到達角方向(Direction of Arrival, DOA)的估測在5G通訊多輸入多輸出(Multiple-Input Multiple-Output, MIMO)的架構上扮演著重要的角色。因此，如何精準估測DOA並優化DOA估測之運算已廣受到許多學者的重視。首先，本研究在5G基地臺實體層使用Multiple Signal Classification (MUSIC)和Minimum Variance Distortionless Response (MVDR)兩種演算法來估測DOA。更進一步，我們在Intel i9-9960X CPU上，使用AVX-512指令集，以優化DOA估測中的矩陣乘法運算。AVX-512的引入，使得系統每次能處理16筆浮點數據，有效地提高了複數數據處理的效率。通過多執行緒平行處理的技術，使得每個CPU核心獨立進行AVX-512運算。之後，我們將此方法整合至5G規格下的OpenAirInterface (OAI)開源軟體實驗平台。並且，比較不同天線數量4、8、16、32和64的各種情況下，AVX-512的運算時間的優化程度。此外，我們透過頻譜分析圖來比較MUSIC和MVDR兩種演算法對於訊號的估測能力。最後將MUSIC、MVDR兩者擴展成4個使用者數量的情境，比較多使用者架構下與單使者的運算差異。實驗結果證明了：本研究所提出的方法，可以提升在5G基地臺實體層對到達角方向估測之效能，減少到達角方向估測時間。特別是針對處理大型矩陣的運算需求，本方法可以提高運算的靈活性和效率。然而，並非所有情況都適用AVX-512指令集做加速。根據資料量的大小，來選擇適當的運算方式，對於最大化運算效率至關重要，這可以被考慮為未來研究方向之一。

The Direction-of-Arrival (DOA) estimation of plays an important role in the Multiple-Input Multiple-Output (MIMO) architecture of the 5th Generation Mobile Networks (5G) communications. Therefore, how to accurately estimate DOA and optimize the operation of DOA estimation has been widely valued by many scholars. First, this study uses two algorithms: 1) MUltiple SIgnal Classification (MUSIC) and 2) Minimum Variance Distortionless Response (MVDR), to estimate DOA in the physical layer of the 5G gNodeB. Furthermore, we use the AVX-512 instruction set on the Intel i9-9960X CPU to optimize the matrix multiplication operation in DOA estimation. The usage of AVX-512 enables the system to process 16 floating point data at a time, effectively improving the efficiency of data processing of the complex values. Through the multi-thread parallel processing technique, each Central Processing Unit (CPU) core independently performs AVX-512 operations. Afterwards, we integrated this method into the OpenAirInterface (OAI) open-source software experimental platform under 5G specifications. Moreover, we compare the degree of optimization of the computing time of AVX-512 under various conditions with different numbers of antennas: 4, 8, 16, 32, and 64. In addition, we use spectrum analysis charts to compare the signal estimation capabilities of the two algorithms, MUSIC and MVDR. Finally, MUSIC and MVDR are expanded to a scenario with four users, and the computing differences between multi-user architecture and single user are compared. The experimental results prove that the method proposed in this study can improve the performance of DOA estimation in the physical layer of 5G gNodeB and reduce the time of DOA estimation. Especially for the operation requirements of processing large matrices, this method can improve the flexibility and efficiency of operations. However, not all situations are suitable for AVX-512 instruction set acceleration. Selecting an appropriate computing method based on the amount of data is crucial to maximizing computing efficiency, and this can be considered as one of the future research directions.

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