Xinping Yi

1.9k total citations · 1 hit paper
79 papers, 996 citations indexed

About

Xinping Yi is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Xinping Yi has authored 79 papers receiving a total of 996 indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Electrical and Electronic Engineering, 38 papers in Computer Networks and Communications and 16 papers in Artificial Intelligence. Recurrent topics in Xinping Yi's work include Advanced MIMO Systems Optimization (42 papers), Cooperative Communication and Network Coding (30 papers) and Millimeter-Wave Propagation and Modeling (12 papers). Xinping Yi is often cited by papers focused on Advanced MIMO Systems Optimization (42 papers), Cooperative Communication and Network Coding (30 papers) and Millimeter-Wave Propagation and Modeling (12 papers). Xinping Yi collaborates with scholars based in United Kingdom, China and Germany. Xinping Yi's co-authors include Wenjin Wang, Giuseppe Caire, Xiqi Gao, Xiaowei Huang, David Gesbert, Wenjie Ruan, Li You, James J. Sharp, Daniel Kroening and Youcheng Sun and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing.

In The Last Decade

Xinping Yi

69 papers receiving 987 citations

Hit Papers

A survey of safety and trustworthiness of deep neural net... 2020 2026 2022 2024 2020 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xinping Yi United Kingdom 16 623 318 288 139 71 79 996
Hiroshi Inamura Japan 9 648 1.0× 344 1.1× 182 0.6× 141 1.0× 22 0.3× 55 879
Hang Zhang China 17 431 0.7× 429 1.3× 125 0.4× 169 1.2× 70 1.0× 95 871
Xiaojiang Chen China 10 322 0.5× 163 0.5× 261 0.9× 80 0.6× 211 3.0× 32 786
Horácio C. Neto Portugal 18 426 0.7× 194 0.6× 144 0.5× 65 0.5× 183 2.6× 89 876
Rishad Shafik United Kingdom 18 1.0k 1.6× 375 1.2× 213 0.7× 46 0.3× 37 0.5× 129 1.4k
Miloš Krstić Germany 16 933 1.5× 285 0.9× 88 0.3× 120 0.9× 32 0.5× 225 1.2k
Brian Richards United States 12 470 0.8× 328 1.0× 141 0.5× 26 0.2× 88 1.2× 35 1.1k
Diana Göhringer Germany 16 307 0.5× 419 1.3× 60 0.2× 87 0.6× 130 1.8× 139 900
Yingyan Lin United States 19 393 0.6× 187 0.6× 472 1.6× 56 0.4× 522 7.4× 81 1.1k
Anshul Kumar India 14 303 0.5× 280 0.9× 78 0.3× 49 0.4× 99 1.4× 89 714

Countries citing papers authored by Xinping Yi

Since Specialization
Citations

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

Fields of papers citing papers by Xinping Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xinping Yi

This figure shows the co-authorship network connecting the top 25 collaborators of Xinping Yi. A scholar is included among the top collaborators of Xinping Yi 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 Xinping Yi. Xinping Yi 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.
Yi, Xinping, et al.. (2026). Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization. IEEE Transactions on Information Forensics and Security. 21. 2284–2299.
2.
Yi, Xinping, et al.. (2025). GRLinQ: A Hybrid Model/Data-Driven Spectrum Sharing Mechanism for Device-to-Device Communications. IEEE Transactions on Communications. 73(11). 11214–11229.
3.
Zhang, Junqing, et al.. (2025). Evasion Attacks and Countermeasures in Deep Learning-Based Wi-Fi Gesture Recognition. IEEE Transactions on Mobile Computing. 24(9). 8180–8195. 1 indexed citations
4.
Yi, Xinping, et al.. (2025). Revisiting Topological Interference Management: A Learning-to-Code on Graphs Perspective. IEEE Transactions on Communications. 73(12). 14275–14288. 1 indexed citations
5.
Yi, Xinping, et al.. (2025). A Tensor-Structured Approach to Dynamic Channel Prediction for Massive MIMO Systems With Temporal Non-Stationarity. IEEE Transactions on Wireless Communications. 25. 6869–6886. 1 indexed citations
6.
Yi, Xinping, et al.. (2025). Toward Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework. IEEE Transactions on Wireless Communications. 24(12). 10517–10533. 1 indexed citations
7.
Lin, Yuxing, Shi Jin, Michail Matthaiou, & Xinping Yi. (2025). Spherical RIS-Enabled Channel Estimation and User Self-Localization for ISAC Systems. IEEE Transactions on Communications. 73(10). 9597–9611.
8.
Liang, Le, et al.. (2025). Meta-Learning Empowered Graph Neural Networks for Radio Resource Management. IEEE Transactions on Communications. 73(9). 7584–7598.
9.
Wan, Kai, et al.. (2025). Blind and Topological Interference Managements for Bistatic Integrated Sensing and Communication. IEEE Journal on Selected Areas in Communications. 44. 76–91. 1 indexed citations
10.
Yi, Xinping, et al.. (2024). Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems. IEEE Journal of Selected Topics in Signal Processing. 18(4). 646–661. 3 indexed citations
11.
Zhuang, Qian, Shufei Zhang, Kaizhu Huang, et al.. (2024). Perturbation diversity certificates robust generalization. Neural Networks. 172. 106117–106117.
12.
Huang, Kaizhu, et al.. (2024). ES-GNN: Generalizing Graph Neural Networks Beyond Homophily With Edge Splitting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 11345–11360. 2 indexed citations
13.
Yi, Xinping, et al.. (2022). A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network. Frontiers in Neuroscience. 16. 759900–759900. 11 indexed citations
14.
Liao, Chung-Shou, et al.. (2021). Topological Interference Management with Adversarial Topology Perturbation: An Algorithmic Perspective. arXiv (Cornell University). 3 indexed citations
15.
Zhang, Shufei, et al.. (2021). Towards Better Robust Generalization with Shift Consistency Regularization. International Conference on Machine Learning. 12524–12534. 6 indexed citations
16.
Yang, Tianyu, et al.. (2020). Active Channel Sparsification and Precoding for Dual-Polarized FDD Massive MIMO.. 1–6. 1 indexed citations
17.
Yi, Xinping, et al.. (2020). How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks. arXiv (Cornell University). 33. 21346–21356. 1 indexed citations
18.
Huang, Xiaowei, Daniel Kroening, Wenjie Ruan, et al.. (2020). A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Computer Science Review. 37. 100270–100270. 259 indexed citations breakdown →
19.
Yi, Xinping & Hua Sun. (2019). Opportunistic Topological Interference Management. 1287–1291. 3 indexed citations
20.
Huang, Xiaowei, Daniel Kroening, Wenjie Ruan, et al.. (2018). A Survey of Safety and Trustworthiness of Deep Neural Networks. arXiv (Cornell University). 5 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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