DDPG-based Sum Rate Optimization for Opportunistic Backscatter NOMA Networks

被引：0

作者：

Zeeshan, Hafiz Muhammad Ali ^{[1
]}

Ullah, Syed Asad ^{[1
]}

Hassan, Syed Ali ^{[1
]}

Ding, Zhiguo ^{[2
]}

Jung, Haejoon ^{[3
]}

机构：

[1] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci SEECS, Islamabad 44000, Pakistan

[2] Univ Manchester, Dept Elect & Elect Engn, Manchester, Lancs, England

[3] Kyung Hee Univ KHU, Dept Elect Engn, Yongin 17104, South Korea

来源：

IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM | 2023年

基金：

新加坡国家研究基金会;

关键词：

Quality of service (QoS); cognitive radio inspired non-orthogonal multiple access (CR-NOMA); deep deterministic policy gradient (DDPG);

D O I：

10.1109/GLOBECOM54140.2023.10437095

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In today's world of burgeoning IoT and 6G communications, supporting low-powered devices is crucial to fully capitalizing on the Next Generation Internet of Things (NGIoT) revolution. The proliferation of these devices will unlock unprecedented opportunities for energy efficiency, sustainability, and ubiquitous connectivity. This paper investigates the sum rate optimization of a quality-of-service (QoS)-aware EH-enabled passive IoT device in a cognitive radio inspired non-orthogonal multiple access (CR-NOMA)-assisted backscatter communication network. Our goal is to optimize the sum rate of a secondary passive IoT device while guaranteeing the QoS requirements of the scheduled primary device. The deep deterministic policy gradient (DDPG) algorithm is employed to dynamically adjust the reflection coefficient of the backscatter node, yielding optimal performance. Our results demonstrate significant improvements in the sum rate, highlighting the importance of incorporating advanced machine learning (ML) techniques into IoT and wireless communication domains to address critical challenges and enhance the overall performance of NG-IoT networks.

引用

页码：3312 / 3317

页数：6

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