# Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition

arXiv: https://arxiv.org/abs/2111.03258 · HTML: https://mlsanity.com/paper/2111.03258/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [A Learnable Distortion Correction Module for Modulation Recognition](https://mlsanity.com/paper/1803.01319/) (2018)

## Built on by (most cited first, 5 of 5)

- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Modulation and signal class labelling using active learning and classification using machine learning](https://mlsanity.com/paper/2202.12930/) (2022)
- [MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis](https://mlsanity.com/paper/2408.02714/) (2024)
- [Bit-Accurate FPGA Evaluation of Learned Feature Gating in a Fixed-Point Fourier-Feature Automatic Modulation Classifier](https://mlsanity.com/paper/2607.24568/) (2026)
- [Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection](https://mlsanity.com/paper/2609.21334/) (2026)

## Abstract (arXiv)

Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation mode recognition, this paper proposes a time-frequency attention mechanism for a convolutional neural network (CNN)-based modulation recognition framework. The proposed time-frequency attention module is designed to learn which channel, frequency and time information is more meaningful in CNN for modulation recognition. We analyze the effectiveness of the proposed time-frequency attention mechanism and compare the proposed method with two existing learning-based methods. Experiments on an open-source modulation recognition dataset show that the recognition performance of the proposed framework is better than those of the framework without time-frequency attention and existing learning-based methods.