Adaptive Direction of Arrival Estimation and Beamforming Algorithms for Future Networks and Advancements

Multiuser Multiple Input Multiple Output (MU-MIMO) wireless communication systems significantly enhance mobile cellular networks by employing Direction of Arrival (DOA) algorithms and beamforming (BF) techniques. These technologies enable the prediction of sidelobe signal directions, allowing for redirection toward specific desired targets. This research presents various adaptive DOA algorithms designed to improve wireless network performance. The study thoroughly examines DOA algorithms, including Multi-Signal Classification (MUSIC), Minimum Norm (Min-Norm), Minimum Variance Distortionless Response (MVDR), and Bartlett algorithms, comparing them based on angle estimation and peak power spectrum. The paper also discusses the concept of separating incoming signals into the Signals of Interest (SOI) and Signals Not of Interest (SNOI). Additionally, several BF algorithms, such as Least Mean Square (LMS), Recursive Least Square (RLS), and Constant Modulus Algorithm (CMA), are explored and evaluated. The research assesses the impact of BF algorithms on Bit Error Rate (BER) with varying numbers of antenna elements at the base station (BS). Simulation results indicate that while the algorithms demonstrate similar capabilities for interference cancellation, they differ in peak power values and beam widths. Furthermore, the accuracy of angle scanning and interference reveals that the algorithms have unique effects on directivity and gain, with the RLS algorithm showing slight improvements over the SMI and CMA algorithms. The findings highlight that the selection of beamforming algorithms, along with the number of antenna elements, significantly affects BER performance.