  In this paper, we propose an environment sensing-aided beam prediction model
for smart factory that can be transferred from given environments to a new
environment. In particular, we first design a pre-training model that predicts
the optimal beam by sensing the present environmental information. When
encountering a new environment, it generally requires collecting a large amount
of new training data to retrain the model, whose cost severely impedes the
application of the designed pre-training model. Therefore, we next design a
transfer learning strategy that fine-tunes the pre-trained model by limited
labeled data of the new environment. Simulation results show that when the
pre-trained model is fine-tuned by 30\% of labeled data from the new
environment, the Top-10 beam prediction accuracy reaches 94\%. Moreover,
compared with the way to completely re-training the prediction model, the
amount of training data and the time cost of the proposed transfer learning
strategy reduce 70\% and 75\% respectively.
