高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于Hilbert空间向量赋权的网络选择算法

毛忠阳 王婷婷 陆发平 张治霖 康家方

毛忠阳, 王婷婷, 陆发平, 张治霖, 康家方. 基于Hilbert空间向量赋权的网络选择算法[J]. 电子与信息学报. doi: 10.11999/JEIT230641
引用本文: 毛忠阳, 王婷婷, 陆发平, 张治霖, 康家方. 基于Hilbert空间向量赋权的网络选择算法[J]. 电子与信息学报. doi: 10.11999/JEIT230641
MAO Zhongyang, WANG Tingting, LU Faping, ZHANG Zhilin, KANG Jiafang. Network Selection Algorithm Based on Hilbert Space Vector Weighting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230641
Citation: MAO Zhongyang, WANG Tingting, LU Faping, ZHANG Zhilin, KANG Jiafang. Network Selection Algorithm Based on Hilbert Space Vector Weighting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230641

基于Hilbert空间向量赋权的网络选择算法

doi: 10.11999/JEIT230641
基金项目: 国家基础加强计划重点基础研究项目(61701518),山东省“泰山学者”建设工程专项经费(TS20081330),山东省自然科学基金(ZR2023MD045)
详细信息
    作者简介:

    毛忠阳:男,教授,研究方向为抗干扰通信技术、无线光通信、智能组网

    王婷婷:女,硕士生,研究方向为智能组网

    陆发平:男,讲师,主要研究方向为现代通信系统、非正弦波通信、信号波形设计、高效调制

    张治霖:男,博士生,研究方向为智能组网

    康家方:男,副教授,研究方向为抗干扰通信技术、智能组网

    通讯作者:

    王婷婷 709718966@qq.com

  • 中图分类号: TN92

Network Selection Algorithm Based on Hilbert Space Vector Weighting

Funds: The National Basic Research Program of China (61701518), The Special Fund for Construction Project of “Taishan Scholars” of Shandong Province (TS20081330), Shandong Province Natural Science Foundation (ZR2023MD045)
  • 摘要: 为了提升海上异构无线网络中移动节点业务完成率和网络资源配置效率,针对现有网络选择算法与业务需求匹配性较差、动态环境下业务完成率不高的问题,该文提出一种基于Hilbert空间向量赋权的网络接入选择算法。该算法采用基于Hilbert空间的网络-业务匹配模型,将网络特征与业务需求映射至同一空间,在同一坐标系内衡量网络是否满足业务需求;同时,采用基于优劣解距离法的预切换网络选择算法,引入网络-业务匹配权重对优劣解距离法标准化矩阵进行修正,确保所选网络与业务需求相匹配,克服传统网络选择中业务需求考虑较少、网络特征与业务需求难以进行统一衡量的问题。此外,采用基于空间距离的网络切换控制算法,将匹配权重、空间距离引入网络切换控制,保证业务传输连续性,提高动态环境下的业务完成率。仿真结果表明,相较于对比算法,该算法的业务平均完成率提高6.81%以上,有效提升了网络的业务传输能力和通畅度,间接实现了网络资源的有效配置。
  • 图  1  海上异构无线网络架构

    图  2  基于Hilbert空间向量赋权的网络选择模型

    图  3  算法流程图

    图  4  机动站点初始位置图

    图  5  移动节点实时网络参数和业务传输情况对比

    1  基于Hilbert空间向量赋权的网络选择算法

     输入: 可用网络C;可用网络数据B;待处理业务S;时间Tt
     阈值D
     输出: 网络选择结果策略集合$\Pi _{m \in M}^C$
     1:$\forall {N_m} \in C$,$\forall {{\text{s}}_t} \in S$:$ U\left({s}_{i},{N}_{j}\right),{s}_{i}\in S,{N}_{j}\in C $
     2:for n = 1, 2, ···, Tt
     3:  更新当前连接网络Nj
     4:  for k = 1, 2, ···, m
     5:   计算业务权重向量$ {w_{yw}} $,网络权重向量$w_{wl}^{}$,匹配权重
         向量$ \Delta w $
     6:   改进的TOPSIS算法得到最大贴合度网络
         ${N_{r \in m}},{S_{\max }} = {S_r}$
     7:   计算切换度量值${d_{jr}}$
     8:   if ${d_{jr}} < D$
          保持原网络连接
     9:   else
          进行网络切换
     10:  end
     11: end
     12:end
    下载: 导出CSV

    表  1  网络属性参数表

    网络属性包延时(ms)网络速率(kbps)丢包率(×$1{0^{ - 5}}$)抖动(ms)
    参数范围20~25010~18005~1201~25
    下载: 导出CSV

    表  2  业务参数表

    业务类别交互类业务会话类业务流媒体类业务
    包延时(ms)50~27030~100200~1000
    速率(kbps)10~10064~300400~1000
    丢包率(×$1{0^{ - 5}}$)1~101~3001~100
    抖动(ms)1~101~401~50
    服务时间均值(s)158
    个数30010050
    下载: 导出CSV

