of channel errors. Unfortunately, the problem presented is nonconvex, and the imperfection of CSI makes it even more difficult to solve. Therefore, we apply the S-Procedure to convert the original problem to two effectively solvable semidefinite programming (SDP) subproblems and then solve them through the convex-concave procedure (CCP) algorithm and the alternate optimization method. Numerical results show the effectiveness of the algorithm and verify that the assistance of the IRS can greatly save the system transmit power.

“…In order to overcome the unfavorable wireless channel environment of Air-Comp, the authors of [18] proposed to deploy a RIS in the AirComp system to improve the power of the received signal and thus reduce the aggregation error. The authors of [19] innovatively proposed sum power constraints in the RIS-aided AirComp to reduce system energy consumption. To minimize the aggregation error, the authors of [20] investigated the advantages of RIS-assisted AirComp in a large-scale cloud wireless access network.…”

Section: Introductionmentioning

confidence: 99%

“…For a given M t , solve (19) by Algorithm 1 to get Θ t+1 ; 6: t = t + 1 ; 7: until P t sum − P t−1 sum < ǫ ; 8: Then P * sum = P t sum .…”

mentioning

confidence: 99%

Reconfigurable Intelligent Surface-Aided Over-the-Air Computation for IoT Networks

et al. 2022

Preprint

Self Cite

Traditional wireless data aggregation (WDA) technology based on the principle of separated communication and computation is difficult to achieve large-scale access under the limited spectrum resources, especially in scenarios with strict constraints on time latency (e.g. autonomous driving). To solve this problem, Over-the-Air Computation (AirComp) has emerged as a new fast WDA solution. AirComp can perform ultra-high-speed wireless data aggregation in the scenario of limited communication capacity. In this paper, to overcome the disadvantage of wireless channel propagation, we use reconfigurable intelligent surface (RIS) to assist AirComp. As far as we know, most of the research on AirComp is focused on optimizing aggregation errors. Most edge devices of the Internet of Things (IoT) are battery-powered. Therefore, optimizing the transmit power of devices could prolong the life cycle of nodes and save the system power consumption. In this paper, we aim for minimizing the system transmit power subject to maximum tolerable aggregation error constraint, while ensuring that each device rate meets the minimum rate constraint. Unfortunately, the problem presented is a very tricky non-convex problem. To solve the proposed thorny problem, we propose a two-step optimization method. Specifically, we introduce matrix lifting technology to transform the original problems into semidefinite programming problems (SDP) in the first step and then propose an alternate difference-of-convex (DC) framework to solve SDP subproblems. The numerical results show that RIS-assisted communication can greatly save system power and reduce aggregation error. And the proposed alternate DC method is superior to the alternate SDR method.