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.

本文介绍了无线通信中深度学习应用的主动学习算法，通过在无标签数据集中识别关键和信息量最高的样本，仅对这些样本进行标记，从而减少标注开销并保持与传统训练相同的准确性。在深度学习基于毫米波波束选择的案例研究中，我们对多模态数据集进行了不同主动学习算法的性能评估，结果表明，在存在类别不平衡的数据集上，使用主动学习算法可以将标注开销降低 50%。