Abstract
This thesis presents a comprehensive study on angle of arrival (AoA) estimation techniques for spectrum sharing in next-generation (NextG) wireless communication systems. The investigation encompasses signal processing methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), as well as machine learning (ML) techniques such as artificial neural networks (ANNs) and convolutional neural networks (CNNs). These techniques are evaluated using both synthetic and over-the-air (OTA) test scenarios, with a focus on the impact of multipath fading and varying signal-to-noise-ratio (SNR) conditions. The study investigates the role of ML algorithms in enhancing the performance of wireless communication systems under challenging conditions. The robustness of ML techniques in real-world scenarios, including their abilities to maintain stability under multipath fading, is highlighted. The thesis proposes a new ML algorithm aimed at optimizing the performance of providing positional awareness about radio frequency (RF) signals. Furthermore, the finding with testing with OTA tests, reveal that while the MUSIC algorithm excels in synthetic scenarios with high-resolution capabilities beyond the antenna array’s resolution, it suffers from performance degradation in non-line-of-sight (NLOS) conditions and low SNR environments. On the other hand, ML algorithms demonstrate robustness and stability across varying conditions, though they exhibit a slightly higher error rate in simulations.