Robust DoA Estimation in a Uniform Circular Array Antenna With Errors and Unknown Parameters Using Deep Learning

With the considerable rise in the number of devices connecting to the Internet, the demand for an extensive capacity communication networks is already apparent. Beam Division Multiple Access (BDMA) is a communication system method aimed at increasing capacity by simultaneously adapting portions of the antenna beam to the locations of multiple Mobile Stations (MSs) achieve enabling more efficient and effective data transmission. However, to assess the best performance, the Base Station (BS) must calculate the exact location of the MSs. In this research, we propose the Direction of Arrival (DoA) approach for locating MSs, where to determine the DoA, an array of antennas is utilized with two common configurations, Uniform Linear Array (ULA) and Uniform Circular Array (UCA). We suggest UCA because the main advantage of the circular array is the 360-degree coverage. Lately, due to the growth of Neural Networks (NNs), many researchers have studied the estimation of DoA using NNs. However, these studies are not widely accepted in practice due to the effect of series errors such as mutual coupling, gain, phase and element position errors and unknown parameters such as the number of input signals and signal level difference. This paper proposes a Deep Neural Network (DNN) framework for estimating the DoA in UCAs. Transfer Learning and Multi-Task techniques eliminate the need to count the number of input signals, reduce errors and bottleneck of NNs make the approach less sensitive than previous techniques. This paper improved the estimation by 75% compared with UCA-MUSIC, 73% compared with the method in (Basikolo et al., 2016), 92% compared with the method in (Hu, 2019), and 90% compared with the method in (Wang et al., 2015) when all the unknown parameters in the system are affected.