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Modern society places a great deal of importance on wireless communication devices, which have revolutionized how people connect, conduct business, and obtain information. Wireless communication technologies do, in fact, play a key part in modern society and have a big impact on many different industries. Entertainment, Business and Commerce, Commercial Applications, Healthcare, Safety and Emergency Services, and Internet of Things (IoT) are a few of the important industries where wireless communication technologies are essential. From 1G to the 5G systems that are currently in use, wireless communication systems have continuously advanced through time. The future of wireless networks will be shaped by artificial intelligence (AI) and machine learning (ML), as well as the next sixth-generation (6G) wireless technologies. We specifically describe a conceptual model for 6G and demonstrate how ML approaches are used and play a part in each layer of the model. Here, we tested the performance of finding the optimum network with some well-known ML algorithms, including support vector machine (SVM), k-nearest neighbors (KNN), Random Forest, and XGBoost. The results of our experiments clearly show which algorithm is superior for categorizing wireless communication. We also attempt to address some potential application and research issues in the field of ML and AI for 6G networks.

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Empowering the Next Generation of Wireless Networks: Advanced Machine Learning Techniques for 5G and Beyond

  • 会议论文

  • 发表日期：

    2025年

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  • Mortha Sharmila,
  • Salapu Venkata Lakshmi,
  • Polinati Mounika,
  • Syam Kumar Savaram

摘要

Modern society places a great deal of importance on wireless communication devices, which have revolutionized how people connect, conduct business, and obtain information. Wireless communication technologies do, in fact, play a key part in modern society and have a big impact on many different industries. Entertainment, Business and Commerce, Commercial Applications, Healthcare, Safety and Emergency Services, and Internet of Things (IoT) are a few of the important industries where wireless communication technologies are essential. From 1G to the 5G systems that are currently in use, wireless communication systems have continuously advanced through time. The future of wireless networks will be shaped by artificial intelligence (AI) and machine learning (ML), as well as the next sixth-generation (6G) wireless technologies. We specifically describe a conceptual model for 6G and demonstrate how ML approaches are used and play a part in each layer of the model. Here, we tested the performance of finding the optimum network with some well-known ML algorithms, including support vector machine (SVM), k-nearest neighbors (KNN), Random Forest, and XGBoost. The results of our experiments clearly show which algorithm is superior for categorizing wireless communication. We also attempt to address some potential application and research issues in the field of ML and AI for 6G networks.