Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911742876975104 |
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| author | Lee, Byunghyun Marcum, Andrew Love, David Krogmeier, James |
| author_facet | Lee, Byunghyun Marcum, Andrew Love, David Krogmeier, James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_04916 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications Lee, Byunghyun Marcum, Andrew Love, David Krogmeier, James Signal Processing 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. |
| title | Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2309.04916 |