Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

被引:4
作者
Aldosary, Abdallah Mobark [1 ]
Aldossari, Saud Alhajaj [2 ]
Chen, Kwang-Cheng [3 ]
Mohamed, Ehab Mahmoud [2 ,4 ]
Al-Saman, Ahmed [5 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Dept Comp Sci, As Sulayyil 18211, Saudi Arabia
[2] Prince Sattam Bin Abdulaziz Univ, Dept Elect Engn, Wadi Addwasir 11991, Saudi Arabia
[3] Univ S Florida, Dept Elect Engn, Tampa, FL 33620 USA
[4] Aswan Univ, Elect Engn Dept, Aswan 81542, Egypt
[5] Norwegian Univ Sci & Technol, Fac Engn, N-7491 Trondheim, Norway
关键词
beyond; 5G; mmWave; wireless channel modeling; bands; path loss; machine learning; MLP; random forests;
D O I
10.3390/electronics10243114
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.
引用
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页数:19
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