Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.
|EI Accession Number|
Beamforming ; Kalman Filters ; MIMO Systems ; Target Tracking ; Vehicle To Vehicle Communications
|ESI Classification Code|
Electromagnetic Waves In Relation To Various Structures:711.2 ; Radio Systems And Equipment:716.3
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Electrical and Electronic Engineering|
1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
3.Department of Electronic and Electrical Engineering, University College London, London, U.K
4.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
5.ZTE Corporation, Shenzhen, China
6.Electrical Engineering and Computer Science Department, Technische Universität Berlin, Berlin, Germany
|Corresponding Author Affilication||Department of Electrical and Electronic Engineering|
Du，Zhen,Liu，Fan,Yuan，Weijie,et al. Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2022,PP(99):1-1.
Du，Zhen.,Liu，Fan.,Yuan，Weijie.,Masouros，Christos.,Zhang，Zenghui.,...&Caire，Giuseppe.(2022).Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,PP(99),1-1.
Du，Zhen,et al."Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS PP.99(2022):1-1.
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