基于主客观赋权的多属性空间节点选择算法

doi:10.11772/j.issn.1001-9081.2017102534

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1089-1094.DOI: 10.11772/j.issn.1001-9081.2017102534

基于主客观赋权的多属性空间节点选择算法

戴翠琴, 王文翰

重庆邮电大学移动通信技术重点实验室, 重庆 400065

收稿日期:2017-10-25 修回日期:2017-11-16 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 戴翠琴
作者简介:戴翠琴(1976-),女,宁夏固原人,副教授,博士研究生,主要研究方向:无线协作传输;王文翰(1993-),男,安徽合肥人,硕士研究生,主要研究方向:无线协作传输。
基金资助:
国家自然科学基金资助项目（61601075）；重庆市科委自然科学基金资助项目（cstc2016jcyjA0174）；重庆市教委科学技术研究项目（KJ1500440）。

Multi-attribute spatial node selection algorithm based on subjective and objective weighting

DAI Cuiqin, WANG Wenhan

Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2017-10-25 Revised:2017-11-16 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61601075), the Natural Science Foundation of Chongqing Science and Technology Commission (cstc2016jcyjA0174), the Scientific and Technological Research Program of Chongqing Education Commission (KJ1500440).

摘要/Abstract

摘要： 针对空间协作传输中单属性协作节点选择算法无法兼顾系统可靠性和生存时间的问题，引入多属性决策方法（MADM），综合考虑信道衰落等级、协作节点剩余能量和误码率三个属性对空间协作节点进行多属性评估，提出一种基于主客观赋权的多属性空间协作节点选择（SOW-CNS）算法。首先，根据信道受阴影衰落影响程度，建立两状态无线信道模型，分别为无阴影Loo信道衰落模型和有阴影Corazza信道衰落模型；其次，引入基于主客观赋权的多属性决策策略，使用层次分析法和信息熵法建立空间协作节点的主观属性权向量和客观属性权向量；然后，使用最大熵原理和离差和最大法计算主客观属性权向量；最后，利用主客观属性权向量与各节点的属性值计算各潜在节点的评价值，进而选出最佳协作节点参与空间信息协作传输。仿真结果表明，与传统最佳质量协作节点选择算法（BQ-CNS）、能量公平性协作节点选择算法（EF-CNS）和随机协作节点选择算法（R-CNS）相比，基于主客观赋权的多属性决策算法不仅降低系统误码率，而且能够获得更长的系统生存期。

关键词: 空间协作传输, 协作节点选择, 多属性决策, 误码率, 生存期

Abstract: Aiming at the problem that single attribute cooperative node selection algorithm in spatial cooperative transmission cannot balance the reliability and the survival time of the system, a Subjective and Objective Weighting Based Multi-attribute Cooperative Node Selection (SOW-CNS) algorithm was proposed by introducing Multiple Attribute Decision Making (MADM), and considering three attributes such as channel fading level, residual energy of the cooperative nodes and packet loss rate were considered to complement multi-attribute evaluation of spatial cooperative nodes. Firstly, according to the influence of shadow fading, a two-state wireless channel model was established, including the shadow free Loo channel fading model and the shadow Corazza channel fading model. Secondly, considering the channel fading level, the residual energy of cooperative nodes and the system packet loss rate, the multi-attribute decision making strategy based on subjective and objective weighting was introduced, and the subjective attribute weight vector and objective attribute weight vector of spatial cooperative nodes were established by using Analytic Hierarchy Process (AHP) and information entropy method. Then the maximum entropy principle, the deviation and the maximum method were used to calculate the subjective and objective attribute weight vectors. Finally, the evaluation value of each potential node was calculated by using the subjective and objective attributes weight vector and the attribute value of each node, and then the best cooperative node was selected to participate in the cooperative transmission of spatial information. Simulation results show that SOW-CNS algorithm detain lower system packet loss rate, and longer system Survival time compared with traditional Best Quality based Cooperative Node Selection (BQ-CNS) algorithm, Energy Fairness based Cooperative Node Selection (EF-CNS) algorithm and Random based Cooperative Node Selection (R-CNS) algorithm.

Key words: spatial cooperative transmission, cooperative node selection, Multiple Attribute Decision Making (MADM), bit error rate, survival time

中图分类号:

TN929.5

戴翠琴, 王文翰. 基于主客观赋权的多属性空间节点选择算法[J]. 计算机应用, 2018, 38(4): 1089-1094.

DAI Cuiqin, WANG Wenhan. Multi-attribute spatial node selection algorithm based on subjective and objective weighting[J]. Journal of Computer Applications, 2018, 38(4): 1089-1094.

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基于主客观赋权的多属性空间节点选择算法

Multi-attribute spatial node selection algorithm based on subjective and objective weighting

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