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UAV-RIS Assisted Multiuser Communications Through Transmission Strategy Optimization: GBD Application

delete2024-06-01
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PRE
AI
A
Aamer Mohamed Huroon
Y
Yu-Chih Huang
王
王立春 (Li‐Chun Wang) *
DOI:10.1109/TVT.2024.3362066delete
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摘要

摘要

En 中文
In this article, we present a transmission strategy optimization technique for multiple unmanned aerial vehicles (UAVs) base stations assisted by a terrestrial reconfigurable intelligent surface (RIS). While each UAV is designed to serve a particular group of users, whether a UAV is assisted by RIS depends on the distance between the UAV and the terrestrial RIS. As such, a joint optimization problem of the allocation of RIS clusters for each associated UAV and the computation of phase rotations corresponding to terrestrial RIS elements is formulated. Based on the generalized Benders decomposition (GBD), we develop an algorithm to solve the problem efficiently for this mixed integer non-linear programming problem (MINLP) in two cases: 1) treating interference as noise (TIN) and 2) applying successive interference cancellation (SIC) at user terminals. Through extensive simulations, we provide evidence of our algorithm's superiority over existing methods, including alternating optimization and multi-task learning. By checking the upper and lower bounds provided by the proposed algorithm, we show that our algorithm attains the global optimality for the considered simulations. Notably, our results also reaffirm the common belief that SIC decoding can provide performance enhancement over TIN decoding in the UAV-RIS-assisted systems.
Keyword:
Autonomous aerial vehicles
Optimization
NOMA
Decoding
Base stations
Resource management
Interference cancellation
Aerial-terrestrial communications
generalized Benders decomposition
mixed-integer nonlinear programming
reconfigurable intelligent surfaces
transmission strategy

期刊

IEEE Transactions on Vehicular Technology 封面图
IEEE Transactions on Vehicular Technology
IF:
7.1
论文数:
1.8W
被引数:
6.6W

机构

N
National Yang Ming Chiao Tung University
学者数:
2.5W
论文数: 2.3W
被引数: 2.2W
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