## 1 Introduction
Beamsteering only in the horizontal plane can be reduced to sectorization of the service area of the V2I base station (infrastructure transceiver) and the optimal service sector selection. In the well-known beamsteering algorithm of the 802.11 ad [16] standard, the sector is selected based on the highest SNR at the subscriber's reception. Such an approach requires several iterations of bidirectional frame transmission to evaluate the channel quality (channel state information, CSI); in practice, this leads to frequent sector changes, which reduces the link startup speed. In this regard, a significant part of the current research concerns reducing the time to establish a connection, which is equivalent to reducing delays. Such a decrease can be achieved by positioning the vehicle based on information from its request message for connection to the network, which was demonstrated in [17]. In this research, the 802.11 ad beamsteering technique was updated in the context of the V2I system by relying on vehicle location data. The technique reduces the number of iterations for establishing a link but for sectors relatively close to the infrastructure object. The scalability could be increased by storing load statistics of sectors, that is, by introducing a priori information about the V2I system. In [18], the vehicle's initial position data within the 802.11 ad standard was expressed in terms of GPS coordinates and speed. The drawbacks of the algorithm were the lack of accounting for positioning errors, the assumption of constant vehicle speed, and a rectilinear path. The insufficient number of visible satellites and multipath effects often limit the positioning accuracy of GPS trackers [19; 20]. Hence, the application of an additional positioning system that is superior in accuracy to GNSS is essential for meeting a critical safety requirement in intelligent transportation systems. In this regard, dedicated short-range communications (DSRC) technology, in addition to GPS coordinates, uses MAP (specific for the DSRC message type) data representing statistical information on the physical geometry of the road section at the transceiver location [21]. The problem of the lack of the line-of-sight condition for GPS satellites when positioning the vehicle was taken into account in [22], where time of arrival (ToA) served as the location update information. When processing this information in conjunction with a video stream from road cameras, the algorithm has demonstrated an increase in positioning accuracy. However, the universality of the proposed approach is limited by the availability of video recording for all sections of the path, as well as by the features of the development, because multipath propagation needs to be taken into account when calculating ToA. To date, algorithms based on machine learning (ML) have shown high levels of performance in solving tasks involving increasing positioning accuracy [23; 24; 25]. An ML algorithm, which controls the PAA, compares the received data from the vehicle request message with the required tilt angle of the radiation pattern (RP) main lobe, which should significantly reduce connection delays. The study [26] applied an intelligent algorithm that utilized vehicle frame requests in the 2.4/5 GHz band and estimated the direction of message transmission. A significant limitation of the proposed technique's accuracy was the effect of the multipath propagation, primarily due to the peculiarity of the predictors' choices. In [27], an offline deep reinforcement learning (offline-DRL) method with GPS coordinates and multipath pattern as input parameters was used for radio beam control and provided a probability of 3 dB power loss at the receiver input equal to \(10^{-3}\). However, the research did not consider the required delays for the ML algorithm, which are critical for V2X application and should be at most tens of milliseconds [28], which. in the case of DRL, inevitably affects the accuracy.