Deep Learning-based Reference Signal Received Power Prediction for LTE Communication System
Author:
Affiliation:
1. School of Engineering, King Mongkut's Institute of Technology Ladkrabang,Bangkok,Thailand,6001
2. Advanced Wireless Network Company Limited,Bangkok,Thailand
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9894733/9894851/09895098.pdf?arnumber=9895098
Reference9 articles.
1. Machine Learning for Forecasting Signal Strength in Mobile Networks;prihodko;Independent Thesis Advanced Level,2018
2. City-Wide Signal Strength Maps: Prediction with Random Forests
3. Mobility Support in Cellular Networks
4. Feature Extraction in Reference Signal Received Power Prediction Based on Convolution Neural Networks
5. Radio Resource Management in WiMAX;shabbir;MS thesis Blekinge Institute of Technology Karlskrona Sweden,2009
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy;IEEE Access;2024
2. Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss;2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2023-06-25
3. Improvement of Deep Learning-Based Reference Signal Received Power Prediction for LTE Communication System;2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2023-06-25
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