不完全CSI下并行衰落信道URLLC的双时间尺度跨层设计

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Hongsen Peng;Meixia Tao
{"title":"不完全CSI下并行衰落信道URLLC的双时间尺度跨层设计","authors":"Hongsen Peng;Meixia Tao","doi":"10.1109/OJCOMS.2025.3564296","DOIUrl":null,"url":null,"abstract":"This paper investigates the cross-layer design for point-to-point ultra-reliable low latency communication (URLLC) over parallel fading sub-channels by jointly considering channel estimation and adaptive data transmission. The model includes a stochastic traffic arrival process and the transmissions are done in the finite blocklength (FBL) regime with imperfect channel state information (CSI). Specifically, we formulate a two-timescale total average power minimization problem under reliability, latency, and peak power constraints. In the large timescale, the pilot length and pilot power are optimized while in the small timescale, the data transmit power and decoding error probability are optimized according to the estimated channel coefficients and queueing information. As a starting step in our small timescale solution, we train a deep reinforcement learning (DRL) agent employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to allocate the data transmit power on each sub-channel and determine the decoding error probability to satisfy the URLLC constraints in an ideal environment with perfect instantaneous CSI. Then we utilize a water-filling framework to accommodate the trained TD3 network for the environment with imperfect CSI. Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. Simulation results are provided to reveal the impact of the reliability, latency and the number of sub-channels. Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4126-4139"},"PeriodicalIF":6.3000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976658","citationCount":"0","resultStr":"{\"title\":\"Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI\",\"authors\":\"Hongsen Peng;Meixia Tao\",\"doi\":\"10.1109/OJCOMS.2025.3564296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the cross-layer design for point-to-point ultra-reliable low latency communication (URLLC) over parallel fading sub-channels by jointly considering channel estimation and adaptive data transmission. The model includes a stochastic traffic arrival process and the transmissions are done in the finite blocklength (FBL) regime with imperfect channel state information (CSI). Specifically, we formulate a two-timescale total average power minimization problem under reliability, latency, and peak power constraints. In the large timescale, the pilot length and pilot power are optimized while in the small timescale, the data transmit power and decoding error probability are optimized according to the estimated channel coefficients and queueing information. As a starting step in our small timescale solution, we train a deep reinforcement learning (DRL) agent employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to allocate the data transmit power on each sub-channel and determine the decoding error probability to satisfy the URLLC constraints in an ideal environment with perfect instantaneous CSI. Then we utilize a water-filling framework to accommodate the trained TD3 network for the environment with imperfect CSI. Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. Simulation results are provided to reveal the impact of the reliability, latency and the number of sub-channels. Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.\",\"PeriodicalId\":33803,\"journal\":{\"name\":\"IEEE Open Journal of the Communications Society\",\"volume\":\"6 \",\"pages\":\"4126-4139\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976658\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Communications Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10976658/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10976658/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

本文综合考虑信道估计和自适应数据传输,研究了并行衰落子信道上点对点超可靠低延迟通信的跨层设计。该模型包含一个随机的流量到达过程,并且传输是在有限块长(FBL)状态下进行的,信道状态信息(CSI)是不完全的。具体而言,我们在可靠性、延迟和峰值功率约束下制定了一个双时间尺度的总平均功率最小化问题。在大时间尺度下,优化导频长度和导频功率;在小时间尺度下,根据估计的信道系数和排队信息,优化数据传输功率和译码错误概率。作为小时间尺度解决方案的第一步,我们使用双延迟深度确定性策略梯度(TD3)算法训练深度强化学习(DRL)智能体,在具有完美瞬时CSI的理想环境中分配每个子信道上的数据传输功率并确定解码错误概率以满足URLLC约束。然后,我们利用充水框架来容纳训练后的TD3网络,以适应CSI不完美的环境。在小时间尺度优化方法的基础上,采用三元搜索算法,在大时间尺度上通过蒙特卡罗评估对导频长度和导频功率进行优化。仿真结果揭示了可靠性、时延和子信道数量对系统性能的影响。此外,训练后的网络对不同的交通到达模型以及平均到达率的变化具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI
This paper investigates the cross-layer design for point-to-point ultra-reliable low latency communication (URLLC) over parallel fading sub-channels by jointly considering channel estimation and adaptive data transmission. The model includes a stochastic traffic arrival process and the transmissions are done in the finite blocklength (FBL) regime with imperfect channel state information (CSI). Specifically, we formulate a two-timescale total average power minimization problem under reliability, latency, and peak power constraints. In the large timescale, the pilot length and pilot power are optimized while in the small timescale, the data transmit power and decoding error probability are optimized according to the estimated channel coefficients and queueing information. As a starting step in our small timescale solution, we train a deep reinforcement learning (DRL) agent employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to allocate the data transmit power on each sub-channel and determine the decoding error probability to satisfy the URLLC constraints in an ideal environment with perfect instantaneous CSI. Then we utilize a water-filling framework to accommodate the trained TD3 network for the environment with imperfect CSI. Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. Simulation results are provided to reveal the impact of the reliability, latency and the number of sub-channels. Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信