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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.