# Learning from the Best: Active Learning for Wireless Communications

arXiv: https://arxiv.org/abs/2402.04896 · HTML: https://mlsanity.com/paper/2402.04896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [GALAXY: Graph-based Active Learning at the Extreme](https://mlsanity.com/paper/2202.01402/) (2022)
- [Dropout-based Active Learning for Regression](https://mlsanity.com/paper/1806.09856/) (2018)
- [Active Learning for Wireless IoT Intrusion Detection](https://mlsanity.com/paper/1808.01412/) (2018)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [Neural Network-based OFDM Receiver for Resource Constrained IoT Devices](https://mlsanity.com/paper/2205.06159/) (2022)
- [PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization](https://mlsanity.com/paper/2112.10885/) (2021)

## Built on by (most cited first, 4 of 4)

- [AHA: Human-Assisted Out-of-Distribution Generalization and Detection](https://mlsanity.com/paper/2410.08000/) (2024)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [Specific Emitter Identification via Active Learning](https://mlsanity.com/paper/2601.04502/) (2026)
- [LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models](https://mlsanity.com/paper/2604.17770/) (2026)

## Abstract (arXiv)

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.