    表  3  业务传输和切换情况对比

    网络选择
    情况
    网络
    匹配率(%)
    业务
    完成率(%)
    业务
    丢弃率(%)
    切换
    次数
    50次业务
    平均完成率(%)
    50次业务
    平均丢弃率(%)
    50次平均
    切换次数
    Dmin35.0056.294.55↓052.4311.07↓0.60
    SNRmax↓27.9057.433.28154.188.845.74
    TOPSIS32.10↓52.14↑12.446↓51.12↑14.013.80
    DAHP+S49.1058.438.96869.354.708.04
    本文算法↑87.30↑80.43↓0.00↑13↑76.16↓2.18↑11.00
    下载: 导出CSV
  • [1] XU Yongjun, GUI Guan, GACANIN H, et al. A survey on resource allocation for 5g heterogeneous networks: Current research, future trends, and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668–695. doi: 10.1109/COMST.2021.3059896.
    [2] ZHU Anqi, MA Mingfang, GUO Songtao, et al. Adaptive access selection algorithm for multi-service in 5G heterogeneous internet of things[J]. IEEE Transactions on Network Science and Engineering, 2022, 9(3): 1630–1644. doi: 10.1109/TNSE.2022.3148399.
    [3] XU Yongjun, XIE Hao, WU Qingqing, et al. Robust max-min energy efficiency for RIS-aided HetNets with distortion noises[J]. IEEE Transactions on Communications, 2022, 70(2): 1457–1471. doi: 10.1109/TCOMM.2022.3141798.
    [4] YU Yiding, LIEW S C, and WANG Taotao. Multi-agent deep reinforcement learning multiple access for heterogeneous wireless networks with imperfect channels[J]. IEEE Transactions on Mobile Computing, 2022, 21(10): 3718–3730. doi: 10.1109/TMC.2021.3057826.
    [5] KARIMI-BIDHENDI S, GUO Jun, and JAFARKHANI H. Energy-efficient deployment in static and mobile heterogeneous multi-hop wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2022, 21(7): 4973–4988. doi: 10.1109/TWC.2021.3135385.
    [6] GUO Shiwei. An improved KNN based decision algorithm for vertical handover in heterogeneous wireless networks[C]. 2021 40th Chinese Control Conference (CCC), Shanghai, China, 2021: 3011–3016. doi: 10.23919/CCC52363.2021.9550412.
    [7] GOUTAM S, UNNIKRISHNAN S, and KUDU N. Decision for vertical handover using k-means clustering algorithm[C]. 2020 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India, 2020: 31–35. doi: 10.1109/IBSSC51096.2020.9332156.
    [8] WANG Dongli, SUN Qilu, WANG Yequn, et al. Network-assisted vertical handover scheme in heterogeneous aeronautical network[C]. 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, 2020: 148–152. doi: 10.1109/IPEC49694.2020.9115120.
    [9] XIE Jianli, GAO Wenjuan, and LI Cuiran. Heterogeneous network selection optimization algorithm based on a Markov decision model[J]. China Communications, 2020, 17(2): 40–53. doi: 10.23919/JCC.2020.02.004.
    [10] 赵亚军, 郁光辉, 徐汉青. 6G移动通信网络: 愿景、挑战与关键技术[J]. 中国科学:信息科学, 2019, 49(8): 963–987. doi: 10.1360/N112019-00033.

    ZHAO Yajun, YU Guanghui, and XU Hanqing. 6G mobile communication networks: Vision, challenges, and key technologies[J]. Scientia Sinica (Informationis), 2019, 49(8): 963–987. doi: 10.1360/N112019-00033.
    [11] 章广梅. 基于AI的无线网络感知技术研究综述[J]. 电讯技术, 2022, 62(5): 686–694. doi: 10.3969/j.issn.1001-893x.2022.05.020.

    ZHANG Guangmei. Researches on wireless network sensing technology based on AI: An overview[J]. Telecommunication Engineering, 2022, 62(5): 686–694. doi: 10.3969/j.issn.1001-893x.2022.05.020.
    [12] 俞鹤伟, 梁根. 异构无线网络接入选择算法综述[J]. 哈尔滨工业大学学报, 2017, 49(11): 178–188. doi: 10.11918/j.issn.0367-234.201605125.

    YU Hewei and LIANG Gen. A survey of access selection algorithms in heterogeneous wireless networks[J]. Journal of Harbin Institute of Technology, 2017, 49(11): 178–188. doi: 10.11918/j.issn.0367-6234.201605125. doi: 10.11918/j.issn.0367-234.201605125.
    [13] OZSAHIN D U, GÖKÇEKUŞ H, UZUN B, et al. Application of Multi-Criteria Decision Analysis in Environmental and Civil Engineering[M]. Cham: Springer, 2021: 268–285. doi: 10.1007/978-3-030-64765-0.
    [14] 孙雷, 田辉, 沈东明, 等. 基于Hilbert空间向量范数的网络选择算法[J]. 北京邮电大学学报, 2009, 32(4): 54–58. doi: 10.3969/j.issn.1007-5321.2009.04.012.

