Wireless Signal Modulation Classification
Wireless Signal Modulation Classification is a research topic within Artificial Intelligence. Science Explorer counts 8.2k research works in it since 1951. 20.2% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of deep learning techniques for wireless signal classification, including modulation classification, channel estimation, RF fingerprinting, and spectrum monitoring in cognitive radios. The papers explore the opportunities and challenges of using deep learning in wireless communications and physical layer signal processing.
- Deep Learning
- Wireless Communications
- Modulation Classification
- Channel Estimation
- RF Fingerprinting
- Cognitive Radios
- Automatic Recognition
- Physical Layer
- Massive MIMO
- Spectrum Monitoring
- Research works
- 8.2k fractional, since 1951
- In the world top 10%
- 1.7k per year above
- Top-10% rate
- 20.2% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +204% the tick is no change
Which countries lead Wireless Signal Modulation Classification research?
By volume, China and the United States publish the most (2k and 323 works in 2022–2025).
By volume, 2022–2025
- 1 China 2k works
- 2 United States 323 works
- 3 India 252 works
- 4 South Korea 108 works
- 5 United Kingdom 96 works
- 6 Germany 57 works
- 7 Canada 54 works
- 8 France 48 works
- 9 Türkiye 45 works
- 10 ?? 41 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Wireless Signal Modulation Classification research?
By volume in 2022–2025, Xidian University publishes the most Wireless Signal Modulation Classification research, followed by National University of Defense Technology and University of Electronic Science and Technology of China.
By volume, 2022–2025
- 1 Xidian University China 107 works
- 2 National University of Defense Technology China 101 works
- 3 University of Electronic Science and Technology of China China 93 works
- 4 Beijing University of Posts and Telecommunications China 75 works
- 5 Harbin Engineering University China 71 works
- 6 Southeast University China 59 works
- 7 Beijing Institute of Technology China 56 works
- 8 Nanjing University of Posts and Telecommunications China 45 works
- 9 Nanjing University of Aeronautics and Astronautics China 30 works
- 10 PLA Army Engineering University China 29 works
Who are the leading researchers in Wireless Signal Modulation Classification?
The most-cited researchers publishing on Wireless Signal Modulation Classification include H. Vincent Poor, Zhu Han and Dusit Niyato.
- 1 H. Vincent Poor 9.5k citations
- 2 Zhu Han 4.7k citations
- 3 Dusit Niyato 4.5k citations
- 4 Mérouane Debbah 4.3k citations
- 5 Yonina C. Eldar 4k citations
- 6 Victor C. M. Leung 3.7k citations
- 7 Shlomo Shamai 3.2k citations
- 8 Geoffrey Ye Li 3k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Wireless Signal Modulation Classification research done?
The largest centres of Wireless Signal Modulation Classification research in 2022–2025 are Beijing (China), Nanjing (China), Xi'an (China) and Chengdu (China). Among places with at least 20 works in it, it is an unusually large share of all research in Xi'an, Harbin and Changsha.
Largest cities, 2022–2025
Where it is the local speciality
- Xi'anCN · 216.6 works8.0×
- HarbinCN · 106.1 works7.6×
- ChangshaCN · 114.6 works6.7×
- ChengduCN · 156.0 works6.7×
- NanjingCN · 227.5 works6.6×
Location quotient: how much more of its research is in Wireless Signal Modulation Classification than the world average.
Where is the best place to study Wireless Signal Modulation Classification?
Among universities, judged by research, Southeast University, Beijing University of Posts and Telecommunications and University of Electronic Science and Technology of China score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Southeast University China | 69.8 | 19.8% | 12.6× | 59 | +274.3% |
| 2 | Beijing University of Posts and Telecommunications China | 69.0 | 25.7% | 35.0× | 75 | +126.6% |
| 3 | University of Electronic Science and Technology of China China | 67.9 | 25.1% | 23.9× | 93 | +70.0% |
| 4 | Xidian University China | 67.8 | 31.2% | 42.0× | 107 | -6.0% |
| 5 | Beijing Institute of Technology China | 65.2 | 26.2% | 13.5× | 56 | +89.8% |
| 6 | Nanjing University of Posts and Telecommunications China | 63.9 | 25.6% | 30.0× | 45 | +104.1% |
| 7 | Northeastern University United States | 63.5 | 41.7% | 10.9× | 12 | — |
| 8 | Harbin Engineering University China | 63.2 | 17.8% | 39.2× | 71 | +120.6% |
| 9 | National University of Defense Technology China | 62.5 | 21.4% | 37.3× | 101 | +44.1% |
| 10 | Beijing Jiaotong University China | 55.0 | 23.4% | 9.6× | 20 | +98.0% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Wireless Signal Modulation Classification research growing?
Output in 2018–2022 was 204% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Wireless Signal Modulation Classification.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.