Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (1): 195-203.doi: 10.13229/j.cnki.jdxbgxb20200876

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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

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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

CLC Number: 

  • TP391

Fig.1

Extreme learning machine structure diagram"

Fig.2

Denoising autoencoder network structure"

Fig.3

SDAE-ELM network structure"

Fig.4

Data extraction flowchart"

Fig.5

Original MR data format"

Table 1

Statistical table of partial PRB uplink interference power"

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

Fig.6

Visualization of 12 h uplink interference power data in a cell"

Table 2

Data set composition and distribution table"

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

Table 3

Model parameter combination"

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

Fig.7

Performance analysis of different parameter combinations of SDAE-ELM model"

Table 4

SDAE and SDAE-ELM training time comparison"

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

Fig.8

Variation of classification accuracy in noisy data sets"

Table 5

Comparison of classification accuracy of different algorithms in LTE network uplink interference data set"

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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