基于遗传算法的5G车联网的数据协作分发策略研究
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摘要
近年来,5G车联网相关问题日益受到关注,由于车辆的快速移动性导致在基站覆盖范围内与之通信的时间很短,从而获得的数据有限。针对这一问题,在采用5G通信技术中的D2D来实现车辆间灵活的终端通信的车联网环境下,提出了一种基于遗传算法的数据协作分发方案,通过二进制编码来表现车辆是否被选择作为协作分发数据的车辆,寻找最优的协作分发方案来实现优化请求车辆能效的目标。仿真结果表明,相较于常规的随机方案,所提出的方案表现出较强的全局寻优能力,能够有效提高请求车辆的能效,实现接收数据的最大化。
In recent years, more and more attention has been paid to the problems related to 5 G networked vehicles. Due to the rapid mobility of vehicles, the time of communication in the coverage of the base station is very short, and the data obtained is limited. In order to solve this problem, a data cooperative distribution scheme based on genetic algorithm is proposed in the networked vehicles environment that D2 D of the 5 G communication technology is used to realize flexible terminal communication between vehicles. Through binary coding, it shows whether the vehicle is selected as a cooperative distribution vehicle, and find the optimal cooperative distribution scheme to achieve the goal of optimizing the energy efficiency of the request vehicle. The simulation results show that the proposed scheme has a stronger global optimization ability than the conventional random scheme, which can effectively improve the energy efficiency of the requesting vehicle and maximize the received data.
In recent years, more and more attention has been paid to the problems related to 5 G networked vehicles. Due to the rapid mobility of vehicles, the time of communication in the coverage of the base station is very short, and the data obtained is limited. In order to solve this problem, a data cooperative distribution scheme based on genetic algorithm is proposed in the networked vehicles environment that D2 D of the 5 G communication technology is used to realize flexible terminal communication between vehicles. Through binary coding, it shows whether the vehicle is selected as a cooperative distribution vehicle, and find the optimal cooperative distribution scheme to achieve the goal of optimizing the energy efficiency of the request vehicle. The simulation results show that the proposed scheme has a stronger global optimization ability than the conventional random scheme, which can effectively improve the energy efficiency of the requesting vehicle and maximize the received data.
引文
[1] 王良民, 刘晓龙, 李春晓,等. 5G车联网展望[J]. 网络与信息安全学报, 2016, 2(6):1-12.
[2] GONG H, YU L, LIU N, et al. Mobile content distribution with vehicular cloud in urban VANETs[J].2016, 13(8):84-96.
[3] CHEN J, ZAFAR A, MAO G, et al. On the achievable throughput of cooperative vehicular networks[C].IEEE International Conference on Communications,2016:1-7.
[4] MERSHAD K, ARYAIL H. A framework for secure and efficient data acquisition in vehicular Ad hoc networks[J]. IEEE Transactions on Vehicular Technology, 2013, 62(2): 536-551.
[5] 徐灿辉. 5G网络架构对车联网发展影响的研究[J]. 广东通信技术, 2018(1):76-79.
[6] LLUSIA D, MáRQUEZ R, BELTRáN J F, et al. IEEE 802.11p: Towards an international standard for wireless access in vehicular environments[C]. IEEE Vehicular Technology Conference, 2008:2036-2040.
[7] ARANITI G, CAMPOLO C,CONDOLUCI M,et al.LTE for vehicular networking: A survey[J]. IEEE Communications Magazine, 2013, 51(5):148-157.
[8] SUN W, STROM E G, BRANNSTROM F, et al. D2D-based V2V communications with latency and reliability constraints[C]. Globecom Workshops,IEEE, 2015.
[9] SUN W,YUAN D,STR?M E G,et al. Cluster-based radio resource management for D2D-supported safety-critical V2X communications[J]. IEEE Transactions on Wireless Communications, 2016, 15(4):2756-2769.
[10] 彭军, 马东, 刘凯阳,等. 基于LTE D2D技术的车联网通信架构与数据分发策略研究[J]. 通信学报, 2016, 37(7): 62-70.
[11] ZHOU Z, YU H, XU C, et al. Dependable content distribution in D2D-based cooperative vehicular networks: A big data-integrated coalition game approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3):953-964.
[12] JING Y, JAFARKHANI H. Single and multiple relay selection schemes and their achievable diversity orders[J]. IEEE Transactions on Wireless Communications,2009, 8(3):1414-1423.
[13] 顾玮. 遗传算法概述[J]. 办公自动化, 2016(7):34-35.
[14] 于光帅, 于宪伟. 一种改进的自适应遗传算法[J]. 数学的实践与认识, 2015, 45(19):259-264.
[15] 周现甫. 遗传算法的原理及应用[J]. 科技展望, 2017, 27(3):265-265.
[16] 陈刘伟, 梁俊, 朱巍. 一种基于自适应遗传算法的星地协作系统中继选择策略[J]. 空军工程大学学报(自然科学版), 2016, 17(1):41-45.
[2] GONG H, YU L, LIU N, et al. Mobile content distribution with vehicular cloud in urban VANETs[J].2016, 13(8):84-96.
[3] CHEN J, ZAFAR A, MAO G, et al. On the achievable throughput of cooperative vehicular networks[C].IEEE International Conference on Communications,2016:1-7.
[4] MERSHAD K, ARYAIL H. A framework for secure and efficient data acquisition in vehicular Ad hoc networks[J]. IEEE Transactions on Vehicular Technology, 2013, 62(2): 536-551.
[5] 徐灿辉. 5G网络架构对车联网发展影响的研究[J]. 广东通信技术, 2018(1):76-79.
[6] LLUSIA D, MáRQUEZ R, BELTRáN J F, et al. IEEE 802.11p: Towards an international standard for wireless access in vehicular environments[C]. IEEE Vehicular Technology Conference, 2008:2036-2040.
[7] ARANITI G, CAMPOLO C,CONDOLUCI M,et al.LTE for vehicular networking: A survey[J]. IEEE Communications Magazine, 2013, 51(5):148-157.
[8] SUN W, STROM E G, BRANNSTROM F, et al. D2D-based V2V communications with latency and reliability constraints[C]. Globecom Workshops,IEEE, 2015.
[9] SUN W,YUAN D,STR?M E G,et al. Cluster-based radio resource management for D2D-supported safety-critical V2X communications[J]. IEEE Transactions on Wireless Communications, 2016, 15(4):2756-2769.
[10] 彭军, 马东, 刘凯阳,等. 基于LTE D2D技术的车联网通信架构与数据分发策略研究[J]. 通信学报, 2016, 37(7): 62-70.
[11] ZHOU Z, YU H, XU C, et al. Dependable content distribution in D2D-based cooperative vehicular networks: A big data-integrated coalition game approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3):953-964.
[12] JING Y, JAFARKHANI H. Single and multiple relay selection schemes and their achievable diversity orders[J]. IEEE Transactions on Wireless Communications,2009, 8(3):1414-1423.
[13] 顾玮. 遗传算法概述[J]. 办公自动化, 2016(7):34-35.
[14] 于光帅, 于宪伟. 一种改进的自适应遗传算法[J]. 数学的实践与认识, 2015, 45(19):259-264.
[15] 周现甫. 遗传算法的原理及应用[J]. 科技展望, 2017, 27(3):265-265.
[16] 陈刘伟, 梁俊, 朱巍. 一种基于自适应遗传算法的星地协作系统中继选择策略[J]. 空军工程大学学报(自然科学版), 2016, 17(1):41-45.