Unsupervised Learning-Based Coverage Enhancement for RIS-Aided UAV Communication

Unmanned aerial vehicles (UAVs) in integration with reconfigurable intelligent surfaces (RIS) play a crucial role in improving wireless communication coverage and enhancing overall performance. However, optimizing the beamforming for the RIS and base station (BS) is critical in improving coverage and ensuring connectivity in densely populated areas. The primary challenge in this process arises from the diverse Quality of Service (QoS) requirements set by user equipment (UEs). To address this complexity, machine learning algorithms are employed to predict the optimal beamforming configurations for both the BS and RIS. However, the traditional supervised learning methods are becoming less effective due to the ever-changing demands of UEs, as these methods rely on fixed data patterns that struggle to adapt to the fluctuating QoS requirements of UEs. Thus, in this paper, we propose an unsupervised learning-based deep learning (DL) approach to jointly predict the optimal beamforming matrix for RIS and BS, enhancing communication coverage and maximizing QoS satisfaction of UEs. The proposed DL-based beamforming adaptively predicts the beamforming matrix, facilitates efficient data exploration during the initial learning phase, and seamlessly scales as the process advances, thereby enhancing overall performance. Numerical results demonstrate that the proposed DL-based RIS and BS beamforming outperforms by up to 89%, compared to the state-of-the-art methods.