Active learning for efficient data selection in radio-signal-based positioning via deep learning

The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised-learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data-collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. Therefore, an active learning approach for efficient data collection is proposed. It is first shown that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. Then, a practical method is proposed to approximate this genie.