    SUN Lei, TIAN Hui, SHEN Dongming, et al. A network selection scheme based on hilbert space vector norms[J]. Journal of Beijing University of Posts and Telecommunications, 2009, 32(4): 54–58. doi: 10.3969/j.issn.1007-5321.2009.04.012.
    [15] LAHBY M, CHERKAOUI L, and ADIB A. An enhanced-TOPSIS based network selection technique for next generation wireless networks[C]. ICT 2013, Casablanca, Morocco, 2013: 1–5. doi: 10.1109/ICTEL.2013.6632067.
    [16] 陈香, 唐加山, 曹端喜. 基于效用函数的E-TOPSIS异构无线网络选择算法[J]. 现代计算机, 2020(32): 3–7. doi: 10.3969/j.issn.1007-1423.2020.32.001.

    CHEN Xiang, TANG Jiashan, and CAO Duanxi. E-TOPSIS heterogeneous wireless network selection algorithm based on utility functions[J]. Modern Computer, 2020(32): 3–7. doi: 10.3969/j.issn.1007-1423.2020.32.001.
    [17] 毛忠阳, 张治霖, 刘锡国, 等. 基于动态AHP的海上移动节点网络选择算法[J]. 系统工程与电子技术, 2022, 44(6): 2011–2018. doi: 10.12305/j.issn.1001-506X.2022.06.29.

    MAO Zhongyang, ZHANG Zhilin, LIU Xiguo, et al. Network selection algorithm for maritime mobile nodes based on dynamic AHP[J]. Systems Engineering and Electronics, 2022, 44(6): 2011–2018. doi: 10.12305/j.issn.1001-506X.2022.06.29.
    [18] 马彬, 李尚儒, 谢显中. 异构无线网络中基于人工神经网络的自适应垂直切换算法[J]. 电子与信息学报, 2019, 41(5): 1210–1216. doi: 10.11999/JEIT180534.

    MA Bin, LI Shangru, and XIE Xianzhong. An adaptive vertical handover algorithm based on artificial neural network in heterogeneous wireless networks[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1210–1216. doi: 10.11999/JEIT180534.
    [19] 王睿. 基于智能优化算法的海事异构通信网络资源分配研究[D]. [硕士论文], 大连海事大学, 2019.

    WANG Rui. Research on resource allocation of marine heterogeneous communication network based on intelligent optimization algorithm[D]. [Master dissertation], Dalian Maritime University, 2019.
    [20] 孙雷. 异构无线环境中联合无线资源管理关键技术研究[D]. [博士论文], 北京邮电大学, 2011.

    SUN Lei. Research on key strategies of common radioresource management in heterogeneous radio environments [D]. [Ph. D. dissertation], Beijing University of Posts and Telecommunications, 2011.
    [21] 靳超, 李德敏, 佟乐, 等. 一种高斯-马尔科夫自组网组移动模型[J]. 通信技术, 2011, 44(2): 59–61,64. doi: 10.3969/j.issn.1002-0802.2011.02.021.

    JIN Chao, LI Demin, TONG Le, et al. Gauss-markov group mobility model for Ad Hoc networks[J]. Communications Technology, 2011, 44(2): 59–61,64. doi: 10.3969/j.issn.1002-0802.2011.02.021.
    [22] 马彬, 张文静, 谢显中. 面向终端个性化服务的模糊垂直切换算法[J]. 电子与信息学报, 2017, 39(6): 1284–1290. doi: 10.11999/JEIT16039.

    MA Bin, ZHANG Wenjing, and XIE Xianzhong. Individualization service oriented fuzzy vertical handover algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1284–1290. doi: 10.11999/JEIT16039.
    [23] 方旭愿, 田红心, 孙德春, 等. 基于绿色能源感知的效用函数异构网络接入算法[J]. 计算机科学, 2019, 46(8): 127–132. doi: 10.11896/j.issn.1002-137X.2019.08.021.

    FANG Xuyuan, TIAN Hongxin, SUN Dechun, et al. Utility function heterogeneous network access algorithm based on green energy perception[J]. Computer Science, 2019, 46(8): 127–132. doi: 10.11896/j.issn.1002-137X.2019.08.021.
  • 加载中
图(5) / 表(4)
计量
  • 文章访问数:  61
  • HTML全文浏览量:  21
  • PDF下载量:  21
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-06-29
  • 修回日期:  2023-12-01
  • 网络出版日期:  2024-01-28

目录

    /

    返回文章
    返回