  Due to the short wavelength and large attenuation of millimeter-wave
(mmWave), mmWave BSs are densely distributed and require beamforming with high
directivity. When the user moves out of the coverage of the current BS or is
severely blocked, the mmWave BS must be switched to ensure the communication
quality. In this paper, we proposed a multi-camera view based proactive BS
selection and beam switching that can predict the optimal BS of the user in the
future frame and switch the corresponding beam pair. Specifically, we extract
the features of multi-camera view images and a small part of channel state
information (CSI) in historical frames, and dynamically adjust the weight of
each modality feature. Then we design a multi-task learning module to guide the
network to better understand the main task, thereby enhancing the accuracy and
the robustness of BS selection and beam switching. Using the outputs of all
tasks, a prior knowledge based fine tuning network is designed to further
increase the BS switching accuracy. After the optimal BS is obtained, a beam
pair switching network is proposed to directly predict the optimal beam pair of
the corresponding BS. Simulation results in an outdoor intersection environment
show the superior performance of our proposed solution under several metrics
such as predicting accuracy, achievable rate, harmonic mean of precision and
recall.
