面向软件定义多模态车联网的双时间尺度RAN切片资源分配

亓伟敬; 宋清洋; 郭磊

doi:10.11959/j.issn.1000-436x.2022067

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面向软件定义多模态车联网的双时间尺度RAN切片资源分配

专题：多模态网络环境关键技术 | 更新时间：2024-06-05

- 面向软件定义多模态车联网的双时间尺度RAN切片资源分配
- Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV
- 通信学报 2022年43卷第4期页码：60-70
- 作者机构：
  
  重庆邮电大学通信与信息工程学院智能通信与网络安全研究院，重庆 400065
- 作者简介：
  
  [ "亓伟敬（1991- ），女，山东济南人，博士，重庆邮电大学讲师，主要研究方向为车联网、边缘计算、资源分配等" ]
  [ "宋清洋（1976- ），女，河北唐山人，博士，重庆邮电大学教授、博士生导师，主要研究方向为协作资源管理、无线携能通信、边缘计算、移动缓存等" ]
  [ "郭磊（1980- ），男，四川眉山人，博士，重庆邮电大学教授、博士生导师，主要研究方向为光通信网络、无线通信网络" ]
- 基金信息：
  
  国家自然科学基金资助项目(62025105);重庆市自然科学基金资助项目(cstc2020jcyj-msxmX0918)
- DOI：10.11959/j.issn.1000-436x.2022067
  中图分类号： TN92
- 网络出版日期：2022-04，
  
  纸质出版日期：2022-04-25
- 稿件说明：
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亓伟敬, 宋清洋, 郭磊. 面向软件定义多模态车联网的双时间尺度RAN切片资源分配[J]. 通信学报, 2022,43(4):60-70.

Weijing QI, Qingyang SONG, Lei GUO. Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV[J]. Journal on communications, 2022, 43(4): 60-70.
亓伟敬, 宋清洋, 郭磊. 面向软件定义多模态车联网的双时间尺度RAN切片资源分配[J]. 通信学报, 2022,43(4):60-70. DOI： 10.11959/j.issn.1000-436x.2022067.

Weijing QI, Qingyang SONG, Lei GUO. Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV[J]. Journal on communications, 2022, 43(4): 60-70. DOI： 10.11959/j.issn.1000-436x.2022067.

摘要

为了有效满足不同车载应用的差异化服务质量需求，针对软件定义多模态车联网提出了一种双时间尺度的无线接入网切片资源分配算法。考虑增强型移动宽带切片用户最小速率约束、车到车链路可靠性约束、节点最大功率约束、RB 约束等，以最小化超可靠低时延切片用户的平均时延为目标，建立缓存、频谱、功率联合资源分配模型。基于匈牙利算法、线性整数规划方法和DDQN算法，将原NP-hard问题在双时间尺度内求解。仿真结果表明，所提算法在保证不同切片用户服务质量需求和提高频谱利用率方面优于传统算法。

Abstract

To effectively meet the differentiated quality of service (QoS) requirements of various vehicular applications

a dual time scale resource allocation algorithm for radio access network (RAN) slicing in software-defined polymorphic Internet of vehicles (IoV) was proposed.Considering the constraints of the minimum rate requirement of enhanced mobile broadband (eMBB) slice users

vehicle-to-vehicle (V2V) link reliability

the maximum power of nodes

the maximum number of RBs

a joint optimization problem of caching

spectrum

power allocation was formulated

with the aim of minimizing the average delay of ultra-reliable and low-latency communication (URLLC) slice users.By using the Hungarian algorithm

linear integer programming method and the double deep Q-Learning network (DDQN) algorithm

the original NP-hard problem was solved in dual time scales.The simulation results show that the proposed algorithm is superior to the traditional algorithm in ensuring the QoS requirements of different slice users and improving the spectrum utilization.

关键词

Keywords

references

张彦 , 张科 , 曹佳钰 . 边缘智能驱动的车联网 [J ] . 物联网学报 , 2018 , 2 ( 4 ): 40 - 48 .

