窄頻物聯網是由第三代合作夥伴計劃所制定，並用於大規模連接的新蜂窩技術，在增強式覆蓋區域中支持大量使用者裝置的技術。窄頻物聯網與現有的行動網路具有共通性，下行通道傳輸採用正交頻域多工技術，而子載波間隔與長期演進系統相同。此外，窄頻物聯網僅分配180KHz的頻寬與長期演進系統中的一個物理資源塊的大小相同。當基地台和使用者裝置連接時，細胞搜尋是第一個程序。通常細胞搜尋過程包括同步和細胞身分辨識。本文根據同步序列的性質，提出了新的低複雜度細胞偵測演算法，主要是利用同步序列的性質來減少互相關值計算的數量，將2016個同步序列分為1008個群組，並在接收訊號和代表序列之間挑選最大的互相關值的群組。再接著從群組中挑選互相關值最大的序列，以獲得所需的細胞身分代碼和訊框位置。在此論文中，我們根據同步訊號組成序列的特性，提出了多個不同的分群方法，並分析相關值特性與運算複雜度。模擬結果顯示，本文所提出的第三個分群演算法因為充分利用同步序列的結構性，大大降低73％的計算複雜度。具有低複雜度的細胞搜尋演算法完全符合窄頻物聯網低成本裝置的需求。

Narrowband internet of things (NB-IoT) is a new cellular technology which was proposed to support a massive number of user equipments (UEs) operated in an enhanced coverage area. NB-IoT downlink transmission employs orthogonal frequency division multiplexing (OFDM) technique with subcarrier spacing equal to that of long term evolution (LTE) system.

Furthermore, NB-IoT only allocates 180KHz bandwidth which is the same as the size of one physical resource block (PRB) in LTE. When the enhanced NodeB (eNB) and the UE are connected, the initial carrier frequency offset (CFO) may occur because of the mismatch of oscillators between transmitter and receiver. As a result, the synchronization is a critical procedure.

Generally, the cell search process includes synchronization and cell identification (ID). This thesis proposes a new low-complexity algorithm for cell search according to the property of the synchronization sequences. A two-stage grouping algorithm is presented to divide all the synchronization sequences into groups by utilizing the construction of synchronization sequences. The number of searching operations are hence reduced. Therefore, the computational complexity is decreased. In this thesis, the main contribution is to reduce the number of cross-correlation calculations by utilizing the property of the synchronization sequences. This thesis presents a new low-complexity algorithm of cell ID detection by dividing 2016 sequences into 1008 groups. We first select certain number of groups with large cross-correlations between the received signal and the group representative sequences. Then, the cross-correlations of the received signal and the constituent sequences in the selected groups are performed to obtain the desired cell ID and frame number.

In this thesis, we propose two new grouping methods according to the sequence properties. The correlation property and the computational complexity are both analyzed. The simulation results show that the proposed grouping method III can achieve 73 percent reduction with acceptable performance loss.

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