车联网中视频语义驱动的资源分配算法

陈九九; 冯春燕; 郭彩丽; 杨洋; 孙启政; 朱美逸

doi:10.11959/j.issn.1000-436x.2021080

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车联网中视频语义驱动的资源分配算法

学术论文 | 更新时间：2024-06-05

- 车联网中视频语义驱动的资源分配算法
- Video semantics-driven resource allocation algorithm in Internet of vehicles
- 通信学报 2021年42卷第7期页码：1-11
- 作者机构：
  
  北京邮电大学北京先进信息网络实验室，北京 100876
- 作者简介：
  
  [ "陈九九（1994− ），男，湖南平江人，北京邮电大学博士生，主要研究方向为车联网资源分配、语义通信、强化学习算法等" ]
  [ "冯春燕（1963− ），女，北京人，博士，北京邮电大学教授、博士生导师，主要研究方向为无线通信信息传输与处理、宽带通信网络理论与技术、社交网络分析和信息检索、电信大数据分析与挖掘等" ]
  [ "郭彩丽（1977− ），女，山西太原人，博士，北京邮电大学教授、博士生导师，主要研究方向为语义通信、无线移动通信技术、认知无线电、信号检测与估值、车联网、可见光通信，视觉智能计算，社交跨媒体数据挖掘与分析等" ]
  [ "杨洋（1991− ），男，湖南娄底人，博士，北京邮电大学讲师，主要研究方向为可见光通信、室内定位技术、车联网技术、语义编码等" ]
  [ "孙启政（1997− ），女，河南安阳人，北京邮电大学博士生，主要研究方向为语义通信、视觉内容理解、深度学习算法等" ]
  [ "朱美逸（1999− ），女，湖北襄阳人，北京邮电大学硕士生，主要研究方向为车联网通信、强化学习算法等" ]
- 基金信息：
  
  中央高校基本科研业务费专项资金资助项目(2021XD-A01-1);国家自然科学基金资助项目(92067202);北京市自然科学基金资助项目(4202049);北京邮电大学（济南）工业互联网研究院项目(201915001)
- DOI：10.11959/j.issn.1000-436x.2021080
  中图分类号： TN929.5
- 网络出版日期：2021-07，
  
  纸质出版日期：2021-07-25
- 稿件说明：
移动端阅览
陈九九, 冯春燕, 郭彩丽, 等. 车联网中视频语义驱动的资源分配算法[J]. 通信学报, 2021,42(7):1-11.

Jiujiu CHEN, Chunyan FENG, Caili GUO, et al. Video semantics-driven resource allocation algorithm in Internet of vehicles[J]. Journal on communications, 2021, 42(7): 1-11.
陈九九, 冯春燕, 郭彩丽, 等. 车联网中视频语义驱动的资源分配算法[J]. 通信学报, 2021,42(7):1-11. DOI： 10.11959/j.issn.1000-436x.2021080.

Jiujiu CHEN, Chunyan FENG, Caili GUO, et al. Video semantics-driven resource allocation algorithm in Internet of vehicles[J]. Journal on communications, 2021, 42(7): 1-11. DOI： 10.11959/j.issn.1000-436x.2021080.

摘要

针对车联网中视频语义理解等智能计算业务需求下传统资源分配方式不再适用的问题，研究了视频语义驱动的资源分配算法。首先，以目标检测任务为例，提出视频语义驱动的资源分配指导模型并给出模型参数的求解算法；其次，构建了车联网场景中视频语义驱动的资源分配优化问题，将该问题转化成凸问题并利用凸优化算法求解；进一步，为降低凸优化算法的复杂度，提出了基于强化Q学习的资源分配算法；最后，仿真验证了所提资源分配算法的性能优势。

Abstract

Aiming at the problem that traditional resource allocation methods will no longer be applicable

with the demand of intelligent computing services such as video semantic understanding in Internet of vehicles

the video semantic driven resource allocation algorithm was studied.First of all

taking the object detection task as an example

a semantic driven resource allocation guidance model for video was proposed and an algorithm for solving model parameters was given.Secondly

an optimization problem of resource allocation driven by video semantics in Internet of vehicles was constructed

which was transformed into a convex problem and solved by convex optimization algorithm.Furthermore

in order to reduce the complexity of the convex optimization algorithm

a resource allocation algorithm based on reinforcement Q learning was proposed.Finally

the performance advantages of the proposed algorithm are verified by simulations.

