在毫米波通訊中，準確和快速的波束對準(beam alignment)是必要的。而數據驅動(data-driven)的學習方法被認為是一個很有潛力的解決方案，可以用來解決波束對準的問題，此方法利用了補助訊息(side information)和現場量測(on the field measurement)。在本論文中我們提出基於支持向量機(support vector machine)的學習方法，當給定一小部份用戶位置的波束方向時，此方法利用用戶的位置補助資訊來預測其餘用戶位置可能的波束方向。我們使用 DeepMIMO 數據庫提供模擬用的通道參數。實驗結果顯示我們提出的方法和基於神經網路(neural network)的學習方法在能量損失機率上有差不多的錯誤率，不過在運行時間方面，我們所提出的方法完全勝過基於神經網路的學習方法。

Accurate and fast beam alignment is important in millimeter wave communications. A data-driven learning method is a promising solution to solve this problem by leveraging side information and on the field measurement. In this thesis, a learning method based on support vector machine is proposed to predict the beam direction on a set of possible users’ position, given beam direction measurements on a small subset of users’ position; based on these predictions, a small subset of beams is recommended, based on position side information. The proposed method is evaluated with DeepMIMO dataset, which provides parameterized channels for the experiment. The simulation result show that our method outperform the learning method based on neural network in computation complexity and also has same power loss probability.

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