# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
###### Abstract
Only in the scenarios where enough training data is acquired can the methods have great performance. It's noteworthy that the emerging of Integrated sensing and communication (ISAC) technology provides a new effective solution to beam tracking in high-speed vehicle mobility scenarios. As the typical application of ISAC, dual-function radar communication (DFRC) system uses joint hardware equipment to provide dual functionalities, i.e., radar sensing and communication. Thus, it's possible to use the function of radar sensing to assist beam tracking in communication. For example, a novel transceiver architecture and frame structure for a DFRC system operating in the mmWave band were proposed in [13]. Yet this scheme is not tailored for vehicle mobility scenarios. Further, the authors in [14] extracted significant information from radar signals to perform beam alignment for the mmWave vehicle-to-infrastructure (V2I) scenario. However, this scheme separates the communication system from the radar system, an extra radar device will inevitably result in high hardware costs. In [15], a millimeter-wave joint radar-communications (JRC) system comprising a bi-static automotive radar and vehicle-to-vehicle (V2V) communications was proposed, but the high mobility of the vehicle is not addressed. The authors of [16] proposed a radar-assisted EKF scheme to perform beam tracking by predicting the kinematic parameters of the vehicle in the mmWave band. Furthermore, based on [16; 17] exploited Bayesian framework-based message passing techniques for predicting vehicle mobility parameters, which can reduce computational complexity. However, the above two schemes only model linear mobility scenarios, which are difficult to meet the requirements of complex curvilinear mobility scenarios. In addition, the authors in [18] proposed to explore the use of intelligent reflecting surfaces (IRS) in OFDM-DFRC systems to enhance sensing and communication performance by taking advantage of the passive beamforming gain. To further enhance the tracking precision of DFRC systems under high mobility scenarios, we propose a multi-RSU cooperative mmWave beam tracking algorithm based on radar echo and EKF prediction in this paper. Our scheme not only removes the feedback and pilot signaling overhead in conventional solutions but also improves the prediction accuracy of the kinematic parameters and beam tracking performance by multi-RSU cooperation. Considering the real complex road conditions, we studied both linear and curvilinear mobility scenarios and derived the vehicle mobility model and EKF iteration process for different trajectories. More specifically, we employ a downlink DFRC system, where the DFRC signal completes both sensing and communication tasks. The signal transmitted by the RSU via each beam is partially reflected the transmitter by the body of the vehicle and is also partially received by the vehicle's antenna array. The radar echoes are used for angle, distance and speed prediction after matched filtering and the EKF, while the signal received by the vehicle is used for communication. Each RSU uploads the predicted kinematic parameters to the ES, where the optimal vehicle predicted position is calculated based on the weights. Following this, the ES downlinks the optimal location prediction to each RSU as the input of the EKF's next time slot prediction. The main contributions of our work are summarized as follows:  
* We propose a radar-assisted and multi-RSU cooperation mmWave beam tracking algorithm based on joint sensing and communication for V2I networks. The proposed method does not require downlink pilot and uplink feedback and significantly reduces latency and communication overhead. In contrast with non-cooperative beam tracking, multi-RSU cooperation embodies the advantages of distributed sensing, which can provide higher prediction accuracy and more efficient beam tracking. * We investigate the prediction of the vehicle's position in two mobility scenarios, i.e., linear and curvilinear, respectively.