Sensing-Assisted Predictive Beamforming with NLoS Identification

Yongkang Zhao1; Xiaoli Xu1; Yong Zeng1; Fan Liu2

10.1109/ICC45041.2023.10278781

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

6455-6460

28 May-1 June 2023

Rome, Italy

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

IEEE

Sensing-assisted predictive beamforming is a promising technique for reducing the communication overhead and latency in millimeter wave (mmWave) communication systems. In this paper, we propose a robust sensing-assisted predictive beamforming scheme that performs non-line-of-sight (NLoS) identification based on the reflected joint communication and sensing signal. Specifically, the time delay, Doppler shift and reflection coefficient are estimated from the echo signal, based on which a hypothesis test problem is formulated to determine whether the echo signal is reflected by the target vehicle or obstacles. Besides, based on the estimated channel parameters, extended Kalman filtering (EKF) is employed to estimate and predict the motion parameters of the target vehicle. If the echo signal is reflected by obstacles, it implies that the LoS link between the base station (BS) and the target vehicle is blocked. In this case, the proposed scheme can adjust the communication mode and vehicle tracking mechanism accordingly to enhance link reliability. Numerical results demonstrate that the proposed predictive beamforming scheme with NLoS identification can substantially enhance the achievable communication rate as compared to the existing techniques without taking blockage into account.

Target tracking Array signal processing Vehicle-to-infrastructure Simulation Millimeter wave technology Sensors Reflection coefficient

其他

英语

EI ; CPCI-S

National Key R&D Program of China[2019YFB1803400]

Telecommunications

Telecommunications

WOS:001094862606095

20234815114223

Extended Kalman filters ; Millimeter waves ; Vehicle to vehicle communications ; Vehicles

Electromagnetic Waves:711 ; Electromagnetic Waves in Relation to Various Structures:711.2 ; 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

Yongkang Zhao,Xiaoli Xu,Yong Zeng,et al. Sensing-Assisted Predictive Beamforming with NLoS Identification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:6455-6460.