超密集网络中基于判别函数的聚类算法

doi:10.13190/j.jbupt.2021-180

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (2): 104-109.doi: 10.13190/j.jbupt.2021-180

超密集网络中基于判别函数的聚类算法

康玲¹, 王翊^1,2, 胡艳军¹, 蒋芳¹, 李莉萍¹

1. 安徽大学计算智能与信号处理教育部重点实验室, 合肥 230601;
2. 中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室, 上海 200050

收稿日期:2021-08-25 发布日期:2021-12-16
通讯作者: 王翊(1983—),男,讲师,硕士生导师,邮箱:yiwang@ahu.edu.cn。 E-mail:yiwang@ahu.edu.cn
作者简介:康玲(1993—),女,博士生。
基金资助:
国家自然科学基金面上项目(62071002);中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室开放课题(20190911)

Clustering Algorithm Combined with Discriminant Function in Ultra Dense Network

KANG Ling¹, WANG Yi^1,2, HU Yanjun¹, JIANG Fang¹, LI Liping¹

1. Key Laboratory of Intelligent Computing and Signal Processing(Ministry of Education), Anhui University, Hefei 230601, China;
2. Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology (Chinese Academy of Sciences), Shanghai 200050, China

Received:2021-08-25 Published:2021-12-16

摘要/Abstract

摘要： 超密集网络可以通过虚拟小区间的协作来提升用户体验,但由于小区的重叠覆盖使得用户间存在较复杂的干扰问题。因此,提出了一种基于判别函数的聚类算法来缓解强干扰带来吞吐量下降的问题。首先,利用用户间干扰信道的余弦相似度定义用户间的干扰网络;然后,基于干扰网络选出簇头并划分用户,同时为了解决虚拟小区下的模糊用户归属簇问题,以簇间干扰权重之和最大,簇内干扰权重之和最小为原则设计判别函数,对用户进行模糊归类。仿真结果表明,在不增加复杂度的同时,所提算法比其他方法的系统吞吐量提升了10%~30%,对于边缘用户具有一定优势。

关键词: 超密集网络, 干扰网络, 判别函数, 模糊用户

Abstract: Ultra-dense networks can enhance user experience through collaboration between virtual cells, while the overlapping coverage of cells makes the interference problem between users more complex. Therefore, a discriminant function-based clustering algorithm is proposed to mitigate the throughput degradation problem caused by strong interference. Firstly, the inter-user interference network is defined based on the cosine similarity of inter-user interference channels. Then, cluster heads are selected and users are classified based on the interference network. Meanwhile, to solve the fuzzy user belonging to clusters under virtual cells, a discriminant function is designed to fuzzy-classify users based on the principle of maximizing the sum of inter-cluster interference weights and minimizing the sum of intra-cluster interference weights. The simulation results show that compared with the existing methods, the proposed algorithm improves the system throughput by 10%-30% without increasing the complexity, and has certain advantages for edge users.

Key words: ultra-dense network, interference network, discriminant function, fuzzy user

中图分类号:

TN92

康玲, 王翊, 胡艳军, 蒋芳, 李莉萍. 超密集网络中基于判别函数的聚类算法[J]. 北京邮电大学学报, 2022, 45(2): 104-109.

KANG Ling, WANG Yi, HU Yanjun, JIANG Fang, LI Liping. Clustering Algorithm Combined with Discriminant Function in Ultra Dense Network[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 104-109.

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超密集网络中基于判别函数的聚类算法

Clustering Algorithm Combined with Discriminant Function in Ultra Dense Network

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