{"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}
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.
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.