Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications

TL;DR

This paper introduces a fusion of wireless and sensor data using an extended Kalman filter to improve beam alignment and spectral efficiency in UAV-assisted massive MIMO communications.

Contribution

It presents a novel predictive beamforming framework that integrates channel and sensor data for better UAV position and orientation estimation.

Findings

Enhanced position and orientation estimation accuracy.

Improved spectral efficiency in UAV massive MIMO systems.

Effective fusion of wireless and sensor data using EKF.

Abstract

Cellular-connected unmanned aerial vehicles (UAVs) represent a promising technology for extending the coverage of 5G and 6G networks in a cost-effective manner. Additionally, Massive multiple-input multiple-output (MIMO) serves as an effective solution to interference mitigation in cellular-connected UAV communications. In this letter, we propose a fusion of wireless and sensor data to enhance beam alignment for cellular-connected UAV massive MIMO communications. We develop a predictive beamforming framework, including the frame structure and predictive beamformer. Moreover, we employ an extended Kalman filter (EKF) to integrate channel and sensor data and provide the corresponding state-space and observation models. Simulation results demonstrate that the proposed scheme can improve position/orientation estimation accuracy significantly, leading to higher spectral efficiency.