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

Massive multiple-input multiple-output (MIMO) is a promising technology that can mitigate interference effectively in cellular-connected unmanned aerial vehicle (UAV) communications. In this letter, we propose a fusion of wireless and sensor data to enhance beam alignment for cellular-connected UAV...

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Bibliographic Details
Published inIEEE wireless communications letters Vol. 13; no. 3; pp. 869 - 873
Main Authors Lee, Byunghyun, Marcum, Andrew C., Love, David J., Krogmeier, James V.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Array signal processing
Autonomous aerial vehicles
Base stations
Beamforming
Extended Kalman filter
Frame structures
information fusion
integrated sensing and communication (ISAC)
Kalman filtering
Massive MIMO
MIMO communication
multi-input multi-output (MIMO)
Symbols
Synchronization
Tracking
Unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
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Summary:Massive multiple-input multiple-output (MIMO) is a promising technology that can mitigate interference effectively in cellular-connected unmanned aerial vehicle (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. Simulation results demonstrate that the proposed scheme can improve position/orientation estimation accuracy significantly, leading to higher spectral efficiency.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3348794