# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
###### Abstract  
In the future vehicular networks with an increased number of transceiver antennas and higher vehicle speeds, more frequent beam switching is required to ensure the quality of communication, which poses challenges to beam tracking speed and resource efficiency. Integrated sensing and communication (ISAC) provide a new solution to cope with this problem since radar echo can help to predict the vehicle's future location and beam direction. Therefore, we present a radar-assisted beam tracking algorithm based on Extended Kalman filtering (EKF) and multi-road side unit (RSU) cooperation in this article. Each RSU uses EKF and radar echo to predict and track the vehicle position and upload the prediction information to the edge server (ES). By deploying multiple RSUs, the ES uses the uploaded distributed sensing information for joint estimation and thus improves the accuracy of vehicle location prediction, which is used for the beam tracking task at the next moment. Considering the real complex road conditions, we investigate two scenarios where vehicles move linearly or curvilinearly. Simulation results show that the proposed method with multiple base station cooperation improves the spectral efficiency by 34% and 20% over non-cooperative beam tracking in linear and curvilinear mobility, respectively. In addition, compared with traditional beam tracking based on beam scanning and signaling feedback, radar-assisted beam tracking significantly reduces the communication overhead. integrated sensing and communication (ISAC); multi-road side unit (RSU) cooperation; curvilinear mobility; beamforming; Extended Kalman filtering (EKF); distributed sensing +
Footnote †: journal: _electronics_  
overhead of beam tracking and perform efficient beam switching has become a significant challenge in the high mobility VEHICLE-TO-EVERYTHING (V2X) scenario. Conventional solutions for beam tracking can be divided into two types. The first type is to reduce CSI dimensionality by tracking some parameters of the channel. As the direction of the optimal beam is closely related to the angle of the physical channel, many works optimize beam switching by tracking the angular parameter of the channel, i.e., Angle of Arrival (AOA) or Angle of Departure (AOD). In [3], an EKF-based AOA/AOD tracking algorithm was proposed. In [4], the authors investigated the problem of beam tracking for UAV scenarios and proposed to predict the AOA and AOD in a 3D channel based on an Unscented Kalman Filter (UKF). The authors in [5; 6] proposed to transform the spatial domain CSI into the angular domain CSI by DFT and use linear Kalman filtering (LKF) to track the angular domain CSI in a high-speed railway scenario and then complete beam switching. In [7], an EKF-based high-speed railway terminal location prediction algorithm was proposed. Pioneered by [7], authors in [8] proposed a novel nonlinear state transfer model, in which the position, speed, forward direction and dynamically correlated angular velocity variance of the train are jointly considered to further improve the position prediction and beam switching accuracy. However, the above methods still require some signaling feedback and interaction at each moment, as well as the scenario is relatively simple and idealized. The second method for beam tracking uses neural networks to predict CSI. In [9], the authors proposed to predict narrowband channel CSI using Full Connected Recurrent Neural Network (FCRNN). In [10; 11], AOA/AOD, channel parameters, power, and distance are used to construct a beamforming fingerprint database for beam switching based on deep learning techniques. Some work focused on the design of neural networks suitable for communication. For example, a learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for predicting CSI was designed in [12]. However, the performance of the second method is significantly affected by the source of training data and the structure of neural networks.