Design and Implementation of Maximum Likelihood Algorithm Considering Multi-User Parallelization for B5G Direction-of-Arrival Estimation in OpenAirInterface

As B5G (beyond 5G) communication techniques continue to advance rapidly, efficient and precise identification of input signals has become essential for maintaining stability in data transmission across wireless and satellite communication systems. Direction-of-arrival (DOA) estimation plays a vital role in B5G communications, especially when it comes to beamforming. In this study, we implemented the maximum likelihood (ML) algorithm for B5G DOA estimation in OpenAirInterface (OAI). In terms of hardware, we implemented it on an Intel i9-9960X CPU. During simulation testing, we estimate the performance of the ML algorithm with different numbers of antennas and compare the accuracy and time. In addition, to improve the estimation performance of the ML algorithm and reduce the time spent on DOA estimation, we further expand the single-user architecture to a multi-user parallelization architecture using multi-threading. In the multi-user parallelization architecture, we use four threads to perform four DOA estimates simultaneously. Finally, experimental results show that when the SNR is greater than -10dB, the accuracy of 16, 32, and 64 antennas can reach within 0.1 degrees. The DOA estimation accuracy is best when the number of antennas is 64 at 10dB, and the mean square error (MSE) reaches 10-6. When the number of antennas M = 64, the average execution time of the multi-user architecture is 2485.89 ms, which is only about 1.33 times the execution time of 1875.85 ms for a single user. However, it can complete four estimation tasks simultaneously, effectively improving inference throughput.