吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (1): 195-203.doi: 10.13229/j.cnki.jdxbgxb20200876

• 计算机科学与技术 • 上一篇    

结合降噪自编码与极限学习机的LTE上行干扰分析

许鸿奎1,2(),姜彤彤1,李鑫1,姜斌祥1,3,王永雷4   

  1. 1.山东建筑大学 信息与电气工程学院,济南 250101
    2.山东建筑大学 山东省智能建筑技术重点实验室,济南 250101
    3.中国政法大学 青少年犯罪与少年司法研究中心,北京 100088
    4.湖南亿恩科技有限公司 AI研究院,山东 青岛 266000
  • 收稿日期:2020-11-18 出版日期:2022-01-01 发布日期:2022-01-14
  • 作者简介:许鸿奎(1966-),男,教授,博士.研究方向:信号与信息处理.E-mail:xhkui2009@163.com
  • 基金资助:
    国家重点研发计划项目(2017YFC0803604);山东省重大科技创新工程项目(2019JZZY010120)

LTE uplink interference analysis combined with denoising autoencoder and extreme learning machine

Hong-kui XU1,2(),Tong-tong JIANG1,Xin LI1,Bin-xiang JIANG1,3,Yong-lei WANG4   

  1. 1.School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,China
    2.Shandong Key Laboratory of Intelligent Building Technology,Shandong Jianzhu University,Jinan 250101,China
    3.Juvenile Crime & Justice Research Center,China University of Political Science & Law,Beijing 100088,china
    4.AI Research Institute,Hunan ENHT Technology Co. ,Ltd. ,Qingdao 266000,China
  • Received:2020-11-18 Online:2022-01-01 Published:2022-01-14

摘要:

针对长期演进LTE网络上行干扰分类模型中噪声敏感、训练时间长的问题,建立了结合堆栈降噪自编码器与极限学习机的LTE网络上行干扰分析模型。使用上行干扰原始数据无监督地预训练堆栈降噪自编码(SDAE)提取高层抽象特征,并为极限学习机(ELM)分类器提供初始参数。该模型发挥了ELM收敛快和SDAE抑制噪声的优势,同时克服了ELM参数随机赋值造成的鲁棒性不足的问题。实验结果表明,该模型提高了LTE网络上行干扰分析的效率,并具有较强的鲁棒性。

关键词: LTE网络上行干扰, 降噪自编码器, 极限学习机, 特征提取

Abstract:

Aiming at the problems of noise sensitivity and long training time in the uplink interference classification model of LTE (long Term Evolution) network, this article establishes a LTE network uplink interference analysis model combining stacked denoising autoencoder and extreme learning machine, using uplink interference raw data to unsupervised pre-training of SDA (Stacked denoising autoencoder) to extract high-level abstract features, and to provide initial parameters for the ELM (Extreme learning machine) classifier. The model takes advantage of ELM's fast convergence and SDA's noise suppression, and at the same time overcomes the problem of insufficient robustness caused by random assignment of ELM parameters. Experimental results show that this model improves the efficiency of LTE network uplink interference analysis, and at the same time has strong robustness.

Key words: long term evolution network uplink interference, denoising autoencoder, extreme learning machine, feature extraction

中图分类号: 

  • TP391

图1

极限学习机结构图"

图2

降噪自编码器网络结构"

图3

SDAE-ELM网络结构"

图4

数据提取流程图"

图5

原始MR数据格式"

表 1

部分PRB上行干扰功率统计表"

小区ID

PRB0

/dBm

PRB1

/dBm

PRB2

/dBm

PRB3

/dBm

01-106.0-119.8-119.2-118.5
02-106.0-119.4-119.6-118.7
03-106.1-119.5-119.8-119.5
04-106.2-120.0-120.0-119.9
05-105.4-119.3-119.3-119.2
06-106.0-119.9-120.0-119.9
07-106.1-119.8-120.0-119.9
08-106.3-119.9-120.0-119.9

图6

某小区12 h上行干扰功率数据可视化"

表 2

数据集组成分配表"

干扰类型训练集样本测试集样本
互调干扰594201
阻塞干扰709191
外部干扰696204
无干扰696208

表3

SDAE-ELM 模型参数组合"

模型输入层特征层1特征层2ELM隐含层输出层
112256001002004
212256001003004
312256001004004
41225600502004
51225600503004
61225600504004

图 7

SDAE-ELM模型不同参数组合性能分析"

表4

SDAE与SDAE-ELM训练时间对比"

模型SDAE/sSDAE?ELM/s
1187134
2188139
3194134
4192131
5192133
6191131

图8

加噪数据集中的分类准确率变化"

表5

LTE网络上行干扰数据集上不同算法分类准确率对比 (%)"

加噪情况ELMSDAEELM?AESDAE?ELM
未添加噪声93.992.995.996.9
15%高斯白噪声90.990.193.996.5
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