CKM-Assisted LoS Identification and Predictive Beamforming for Cellular-Connected UAV

Shiqi Zeng1; Xiaoli Xu1; Yong Zeng1; Fan Liu2

10.1109/ICC45041.2023.10278702

2023-05-28

IEEE International Conference on Communications (IEEE ICC)

1938-1883

978-1-5386-7463-5

ICC 2023 - IEEE International Conference on Communications

2023-May

2877-2882

28 May-1 June 2023

Rome, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by proposing a channel knowledge map (CKM)-assisted predictive beamforming approach based on the echoed joint communication and sensing signal, whereby the line-of-sight (LoS) link identification is performed via hypothesis testing using prior information provided by CKM. Depending on the identification result, extended Kalman filtering (EKF) is adopted to reliably track the target UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the target UAV will be immediately connected to a candidate base station (BS), namely a handover will be triggered to alleviate the communication outage. The simulation results show that the proposed method can significantly enhance the UAV tracking and mmWave communication performance compared to the benchmarking schemes without using CKM or LoS identification.

Target tracking Array signal processing Simulation Millimeter wave technology Line-of-sight propagation Autonomous aerial vehicles Sensors

其他

英语

EI ; CPCI-S

Natural Science Foundation of China[62071114]

Telecommunications

Telecommunications

WOS:001094862602162

20234815114815

Aircraft detection ; Antennas ; Benchmarking ; Extended Kalman filters ; Millimeter waves ; Unmanned aerial vehicles (UAV) ; Vehicle to vehicle communications

Aircraft, General:652.1 ; Electromagnetic Waves:711 ; Electromagnetic Waves in Relation to Various Structures:711.2 ; Radar Systems and Equipment:716.2 ; Radio Systems and Equipment:716.3

IEEE

1.School of Information Science and Engineering, Southeast University, Nanjing, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Shiqi Zeng,Xiaoli Xu,Yong Zeng,et al. CKM-Assisted LoS Identification and Predictive Beamforming for Cellular-Connected UAV[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:2877-2882.