Adaptive Direction of Arrival Estimation Algorithms for 5G Network and Beyond

Abstract

Multiuser-multiple input multiple output (MU-MIMO) systems can provide a substantial performance for cellular networks by applying direction of arrival (DOA) technique, in which the direction of sidelobe signals can be estimated. Therefore, this paper presents a different adaptive DOA algorithms where the performance of communication networks can be enhanced, in the meantime the minimizing interferences. Meanwhile, The DOA algorithm such as multi signal classification (MUSIC), minimum norm (Min-Norm), minimum variance distortionless response (MVDR) and Bartlett algorithms are studied and compared extensively regarding the estimation angles and peak power spectrum. In this regard, uniform linear array (ULA) antenna is employed to construct the weight vector for incoming sensor signals. Furthermore, this paper compares the DOA algorithms concerning the number of antenna elements as well as the incoming signal angles. The simulation results approve that the algorithms are likely have the same performance in accuracy of scanning angles. However, it is realized that all algorithms have different peak power values in order to beamwidth. Moreover, the results indicate that the DOA algorithms with the different number of antenna elements have a significant impact on a directivity and gain. Hereafter, this work can be extended to higher frequency of mmWave communication with massive number of antenna, which can meet the requirement of fifth generation (5G) network.