Resource Allocation for UAV-Assisted NOMA Systems With Dual Connectivity

被引：6

作者：

Zhang, Ruizhi ^{[1
]}

Tang, Rui ^{[2
]}

Xu, Yongjun ^{[3
,4
]}

Shen, Xiaofeng ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

[2] China West Normal Univ, Sch Elect Informat Engn, Nanchong, Peoples R China

[3] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing, Peoples R China

[4] Chongqing Jinmei Commun Co Ltd, Dept Wireless Commun, Chongqing 400030, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 02期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Autonomous aerial vehicles; Resource management; NOMA; Bit rate; Uplink; Transforms; Trajectory; UAV; dual connectivity; resource allocation; fractional programming; deep reinforcement learning;

D O I：

10.1109/LWC.2022.3226265

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this letter, we consider a non-orthogonal multiple access (NOMA)-based uplink cellular system assisted by an unmanned aerial vehicle (UAV) through dual connectivity. To balance efficiency and fairness, we aim to maximize the sum weighted bit rate by jointly optimizing the three-dimensional UAV placement and power allocation. Considering the non-convexity of the original problem, we propose a hybrid offline-online algorithm: In the offline training, when the UAV placement is given, the property of fractional programming is utilized to obtain a sub-optimal power allocation solution; Based on the results of power allocation, the deep deterministic policy gradient algorithm is leveraged to learn the optimal UAV trajectory policy that can be deployed in an online fashion. Finally, simulations are conducted to show the efficiency of the proposed algorithm.

引用

页码：341 / 345

页数：5

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