LoRa网络中基于深度强化学习的信息年龄优化  

Optimizing Age of Information in LoRa Networks via Deep Reinforcement Learning

作　　者：程克非 陈彩蝶 罗佳 陈前斌[2] CHENG Kefei;CHEN Caidie;LUO Jia;CHEN Qianbin(School of Cyber Security and Information Law,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Key Laboratory of Mobile Communication Technology,School of Communication and Information Engineer-ing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学网络空间安全与信息法学院,重庆400065 [2]重庆邮电大学通信与信息工程学院移动通信技术重点实验室,重庆400065

出　　处：《电子与信息学报》2025年第2期541-550,共10页Journal of Electronics & Information Technology

基　　金：重庆市教委科学技术研究项目(KJQN202400643)。

摘　　要：信息年龄(AoI)是信息新鲜度的衡量指标,针对时间敏感的物联网,最小化AoI显得尤为重要。该文基于LoRa网络的智能交通环境,分析Slot-Aloha协议下的AoI优化策略,建立了Slot-Aloha协议下数据包之间传输碰撞和等待时间的系统模型。通过分析指出,在LoRa上行传输过程中,随着数据包数量增多,AoI主要受到数据包碰撞影响。为克服优化问题中动作空间过大导致难以实现有效求解的问题,该文采用连续动作空间映射离散动作空间的方式,使用柔性动作-评价(SAC)算法对LoRa网络下的AoI进行优化。仿真结果显示,SAC算法优于传统算法与传统深度强化学习算法,可有效降低网络的平均AoI。Age of Information(AoI)quantifies information freshness,which is critical for time-sensitive Internet of Things(IoT)applications.This paper investigates AoI optimization in an LoRa network under the Slot-Aloha protocol in an intelligent transportation environment.A system model is established to characterize transmission collisions and packet waiting times.Analytical results indicate that in LoRa uplink transmission,as the number of packets increases,AoI is primarily influenced by packet collisions.To address the challenge of a large action space hindering effective solutions,this study maps the continuous action space to a discrete action space and employs the Soft Actor-Critic(SAC)algorithm for AoI optimization.Simulation results demonstrate that the SAC algorithm outperforms conventional algorithms and traditional deep reinforcement learning approaches,effectively reducing the network’s average AoI.Objective With the rapid development of intelligent transportation systems,ensuring the real-time availability and accuracy of traffic data has become essential,particularly in transmission systems for traffic monitoring cameras and related equipment.Long-range,low-power radio frequency(LoRa)networks have emerged as a key technology for sensor connectivity in intelligent transportation due to their advantages of low power consumption,wide coverage,and long-distance communication.However,in urban environments,LoRa networks are prone to frequent data collisions when multiple devices transmit simultaneously,which affects information timeliness and,consequently,the effectiveness of traffic management decisions.This study focuses on optimizing data packet timeliness in LoRa networks to enhance communication efficiency.Specifically,it aims to improve AoI under the Slotted Aloha protocol by analyzing the effects of packet collisions and over-the-air transmission time.Based on this analysis,an optimization method using deep reinforcement learning is proposed,employing the SAC algorithm to minimize AoI.The goal is to achie

关 键 词：信息年龄 LoRa 柔性动作-评价算法 深度强化学习 优化策略 

分 类 号：TN929.5[电子电信—通信与信息系统]