Abstract
The advent of 5G technology promises to revolutionize wireless communication with unprecedented data speeds, ultra-low latency, and reliable connectivity. However, ensuring consistent Quality of Service (QoS) across diverse environments presents significant challenges. This study evaluates the effectiveness of various regression algorithms in predicting 5G QoS. By applying Multiple Linear Regression (MLR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to a comprehensive 5G dataset, we compare their performance based on key metrics. Our findings highlight SVM's superior capability in handling non-linear relationships, offering a robust solution for 5G QoS prediction.
| Original language | English |
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| Title of host publication | 2024 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 235-238 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798350364163 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024 - Langkawi, Malaysia Duration: 21 Dec 2024 → 23 Dec 2024 |
Publication series
| Name | 2024 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024 |
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Conference
| Conference | 10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024 |
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| Country/Territory | Malaysia |
| City | Langkawi |
| Period | 21/12/24 → 23/12/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- 5G Networks
- Machine Learning in Telecommunications
- Network Management
- Predictive Modeling
- Quality of Service (QoS)
- Regression