Lenan Wu

7.1k citations
244 papers · 5.2k · 1 hit paper · h-index 35

Impact in

Papers in

Lenan Wu

232 papers receiving 4.8k citations

Lenan Wu's Hit Papers

Automatic Modulation Classification: A Deep Learning Enabled Approach 2018 · 290 citations
2900+2+5Years since publication50100150200250

Peers

Lenan Wu
Comparison fields: 5 of 157
  • Neurology 881
  • Computer Vision and Pattern Recognition 1.2k
  • Artificial Intelligence 1.4k
  • Signal Processing 414
  • Aerospace Engineering 940
Replace L.M. Patnaik with:
L.M. Patnaik India
Weibo Liu China
Amitava Chatterjee India
Shuai Liu China
Mohammad Teshnehlab Iran
Jacob Goldberger Israel
Rui Zhang China
Tao Li China
Shuiwang Ji United States
Fathi E. Abd El‐Samie Egypt
Lenan Wu relative to L.M. Patnaik India L.M. Patnaik's profile →
Citations per field
00.5×
L.M. Patnaik · 1×
Citations per year

Countries citing papers authored by Lenan Wu

Since Specialization
Citations

This map shows the geographic impact of Lenan Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lenan Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lenan Wu more than expected).

Fields of papers citing papers by Lenan Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lenan Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lenan Wu. The network helps show where Lenan Wu may publish in the future.

Co-authors

The 25 scholars most cited alongside Lenan Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lenan Wu Line = papers co-authored together Lenan Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 244 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008320
2
Automatic Modulation Classification: A Deep Learning Enabled Approach
Hit paper breakdown →
2018290
3 2011281
4 2012248
5 2012223
6 2020202
7 2011177
8 2011155
9 2016129
10 2021110
11 2010109
12 2011107
13 2017102
14 200987
15 201383
16 202378
17 201577
18 200873
19 200971
20 201469

About Lenan Wu

Lenan Wu is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications, Aerospace Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 244 papers that have together received 5.2k indexed citations. Recurring topics across this work include Advanced Wireless Communication Techniques (47 papers), Radar Systems and Signal Processing (34 papers), Advanced SAR Imaging Techniques (27 papers), Indoor and Outdoor Localization Technologies (25 papers), Wireless Communication Networks Research (22 papers), Target Tracking and Data Fusion in Sensor Networks (18 papers), PAPR reduction in OFDM (17 papers) and Sparse and Compressive Sensing Techniques (16 papers). The work is most often cited by research in Neurology (881 citations), Computer Vision and Pattern Recognition (1.2k citations), Artificial Intelligence (1.4k citations), Signal Processing (414 citations) and Aerospace Engineering (940 citations). Lenan Wu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Yudong Zhang, Shuihua Wang‎, Peng Chen, Chenhao Qi, Fan Meng, Xianbin Wang, Julian Cheng, Zhengchao Dong, Wei Geng and Yu Yao. Their work appears in journals such as Sensors, IEEE Transactions on Vehicular Technology, Electromagnetic waves, Electronics Letters and Signal Processing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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