文献类型：期刊文献

英文题名：Robust Resource Allocation for Integrated Satellite-Terrestrial Communication Systems With Eavesdroppers

作者：Zhang, Haibo[5]; Dou, Shengting[5]; Xu, Yongjun[5]; Li, Xingwang[1]; Chen, Xiaoming[2]; Yang, Liang[3]; Li, Lin[4]

第一作者：Zhang, Haibo

机构：[1] Henan Polytechnic University, School of Physics and Electronic Information Engineering, Jiaozuo, 454000, China; [2] Zhejiang University, College of Information Science and Electronic Engineering, Hangzhou, 310027, China; [3] Hunan University, College of Computer Science and Electronic Engineering, Changsha, 410082, China; [4] Beijing Union University, College of Applied Science and Technology, Beijing, 100101, China; [5] School of Communications and Information Engineering, Chongging University of Posts and Telecommunications, Chongqing, 400065, China

第一机构：Henan Polytechnic University, School of Physics and Electronic Information Engineering, Jiaozuo, 454000, China

年份：2025

外文期刊名：IEEE Internet of Things Journal

收录：EI(收录号：20250817919377);Scopus(收录号：2-s2.0-85218152172)

语种：英文

外文关键词：Convex optimization

摘要：To solve the problems of low system security and poor transmission quality in integrated satellite-terrestrial communication systems due to eavesdroppers and channel uncertainties, a robust resource allocation (RA) problem is studied. Firstly, considering the constraints of users' quality of service, the maximum transmit power of the satellite and the base station, and the bounded channel uncertainties, a RA optimization problem is established by jointly optimizing the beamforming vectors, artificial noise (AN) vectors, and power allocation factors. Then, S-Procedure, successive convex approximation, and alternating optimization are adopted to convert the non-convex problem with parameter perturbation into a convex one that can be solved efficiently. Finally, a robust RA algorithm based on the alternating approach is proposed to obtain the solutions. Simulation results show that the proposed algorithm has good security and robustness, and the outage probability is reduced by 9.12% compared to the traditional non-robust algorithms without AN. ? 2014 IEEE.