基于朴素贝叶斯的室内VLC网络天线选择方法
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作者单位:

郑州大学

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中图分类号:

TN911.25

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目),国家高技术研究发展计划(863计划)


Naive Bayesian-Aided Antenna Selection Approach for Indoor VLC Network
Author:
Affiliation:

Zhengzhou University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    在室内多天线多用户可见光通信(Visible Light Communications, VLC)网络中,为了改善在发射天线和用户数量增多的情况下,最优天线选择算法存在时间复杂度过高问题,本文将朴素贝叶斯(Naive Bayes, NB)方法应用于室内多用户 VLC 网络下行链路发光二极管(Light Emitting Diodes, LED)选择问题中。首先,将该LED 选择任务建模为多分类问题,利用用户已知信道状态信息生成训练样本集,并通过 VLC 网络多用户通信和速率最大生成对应类标签;其次,利用生成的训练样本集,通过 NB 方法得到分类器模型;最后,将训练得到的分类器模型应用于新用户的 LED 选择。仿真分析表明,与最优多用户 VLC 网络 LED 选择算法相比,本文提出的基于 NB 的 LED 选择方案可以有效地降低时间复杂度,在算法复杂度和用户传输和速率之间实现了较好平衡。

    Abstract:

    In indoor Visible Light Communications (VLC) network with multiple antennas and multiple users, in order to improve the time complexity of optimal antenna selection algorithm under the condition of increasing number of transmitting antennas and users, Naive Bayes (NB) method is applied to the downlink Light Emitting Diodes (LED) selection problem of indoor multi-user VLC network. Firstly, the LED selection task was modelled as a multi-classification problem, and the training sample set was generated by using the users’m known channel state information. Then, the corresponding class labels were generated by maximizing the sum-rate of multi-user VLC network. Secondly, using the generated training sample set, the classifier model is obtained by NB method. Finally, the trained classifier model is applied to the LED selection of new user. Simulation results show that, compared with the optimal LED selection algorithm of multi-user VLC network, the proposed LED selection scheme aided by NB can effectively reduce the time complexity and achieve a good balance between algorithm complexity and system sum-rate.

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历史
  • 收稿日期:2021-06-03
  • 最后修改日期:2021-09-22
  • 录用日期:2021-09-28
  • 在线发布日期: 2021-11-01
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