ZHANG Y , ZHANG K , CAO J Y . Internet of vehicles empowered by edge intelligence [J ] . Chinese Journal on Internet of Things , 2018 , 2 ( 4 ): 40 - 48 .

胡宇翔 , 伊鹏 , 孙鹏浩 , 等 . 全维可定义的多模态智慧网络体系研究 [J ] . 通信学报 , 2019 , 40 ( 8 ): 1 - 12 .

HU Y X , YI P , SUN P H , et al . Research on the full-dimensional defined polymorphic smart network [J ] . Journal on Communications , 2019 , 40 ( 8 ): 1 - 12 .

李军飞 , 胡宇翔 , 伊鹏 , 等 . 面向2035的多模态智慧网络技术发展路线图 [J ] . 中国工程科学 , 2020 , 22 ( 3 ): 141 - 147 .

LI J F , HU Y X , YI P , et al . Development roadmap of polymorphic intelligence network technology toward 2035 [J ] . Strategic Study of CAE , 2020 , 22 ( 3 ): 141 - 147 .

BOUKERCHE A , ALJERI N . Design guidelines for topology management in software-defined vehicular networks [J ] . IEEE Network , 2021 , 35 ( 2 ): 120 - 126 .

ZHAO L , HAN G , LI Z , et al . Intelligent digital twin-based software-defined vehicular networks [J ] . IEEE Network , 2020 , 34 ( 5 ): 178 - 184 .

SARAIVA T D V , CAMPOS C A V , FONTES R D R , et al . An application-driven framework for intelligent transportation systems using 5G network slicing [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 8 ): 5247 - 5260 .

杨立 , 李大鹏 . 网络切片在5G无线接入侧的动态实现和发展趋势 [J ] . 中兴通讯技术 , 2019 , 25 ( 6 ): 8 - 18 .

YANG L , LI D P . Realization and trend of network slicing in 5G NG-RAN [J ] . ZTE Communications , 2019 , 25 ( 6 ): 8 - 18 .

CAMPOLO C , MOLINARO A , IERA A , et al . 5G network slicing for vehicle-to-everything services [J ] . IEEE Wireless Communications , 2017 , 24 ( 6 ): 38 - 45 .

WU W , CHEN N , ZHOU C H , et al . Dynamic RAN slicing for service-oriented vehicular networks via constrained learning [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 7 ): 2076 - 2089 .

YE Q , SHI W S , QU K G , et al . Joint RAN slicing and computation offloading for autonomous vehicular networks:a learning-assisted hierarchical approach [J ] . IEEE Open Journal of Vehicular Technology , 2021 , 2 : 272 - 288 .

NASSAR A , YILMAZ Y . Deep reinforcement learning for adaptive network slicing in 5G for intelligent vehicular systems and smart cities [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 1 ): 222 - 235 .

KHAN A A , ABOLHASAN M , NI W , et al . An end-to-end (E2E) network slicing framework for 5G vehicular ad-hoc networks [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 7 ): 7103 - 7112 .

XIONG K , LENG S P , HU J , et al . Smart network slicing for vehicular fog-RANs [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 4 ): 3075 - 3085 .

KHAN H , SAMARAKOON S , BENNIS M . Enhancing video streaming in vehicular networks via resource slicing [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 4 ): 3513 - 3522 .

YU K , ZHOU H B , QIAN B , et al . A reinforcement learning aided decoupled RAN slicing framework for cellular V2X [C ] // Proceedings of 2020 IEEE Global Communications Conference . Piscataway:IEEE Press , 2020 : 1 - 6 .

BOLLA R , BRUSCHI R , DAVOLI F , et al . Energy efficiency in the future Internet:a survey of existing approaches and trends in energy-aware fixed network infrastructures [J ] . IEEE Communications Surveys ＆ Tutorials , 2011 , 13 ( 2 ): 223 - 244 .

WEI L L , HU R Q , QIAN Y , et al . Energy efficiency and spectrum efficiency of multihop device-to-device communications underlaying cellular networks [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 1 ): 367 - 380 .

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