Nan Zhao

1.6k citations
40 papers · 1.1k indexed · 2 hit papers · h-index 13

Nan Zhao

36 papers receiving 1.1k citations

Hit Papers

Multi-Agent Deep Reinforcement Learning for Task ...2682019202620212023100200300

Peers

Nan Zhao
Comparison fields: 5 of 71
  • Computer Networks and Communications 547
  • Aerospace Engineering 332
  • Control and Systems Engineering 205
  • Electrical and Electronic Engineering 458
  • Artificial Intelligence 245
Replace Zhiyuan Ren with:
Zhiyuan Ren China
Jochen Seitz Germany
Xiangwang Hou China
Kimihiro Mizutani Japan
Xianglin Wei China
Yuanguo Bi China
Qiang Fan United States
Hichem Sedjelmaci France
Ziaul Haq Abbas Pakistan
Yi Zhou China
Nan Zhao relative to Zhiyuan Ren China Zhiyuan Ren's profile →
Citations per field
00.5×1.5×2.3×
Zhiyuan Ren · 1×
Citations per year

Countries citing papers authored by Nan Zhao

Since Specialization
Citations

This map shows the geographic impact of Nan Zhao'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 Nan Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nan Zhao more than expected).

Fields of papers citing papers by Nan Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nan Zhao. 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 Nan Zhao. The network helps show where Nan Zhao may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Nan Zhao, 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 Nan Zhao Line = papers co-authored together Nan Zhao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20243
2 20240
3 20248
4 20245
5 20231
6 20233
7 20231
8 20236
9 20236
10 202241
11 20221
12
Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computingbreakdown →
2022268
13 202214
14 20210
15 202049
16
Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networksbreakdown →
2019335
17 201852
18 20170
19 20167
20 20084

About Nan Zhao

Nan Zhao is a scholar working on Computer Networks and Communications, Computer Science Applications and Signal Processing, having authored 40 papers that have together received 1.1k indexed citations. Recurring topics across this work include Cooperative Communication and Network Coding (7 papers), Advanced MIMO Systems Optimization (7 papers), Full-Duplex Wireless Communications (6 papers), Privacy-Preserving Technologies in Data (5 papers), Wireless Communication Security Techniques (4 papers), IoT and Edge/Fog Computing (4 papers), UAV Applications and Optimization (4 papers) and Age of Information Optimization (4 papers). The work is most often cited by research in Computer Networks and Communications (547 citations), Aerospace Engineering (332 citations) and Control and Systems Engineering (205 citations). Nan Zhao has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Yiyang Pei, Ying‐Chang Liang, Dusit Niyato, Yunhao Jiang, Zhiyang Ye, Minghu Wu, Zehua Liu, Siqi Liu, Minghu Wu and Wei Xiong. Their work appears in journals such as IEEE Access, Frontiers in Microbiology and Information Sciences.

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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