Network Selection Algorithm Based on Hilbert Space Vector Weighting

MAO Zhongyang; WANG Tingting; LU Faping; ZHANG Zhilin; KANG Jiafang

doi:10.11999/JEIT230641

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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

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Network Selection Algorithm Based on Hilbert Space Vector Weighting

doi: 10.11999/JEIT230641

MAO Zhongyang^{1, 2},
WANG Tingting^{1, 2, 3
,
,},
LU Faping^{1, 2},
ZHANG Zhilin^{1, 2},
KANG Jiafang^{1, 2}

1.
Aviation Communication Teaching and Research Section, Naval Aeronautic University, Yantai 264001, China
2.
State Key Laboratory of Signal and Information Processing of Shandong, Naval Aeronautic University, Yantai 264001, China
3.
Unit 93786, PLA, Zhangjiakou 075000, China

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)

Received Date: 2023-06-29
Rev Recd Date: 2023-12-01

Available Online: 2024-01-28

Abstract

Abstract

In order to improve the service completion rate of mobile nodes and the efficiency of network resource allocation in maritime heterogeneous wireless network, a network access selection algorithm based on Hilbert space vector assignment is proposed to address the problems of poor matching between existing network selection algorithms and service demands, and low service completion rate in dynamic environment. The algorithm adopts the network-service matching model based on Hilbert space, maps the network characteristics and service requirements to the same space, and measures whether the network meets the service requirements in the same coordinate system; at the same time, it adopts the pre-switching network selection algorithm based on the Technique for Order Preference by Similarity to Ideal Solution method, and introduces the network-service matching weights to correct the normalization matrix of the distance-to-preferred-solution method, so as to ensure that the selected network matches the service requirements, and to ensure that the network matches the service requirements. This ensures that the selected network matches the service requirements and overcomes the problems of traditional network selection where the service requirements are less considered and the network characteristics and service requirements are difficult to be measured uniformly. In addition, the network switching control algorithm based on spatial distance is adopted, and matching weight and spatial distance are introduced into the network switching control to ensure the continuity of service transmission and improve the service completion rate in the dynamic environment. Simulation results show that compared with the comparison algorithm, the service completion rate of this algorithm is improved by at least 6.81%, which effectively improves the service transmission capacity and smoothness of the network, and indirectly realizes the effective allocation of network resources.
- Heterogeneous wireless network,
- Access selection,
- Hilbert space,
- Business requirement,
- TOPSIS

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References(23)

References

[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.
[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.
[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.
[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.0367-234.201605125
[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.
[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.
[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