  For millimeter-wave networks, this paper presents a paradigm shift for
leveraging time-consecutive camera images in handover decision problems. While
making handover decisions, it is important to predict future long-term
performance---e.g., the cumulative sum of time-varying data rates---proactively
to avoid making myopic decisions. However, this study experimentally notices
that a time-variation in the received powers is not necessarily informative for
proactively predicting the rapid degradation of data rates caused by moving
obstacles. To overcome this challenge, this study proposes a proactive
framework wherein handover timings are optimized while obstacle-caused data
rate degradations are predicted before the degradations occur. The key idea is
to expand a state space to involve time consecutive camera images, which
comprises informative features for predicting such data rate degradations. To
overcome the difficulty in handling the large dimensionality of the expanded
state space, we use a deep reinforcement learning for deciding the handover
timings. The evaluations performed based on the experimentally obtained camera
images and received powers demonstrate that the expanded state space
facilitates (i) the prediction of obstacle-caused data rate degradations from
500 ms before the degradations occur and (ii) superior performance to a
handover framework without the state space expansion
