Wanli Ni

2.0k total citations · 1 hit paper
108 papers, 1.3k citations indexed

About

Wanli Ni is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Wanli Ni has authored 108 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Electrical and Electronic Engineering, 38 papers in Artificial Intelligence and 33 papers in Aerospace Engineering. Recurrent topics in Wanli Ni's work include Advanced Wireless Communication Technologies (60 papers), Privacy-Preserving Technologies in Data (30 papers) and Advanced MIMO Systems Optimization (22 papers). Wanli Ni is often cited by papers focused on Advanced Wireless Communication Technologies (60 papers), Privacy-Preserving Technologies in Data (30 papers) and Advanced MIMO Systems Optimization (22 papers). Wanli Ni collaborates with scholars based in China, Singapore and United Kingdom. Wanli Ni's co-authors include Hui Tian, Yuanwei Liu, Zhaohui Yang, Wen Wang, Dusit Niyato, Yonina C. Eldar, Xiao Liu, Yue Chen, Xuemin Shen and Kai‐Kit Wong and has published in prestigious journals such as Sensors, IEEE Transactions on Communications and IEEE Transactions on Wireless Communications.

In The Last Decade

Wanli Ni

97 papers receiving 1.3k citations

Hit Papers

Exploiting Multi-Layer Refracting RIS-Assisted Receiver f... 2024 2026 2025 2024 25 50 75 100

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Wanli Ni China 19 895 451 350 326 50 108 1.3k
Toan-Van Nguyen South Korea 23 991 1.1× 370 0.8× 312 0.9× 393 1.2× 26 0.5× 95 1.4k
Shuaishuai Guo China 21 937 1.0× 545 1.2× 115 0.3× 342 1.0× 63 1.3× 84 1.2k
Jingjing Cui United Kingdom 20 1.1k 1.2× 587 1.3× 136 0.4× 434 1.3× 92 1.8× 47 1.5k
Lina Bariah United Arab Emirates 19 917 1.0× 220 0.5× 132 0.4× 313 1.0× 51 1.0× 68 1.2k
Wei Duan China 20 960 1.1× 270 0.6× 104 0.3× 414 1.3× 48 1.0× 110 1.2k
Wenqiang Yi United Kingdom 20 942 1.1× 503 1.1× 115 0.3× 333 1.0× 45 0.9× 72 1.2k
Chengwen Xing China 22 1.5k 1.6× 653 1.4× 199 0.6× 656 2.0× 110 2.2× 112 1.9k
Xuelin Cao United States 20 731 0.8× 478 1.1× 159 0.5× 613 1.9× 147 2.9× 41 1.3k
Junsheng Mu China 16 426 0.5× 316 0.7× 207 0.6× 262 0.8× 107 2.1× 88 905

Countries citing papers authored by Wanli Ni

Since Specialization
Citations

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

Fields of papers citing papers by Wanli Ni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wanli Ni

This figure shows the co-authorship network connecting the top 25 collaborators of Wanli Ni. A scholar is included among the top collaborators of Wanli Ni based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wanli Ni. Wanli Ni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Quan, Hongping, Wanli Ni, Xiangyu Ye, et al.. (2025). Large Language Model Agents for Radio Map Generation and Wireless Network Planning. IEEE Networking Letters. 7(3). 166–170. 5 indexed citations
2.
Tian, Hui, et al.. (2025). Joint Beamforming Design for Multifunctional RIS-Aided Over-the-Air Federated Learning. IEEE Internet of Things Journal. 12(12). 21720–21739.
3.
Xu, Wenjun, et al.. (2024). Diffusion Model-Based Channel Estimation for RIS-Aided Communication Systems. IEEE Wireless Communications Letters. 13(9). 2586–2590. 1 indexed citations
4.
Liu, Chuanhong, Caili Guo, Yang Yang, et al.. (2024). Explainable Semantic Communication for Text Tasks. IEEE Internet of Things Journal. 11(24). 39820–39833. 6 indexed citations
5.
Tian, Hui, et al.. (2024). Federated Split Learning for Distributed Intelligence with Resource-Constrained Devices. 798–803. 1 indexed citations
6.
Zhang, Xinran, Hui Tian, Wanli Ni, & Zhaohui Yang. (2024). Deep Reinforcement Learning for Multi-Functional RIS-Aided Over-the-Air Federated Learning in Internet of Robotic Things. 5461–5466. 3 indexed citations
7.
Ni, Wanli, et al.. (2024). Federated Contrastive Learning for Personalized Semantic Communication. IEEE Communications Letters. 28(8). 1875–1879. 4 indexed citations
8.
Wang, Wen, et al.. (2024). Multi-Functional RIS-Aided Cell-Free Networks. IEEE Transactions on Vehicular Technology. 73(9). 13968–13973. 6 indexed citations
10.
Tian, Hui, et al.. (2024). Multipath Identification, User Localization, and Environment Mapping in Radio SLAM. IEEE Transactions on Communications. 72(10). 6457–6473. 6 indexed citations
11.
Tian, Hui, et al.. (2024). Deep Reinforcement Learning Enables Joint Trajectory and Communication in Internet of Robotic Things. IEEE Transactions on Wireless Communications. 23(12). 18154–18168. 7 indexed citations
12.
Tian, Hui, et al.. (2024). Federated Low-Rank Adaptation for Large Models Fine-Tuning Over Wireless Networks. IEEE Transactions on Wireless Communications. 24(1). 659–675. 6 indexed citations
13.
Zhang, Junhe, Wanli Ni, & Dongyu Wang. (2024). Federated Split Learning With Model Pruning and Gradient Quantization in Wireless Networks. IEEE Transactions on Vehicular Technology. 74(4). 6850–6855.
14.
Ni, Wanli, et al.. (2023). Federated Semantic Learning Driven by Information Bottleneck for Task-Oriented Communications. IEEE Communications Letters. 27(10). 2652–2656. 20 indexed citations
15.
Ni, Wanli, et al.. (2023). Joint Trajectory and Radio Resource Optimization for Autonomous Mobile Robots Exploiting Multi-Agent Reinforcement Learning. IEEE Transactions on Communications. 71(9). 5244–5258. 5 indexed citations
16.
Ni, Wanli, et al.. (2023). Semi-Federated Learning: Convergence Analysis and Optimization of a Hybrid Learning Framework. IEEE Transactions on Wireless Communications. 22(12). 9438–9456. 15 indexed citations
17.
Zhang, Yuchen, Boyu Ning, Wanli Ni, et al.. (2023). Exploiting Faster-Than-Nyquist Signaling for MIMO Covert Communications: A Low-Complexity Design. IEEE Transactions on Vehicular Technology. 73(5). 7322–7327. 2 indexed citations
18.
Ni, Wanli, Yuanwei Liu, Zhaohui Yang, Hui Tian, & Xuemin Shen. (2022). Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role Can RIS Play?. IEEE Transactions on Wireless Communications. 21(12). 10083–10099. 57 indexed citations
19.
Tian, Hui, et al.. (2022). Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-Aided Over-the-Air Federated Learning. IEEE Transactions on Wireless Communications. 21(12). 10964–10980. 33 indexed citations
20.
Ni, Wanli, Yuanwei Liu, Yonina C. Eldar, Zhaohui Yang, & Hui Tian. (2022). STAR-RIS Integrated Nonorthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization. IEEE Internet of Things Journal. 9(18). 17136–17156. 69 indexed citations

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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