关键词

Keywords

references

ZHANG J P , WANG F Y , WANG K F , et al . Data-driven intelligent transportation systems:a survey [J ] . IEEE Transactions on Intelligent Transportation Systems , 2011 , 12 ( 4 ): 1624 - 1639 .

ZHU L , YU F R , WANG Y G , et al . Big data analytics in intelligent transportation systems:a survey [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 1 ): 383 - 398 .

ZHOU Z , CHEN X , LI E , et al . Edge intelligence:paving the last mile of artificial intelligence with edge computing [J ] . Proceedings of the IEEE , 2019 , 107 ( 8 ): 1738 - 1762 .

LI G Y , BOUKHATEM L , WU J S . Adaptive quality-of-service-based routing for vehicular ad hoc networks with ant colony optimization [J ] . IEEE Transactions on Vehicular Technology , 2017 , 66 ( 4 ): 3249 - 3264 .

SUN L , SHAN H G , HUANG A P , et al . Channel allocation for adaptive video streaming in vehicular networks [J ] . IEEE Transactions on Vehicular Technology , 2017 , 66 ( 1 ): 734 - 747 .

TAO X M , DUAN Y P , XU M , et al . Learning QoE of mobile video transmission with deep neural network:a data-driven approach [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1337 - 1348 .

LIANG H B , ZHANG X H , HONG X T , et al . Reinforcement learning enabled dynamic resource allocation in the Internet of vehicles [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 7 ): 4957 - 4967 .

CHEN X , HWANG J N , MENG D , et al . A quality-of-content-based joint source and channel coding for human detections in a mobile surveillance cloud [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2017 , 27 ( 1 ): 19 - 31 .

GUO C T , LIANG L , LI G Y . Resource allocation for vehicular communications with low latency and high reliability [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 8 ): 3887 - 3902 .

SHI J F , YANG Z H , XU H , et al . Dynamic resource allocation for LTE-based vehicle-to-infrastructure networks [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 5 ): 5017 - 5030 .

WU J Y , YUEN C , CHENG B , et al . Bandwidth-efficient multipath transport protocol for quality-guaranteed real-time video over heterogeneous wireless networks [J ] . IEEE Transactions on Communications , 2016 , 64 ( 6 ): 2477 - 2493 .

LIU C H , HUYNH D Q , SUN Y C , et al . A vision-based pipeline for vehicle counting,speed estimation,and classification [J ] . IEEE Transactions on Intelligent Transportation Systems，4066 , PP ( 99 ): 1 - 14 .

SULLIVAN G J , OHM J R , HAN W J , et al . Overview of the high efficiency video coding (HEVC) standard [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2012 , 22 ( 12 ): 1649 - 1668 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

DOLLAR P , WOJEK C , SCHIELE B , et al . Pedestrian detection:an evaluation of the state of the art [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012 , 34 ( 4 ): 743 - 761 .

SUN P , KRETZSCHMAR H , DOTIWALLA X , et al . Scalability in perception for autonomous driving:Waymo open dataset [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2020 : 2443 - 2451 .

BUBECK S . Convex optimization:algorithms and complexity [J ] . Foundations and Trends in Machine Learning , 2015 , 8 ( 3-4 ): 231 - 357 .

JANG B , KIM M , HARERIMANA G , et al . Q-learning algorithms:a comprehensive classification and applications [J ] . IEEE Access , 2019 , 7 : 133653 - 133667 .

Technical Specification Group Radio Access Network Study on LTE-based V2X Services (Release 14) [R ] . 3GPP TR 36.885 V14.0.0 , 2016 .

GONG Y J , ZHANG J , CHUNG H S H , et al . An efficient resource allocation scheme using particle swarm optimization [J ] . IEEE Transactions on Evolutionary Computation , 2012 , 16 ( 6 ): 801 - 816 .

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