Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Processing Request
A Time Delay Prediction Model of 5G Users Based on the BiLSTM Neural Network Optimized by APSO-SD.
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Processing Request
- المؤلفون: Dang, Xiaozheng1 (AUTHOR); He, Di2 (AUTHOR); Xie, Cong3 (AUTHOR)
- المصدر:
Journal of Electrical & Computer Engineering. 6/13/2023, p1-20. 20p.
- الموضوع:
- معلومة اضافية
- نبذة مختصرة :
To address the problems of 5G network planning and optimization, a 5G user time delay prediction model based on the BiLSTM neural network optimized by APSO-SD is proposed. First, a channel generative model based on the ray-tracing model and the statistical channel model is constructed to obtain a large amount of time delay data, and a 5G user ray data feature model based on three-dimensional stereo mapping is proposed for input feature extraction. Then, an adaptive particle swarm optimization algorithm based on a search perturbation mechanism and differential enhancement strategy (APSO-SD) is proposed for the parameters' optimization of BiLSTM neural networks. Finally, the APSO-SD-BiLSTM model is proposed to predict the time delay of 5G users. The experimental results show that the APSO-SD has a better convergence performance and optimization performance in benchmark function optimization compared with the other PSO algorithms, and the APSO-SD-BiLSTM model has better user time delay prediction accuracy in different scenarios. [ABSTRACT FROM AUTHOR]
No Comments.