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| Title: | 結合分類與迴歸技術之電信網路弱訊區域偵測混合模型
A Hybrid Model for Weak Signal Area Detection in Telecom Networks Using Classification and Regression Techniques |
| Authors: | 張舜欣
Chang, Shun-Hsin |
| Contributors: | 謝佩璇
張舜欣
Chang, Shun-Hsin |
| Keywords: | 機器學習
弱訊區偵測
分類技術
迴歸模型
網路優化
Machine Learning
Weak Signal Detection
Classification Techniques
Regression Models
Network Optimization |
| Date: | 2024 |
| Issue Date: | 2025-02-04 16:11:18 (UTC+8) |
| Abstract: | 電信網路優化是提升網路效能和使用者體驗的關鍵過程。隨著行動數據流量的急劇增長，在高密度區域中，大量使用者同時連線，造成頻譜資源迅速被瓜分，使每個用戶可獲得的有效頻寬下降。再加上建物造成的訊號遮蔽，以及各類無線訊號彼此干擾，導致訊號品質與覆蓋範圍嚴重受限。因此，辨識和解決弱訊區成為網路優化的重要目標。弱訊區指的是在特定區域內，訊號強度低於可接受標準的區域，可能導致用戶通話中斷、數據傳輸速度緩慢或網路無法連接等問題。透過標記這些弱訊區，電信業者可以進行針對性的改善措施，如增加基站或小型基站的佈建，調整現有基站的發射功功率與天線傾角，或優化頻譜資源的分配。
本研究旨在探討如何建立一個混合型的機器學習模型框架，針對已知訊號地點，運用分群暨分類技術辨識弱訊區；對於缺乏明確訊號資料的地點，則透過迴歸模型預測該地點的訊號指標。分群暨分類模型的結果可為已知訊號的地點生成一張弱訊區的初步分佈地圖；迴歸模型的結果，進一步補全未知地點的訊號指標，並更新地圖。最終，將兩者的結果結合，生成一張完整的弱訊區分佈圖，用於網路優化和天線調整參考。
實驗結果顯示，採用隨機森林和梯度提升決策樹進行分類時，相較於支援向量機和人工神經網路，兩者在各情境下均表現特別突出。在預測訊號指標方面， K最近鄰迴歸、決策樹迴歸與隨機森林迴歸的R2分數均接近0.9甚至以上，而線性迴歸表現相對較差。進一步的散佈圖分析顯示，隨機森林迴歸的數據點分佈更為緊密，且與參考線的吻合度更高，表現出優異的數據模式捕捉能力與良好的泛化能力。這些實驗結果充分證明了本研究所提出的混合型機器學習模型框架在處理弱訊區辨識和訊號預測方面的有效性，對於電信網路之優化具有實質的技術支援。
With surging mobile data traffic in dense areas, identifying and addressing weak-signal regions is crucial for improving telecommunication networks. Such regions, where signal strength falls below standards, can cause dropped calls and slow data rates.
This study proposes a hybrid machine learning framework that incorporates clustering and classification to identify weak-signal areas from known data points, and regression to predict signal values where data are lacking. Integrating these results yields a comprehensive weak-signal distribution map to guide network optimization and antenna adjustments.
Experiments show that Random Forest and Gradient Boosting consistently outperform other classifiers, while K-Nearest Neighbors Regression, Decision Tree Regression, and Random Forest regressors achieve R² scores exceeding 0.9, outperforming Linear Regression. Random Forest Regression further demonstrates superior alignment of predicted values with actual measurements. These findings confirm the framework’s effectiveness in identifying and predicting weak-signal regions, providing valuable support for telecommunication network optimization. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
100971003 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100971003 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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