為了滿足對高數據速率傳輸的高度需求，毫米波通訊成為了第五代行動通訊系統受矚目的無線通信技術之一。毫米波採用大型天線陣列和波束成形技術彌補高頻電磁波嚴重的路徑損耗，然而行動通訊終端的移動與高度指向性的波束成型技術對波束對齊形成巨大挑戰，使接收訊號能量出現顯著下降，也因此延伸出波束對準和跟踪的議題。現有的一些方法將波束對準問題轉換為線性估計問題，透過對所有可能的波束進行掃描並利用卡爾曼濾波器找出具有最大能量的主波束對，作為後續資料傳輸使用之收發波束，然而此類方法需要全域的掃瞄，造成回傳開銷的負擔。相較於現行的低頻頻段，毫米波在角域上更具稀疏性，也因此有些方法利用壓縮感知技術來尋找主波束對，但此類方法忽略了訊號之間的相關性，缺乏對過去觀察結果的利用。在本篇論文中，將波束對準和跟踪問題表述為具有多個測量向量的稀疏信號恢復問題。並透過結合卡爾曼濾波器與壓縮感知技術，達到降低回傳開銷並且利用稀疏信號的時間與空間相關性提高估計效能。本論文所提出的方法使用了自適應的傳感矩陣與加權壓縮感知來提高估計性能，並且根據通道狀況來動態調整回傳開銷以平衡估計準確性和反饋開銷負擔。最後利用模擬結果評估所提出方法的性能並與現有方法進行比較。

Millimeter wave (mmWave) is a promising technique to fulfill the ever-growing demand for high data rate transmission. To overcome severe path loss in the high-frequency band, beam alignment and tracking are crucial for mmWave communications that employ large antenna array and highly directional beamforming. As far as mobility is concerned, beam alignment and tracking become more challenging due to the fast-varying channels. In contrast to lower frequencies, there are only a few dominant paths in mmWave channels, thus it is generally sufficient to estimate the path gains, angle of arrival, and angle of departure of those paths. Some existing work formulates the beam tracking problem into a linear estimation framework which could be solved by Kalman filter-based approaches with a full scan over all possible beams. Alternatively, compressive sensing (CS) approaches utilize the sparsity in mmWave channels to reconstruct the channel by a limited number of pilot signals, which reduce the feedback overhead burden. However, the aforementioned methods either require a lengthy feedback process or lack of exploiting the past observations, leading to high feedback overhead and large estimation errors. In this work, the beam alignment and tracking problems are formulated as a sparse signal recovery problem with multiple measurement vectors. Since the CS performance has a strong dependence on sparsity level, the proposed method increases the signal sparsity level by applying adaptive CS on the Kalman filter observation residual computed by the previous estimate of the support to estimate the angles of the dominant paths, while the corresponding path gains are tracked by the reduced-order Kalman filter. To further exploit both the spatial and time correlation of sparse signals, our approach adapts of the sensing matrix to the previous estimation to enhance the estimation accuracy, and the weighted CS is also adopted for shrinking the possible range of solutions, where the weighting is designed based on the previously estimated angles and the spatial angle variation model. To balance the estimation accuracy and feedback overhead, a trigger mechanism is proposed that determines the timing for switching the estimation policy between training and tracking stages. Simulations are performed to evaluate the performance of the proposed method subject to numerous important factors, such as signal-to-noise ratio (SNR), antenna array size, feedback overhead size, user mobility, and the number of propagation paths.

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