Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (4): 12-19.doi: 10.6040/j.issn.1672-3961.0.2021.547

• Machine Learning & Data Mining • Previous Articles     Next Articles

Pre-allocation of resources based on trajectory prediction in heterogeneous networks

Xiaobin XU1(),Qi WANG1,Bin GAO1,Zhiyu SUN2,*(),Zhongjun LIANG3,Shangguang WANG4   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    2. Meteorological Information Center, Xinjiang Meteorological Information Center, Urumqi 830002, Xinjiang, China
    3. Data Service Department, National Meteorological Information Center, Beijing 100081, China
    4. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2021-11-15 Online:2022-08-20 Published:2022-08-24
  • Contact: Zhiyu SUN E-mail:xuxiaobin@bjut.edu.cn;ilsunyu@163.com

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Abstract:

Aiming at the problems of insufficient trajectory feature learning, low precision of trajectory prediction results, and coarse particles in the research of network resource management methods for trajectory prediction, a trajectory prediction algorithm of bidirectional recurrent neural network was proposed. Through in-depth mining of the user′s movement rules, the user′s movement prediction was realized. According to the user′s mobile prediction information, the network resource pre-allocation plan was designed and the mobile behavior was divided into network resources, then the collaborative resource optimization management of multiple cells was realized. The simulation results showed that in the trajectory prediction problem, the trajectory prediction algorithm of the bidirectional recurrent neural networked had better comprehensive performance than the ordinary neural network algorithm. In the problem of network resource management, the network resource management pre-allocation scheme of trajectory prediction could accurately predict the base station connected by users, so that the base station had a higher resource utilization rate.

Key words: heterogeneous network, network resource management, trajectory prediction, resource pre-allocation, collaborative management

CLC Number: 

  • TP391

Fig.1

Internal structure of BRNN"

Fig.2

Trajectory prediction model based on BRNN"

Fig.3

Heterogeneous network scenario"

Table 1

Trajectory prediction algorithm comparison test parameter setting"

神经元数量 批量大小 训练轮次 预测窗口 学习率
64 64 45 4 10-3

Table 2

Parameter setting of heterogeneous network simulation environment"

ssize/m Mnum mnum Mbandwidth/GHz mbandwidth/GHz
30 4 9 1 0.1

Table 3

Error comparison results when the training set is 80%"

模型名称 MAE MSE RMSE
BRNN 0.563 2 0.479 4 0.692 3
RNN 1.163 9 2.005 1 1.416 0
LSTM 0.973 1 1.879 1 1.370 8
GRU 0.801 0 1.240 7 1.113 8

Table 4

Error comparison results when the training set is 90%"

模型名称 MAE MSE RMSE
BRNN 0.592 5 0.692 4 0.832 1
RNN 4.607 1 1.650 3 1.284 6
LSTM 4.819 3 1.802 4 1.342 5
GRU 3.744 3 1.516 7 1.231 5

Fig.4

Generated four movement modes"

Table 5

Comparison results of four mean errors of 90% training set"

模型名称 MAE MSE RMSE
BRNN 0.617 6 0.781 6 0.884 0
RNN 4.744 2 1.716 6 1.310 2
LSTM 4.998 0 1.904 7 1.380 1
GRU 3.845 4 1.457 5 1.207 3

Table 6

Comparison results of time complexity  s"

模型名称 训练时间 预测时间
BRNN 41.386 6 0.056 1
RNN 20.541 5 0.030 2
LSTM 49.420 8 0.063 8
GRU 46.079 6 0.058 7

Fig.5

Accuracy of cell access for different models under different number of user equipment"

Table 7

The accuracy of each base station under different models  %"

基站名称 BRNN RNN LSTM GRU
MBS1 95.50 84.76 86.89 94.29
MBS2 88.30 80.83 86.32 87.10
MBS3 93.28 85.15 84.47 86.67
MBS4 90.91 83.67 88.35 93.02
mBS1 96.36 89.19 94.12 98.53
mBS2 94.17 85.16 92.70 93.92
mBS3 97.37 88.37 91.67 95.89
mBS4 93.44 91.38 92.19 90.00
mBS5 91.53 90.23 92.11 93.77
mBS6 93.57 88.72 87.40 87.30
mBS7 97.14 96.30 92.19 93.44
mBS8 94.03 90.77 90.40 90.23
mBS9 93.85 93.06 86.27 95.83

Table 8

The recall rate of each base station under different models  %"

基站名称 BRNN RNN LSTM GRU
MBS1 87.60 84.76 88.33 90.00
MBS2 90.22 80.83 87.07 92.05
MBS3 91.74 85.15 90.63 85.85
MBS4 89.11 83.67 82.73 88.24
mBS1 96.36 89.19 93.02 91.78
mBS2 95.76 85.16 89.44 92.05
mBS3 96.10 88.37 91.67 94.59
mBS4 95.80 91.38 91.47 95.12
mBS5 94.74 90.23 91.42 93.05
mBS6 92.91 88.72 90.98 90.91
mBS7 97.14 96.30 89.39 91.94
mBS8 92.65 90.77 90.40 93.75
mBS9 95.31 93.06 91.67 100.00

Fig.6

Spectrum utilization of different models under different number of user equipment"

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