  Collecting an over-the-air wireless communications training dataset for deep
learning-based communication tasks is relatively simple. However, labeling the
dataset requires expert involvement and domain knowledge, may involve private
intellectual properties, and is often computationally and financially
expensive. Active learning is an emerging area of research in machine learning
that aims to reduce the labeling overhead without accuracy degradation. Active
learning algorithms identify the most critical and informative samples in an
unlabeled dataset and label only those samples, instead of the complete set. In
this paper, we introduce active learning for deep learning applications in
wireless communications, and present its different categories. We present a
case study of deep learning-based mmWave beam selection, where labeling is
performed by a compute-intensive algorithm based on exhaustive search. We
evaluate the performance of different active learning algorithms on a publicly
available multi-modal dataset with different modalities including image and
LiDAR. Our results show that using an active learning algorithm for
class-imbalanced datasets can reduce labeling overhead by up to 50% for this
dataset while maintaining the same accuracy as classical training.
