  For reliable millimeter-wave (mmWave) networks, this paper proposes
cooperative sensing with multi-camera operation in an image-to-decision
proactive handover framework that directly maps images to a handover decision.
In the framework, camera images are utilized to allow for the prediction of
blockage effects in a mmWave link, whereby a network controller triggers a
handover in a proactive fashion. Furthermore, direct mapping allows for the
scalability of the number of pedestrians. This paper experimentally
investigates the feasibility of adopting cooperative sensing with multiple
cameras that can compensate for one another's blind spots. The optimal mapping
is learned via deep reinforcement learning to resolve the high dimensionality
of images from multiple cameras. An evaluation based on experimentally obtained
images and received powers verifies that a mapping that enhances channel
capacity can be learned in a multi-camera operation. The results indicate that
our proposed framework with multi-camera operation outperforms a conventional
framework with single-camera operation in terms of the average capacity.
