Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge Processing

IF 7.1 2区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Vehicular Technology Pub Date : 2025-01-27 DOI:10.1109/TVT.2025.3534148
Seongah Jeong;Jinkyu Kang;Osvaldo Simeone;Shlomo Shamai
{"title":"Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge Processing","authors":"Seongah Jeong;Jinkyu Kang;Osvaldo Simeone;Shlomo Shamai","doi":"10.1109/TVT.2025.3534148","DOIUrl":null,"url":null,"abstract":"Perceptive mobile networks (PMNs) implement sensing and communication by reusing existing cellular infrastructure. Thanks to the cooperation among distributed access points, cell-free multiple-input multiple-output (MIMO) systems support the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, in a cell-free MIMO system, distributed access points (APs) communicate over fronthaul links with a central processing unit (CPU) in the cloud. This work proposes four different types of PMN uplink solutions based on cell-free MIMO systems, in which the sensing and decoding functionalities are carried out at either the cloud or the edge. Accordingly, we develop and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation for fronthaul quantization to maximize the achievable rate under sensing and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.","PeriodicalId":13421,"journal":{"name":"IEEE Transactions on Vehicular Technology","volume":"74 6","pages":"9520-9532"},"PeriodicalIF":7.1000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Vehicular Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10854814/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Perceptive mobile networks (PMNs) implement sensing and communication by reusing existing cellular infrastructure. Thanks to the cooperation among distributed access points, cell-free multiple-input multiple-output (MIMO) systems support the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, in a cell-free MIMO system, distributed access points (APs) communicate over fronthaul links with a central processing unit (CPU) in the cloud. This work proposes four different types of PMN uplink solutions based on cell-free MIMO systems, in which the sensing and decoding functionalities are carried out at either the cloud or the edge. Accordingly, we develop and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation for fronthaul quantization to maximize the achievable rate under sensing and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
无小区 MIMO 感知移动网络:云处理与边缘处理
感知移动网络(pmn)通过重用现有的蜂窝基础设施来实现感知和通信。由于分布式接入点之间的合作,无小区多输入多输出(MIMO)系统支持多基地雷达感测的部署,同时为数据通信服务提供高频谱效率。为此,在无小区MIMO系统中,分布式接入点(ap)通过前传链路与云中的中央处理单元(CPU)进行通信。这项工作提出了基于无小区MIMO系统的四种不同类型的PMN上行解决方案,其中传感和解码功能在云或边缘进行。因此,我们开发并比较了联合基于云的解码和感知(CDCS)、基于云的混合解码和基于边缘的感知(CDES)、基于边缘的混合解码和基于云的感知(EDCS)和基于边缘的解码和感知(EDES)。在所有情况下,我们的目标是统一的前传量化设计问题公式,以最大化在传感和前传容量约束下可实现的速率。通过数值结果,通过突出基于边缘和基于云的传感和通信的相对优点,将四种实现方案作为可用前传资源的函数进行比较。该研究为实现集成传感和通信的前端约束网络的最佳功能分配提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.00
自引率
8.80%
发文量
1245
审稿时长
6.3 months
期刊介绍: The scope of the Transactions is threefold (which was approved by the IEEE Periodicals Committee in 1967) and is published on the journal website as follows: Communications: The use of mobile radio on land, sea, and air, including cellular radio, two-way radio, and one-way radio, with applications to dispatch and control vehicles, mobile radiotelephone, radio paging, and status monitoring and reporting. Related areas include spectrum usage, component radio equipment such as cavities and antennas, compute control for radio systems, digital modulation and transmission techniques, mobile radio circuit design, radio propagation for vehicular communications, effects of ignition noise and radio frequency interference, and consideration of the vehicle as part of the radio operating environment. Transportation Systems: The use of electronic technology for the control of ground transportation systems including, but not limited to, traffic aid systems; traffic control systems; automatic vehicle identification, location, and monitoring systems; automated transport systems, with single and multiple vehicle control; and moving walkways or people-movers. Vehicular Electronics: The use of electronic or electrical components and systems for control, propulsion, or auxiliary functions, including but not limited to, electronic controls for engineer, drive train, convenience, safety, and other vehicle systems; sensors, actuators, and microprocessors for onboard use; electronic fuel control systems; vehicle electrical components and systems collision avoidance systems; electromagnetic compatibility in the vehicle environment; and electric vehicles and controls.
期刊最新文献
Channel Covariance CSI-based Indoor Localization: Machine Leaning versus Neighbourhood Geometric Approaches Integrated Cost-optimal Convex Optimization for Eco-Driving of Connected Hybrid Electric Vehicles on Sloped and Curved Roads Age of Information in Time-Slotted Wireless Status Updating System Under Generic Updating Interval: Statistical Characteristics and Optimal Updating Interval Energy Efficiency Optimization for RIS-Assisted Task Offloading in Vehicular MEC Networks Safety-Enhanced Trajectory Planning for Autonomous Vehicles: Optimization Based on Dynamic Safety Corridors
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1