Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

Huang, Pei-Qiu, Wang, Yong, Wang, Kezhi and Zhang, Qingfu (2023) Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications. IEEE Transactions on Cybernetics, 53 (4). pp. 2647-2657. ISSN 2168-2267

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This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. In this system, we jointly optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability. Due to the dynamic environment, it is challenging to ensure queue stability. In addition, making real-time decisions in each short time slot also needs to be considered. To this end, we propose a method (called LETO) that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the above optimization problem. LETO first adopts Lyapunov optimization to decouple the long-term stochastic optimization problem into deterministic optimization problems in sequential time slots. As a result, it can ensure queue stability since the deterministic optimization problem in each time slot does not involve future information. After that, LETO develops an evolutionary transfer method to solve the optimization problem in each time slot. Specifically, we first define a metric to identify the optimization problems in past time slots similar to that in the current time slot, and then transfer their optimal solutions to construct a high-quality initial population in the current time slot. Since ETO effectively accelerates the search, we can make real-time decisions in each short time slot. Experimental studies verify the effectiveness of LETO by comparison with other algorithms.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant 61976225, and in part by the Royal Society under International Exchanges 2021 Cost Share under Grant IEC\NSFC\211264.
Uncontrolled Keywords: Dynamic environment, evolutionary algorithm (EA), evolutionary transfer optimization (ETO), intelligent reflecting surface (IRS), Lyapunov optimization
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 13 May 2022 10:24
Last Modified: 29 Mar 2023 14:30

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