Optimizing Age of Information in RIS-Assisted NOMA Networks: A Deep Reinforcement Learning Approach

被引：14

作者：

Feng, Xue ^{[1
]}

Fu, Shu ^{[1
]}

Fang, Fang ^{[2
,3
]}

Yu, Fei Richard ^{[4
]}

机构：

[1] Chongqing Univ, Dept Microelect & Commun Engn, Chongqing 400044, Peoples R China

[2] Western Univ, Dept Elect & Comp Engn, London, ON N6A 3K7, Canada

[3] Western Univ, Dept Comp Sci, London, ON N6A 3K7, Canada

[4] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2022年 / 11卷 / 10期

基金：

中国国家自然科学基金;

关键词：

NOMA; Optimization; Internet of Things; Interference; Signal to noise ratio; Information age; Wireless sensor networks; Reconfigurable intelligent surface; age of information; deep reinforcement learning; non-orthogonal multiple access; RECONFIGURABLE INTELLIGENT SURFACES;

D O I：

10.1109/LWC.2022.3193958

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Due to the rapid development of the Internet of Things (IoT), data freshness has become particularly important. In this letter, we study a reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) network for collecting packets of IoT devices. Specifically, we establish a novel age of information (AoI) model to evaluate the freshness of packets. To minimize the average peak AoI, we formulate an optimization problem of jointly optimizing the phase-shift matrix of RIS and service time of packets. Then, we adopt deep deterministic policy gradient (DDPG) to solve the non-convex problem, which can handle a mass of continuous high-dimensional variables. Extensive simulation results demonstrate the superiority of the proposed scheme compared to the conventional schemes.

引用

页码：2100 / 2104

页数：5

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