Il‐Min Kim

4.9k total citations
172 papers, 3.4k citations indexed

About

Il‐Min Kim is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Il‐Min Kim has authored 172 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 135 papers in Electrical and Electronic Engineering, 99 papers in Computer Networks and Communications and 24 papers in Artificial Intelligence. Recurrent topics in Il‐Min Kim's work include Cooperative Communication and Network Coding (71 papers), Advanced MIMO Systems Optimization (54 papers) and Advanced Wireless Communication Technologies (43 papers). Il‐Min Kim is often cited by papers focused on Cooperative Communication and Network Coding (71 papers), Advanced MIMO Systems Optimization (54 papers) and Advanced Wireless Communication Technologies (43 papers). Il‐Min Kim collaborates with scholars based in Canada, South Korea and United States. Il‐Min Kim's co-authors include Jae‐Mo Kang, Zhihang Yi, MinChul Ju, Dong In Kim, Chang-Jae Chun, Cheol Jeong, Vahid Tarokh, Sangseok Yun, Hyung-Myung Kim and Peng Liu and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Il‐Min Kim

165 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Il‐Min Kim Canada 33 3.0k 2.0k 479 324 157 172 3.4k
Stefano Tomasin Italy 24 2.6k 0.9× 1.7k 0.8× 339 0.7× 328 1.0× 141 0.9× 212 3.1k
Bixio Rimoldi Switzerland 19 1.9k 0.6× 1.6k 0.8× 363 0.8× 242 0.7× 175 1.1× 67 2.3k
Amitav Mukherjee United States 19 2.6k 0.8× 1.6k 0.8× 370 0.8× 363 1.1× 243 1.5× 66 2.8k
Sławomir Stańczak Germany 25 2.3k 0.7× 1.9k 0.9× 349 0.7× 264 0.8× 57 0.4× 311 2.9k
Iain B. Collings Australia 38 4.7k 1.5× 3.4k 1.6× 333 0.7× 691 2.1× 91 0.6× 316 5.2k
Soon Xin Ng United Kingdom 31 2.9k 0.9× 2.2k 1.1× 1.3k 2.7× 403 1.2× 163 1.0× 307 4.3k
Ender Ayanoğlu United States 27 2.6k 0.9× 1.8k 0.9× 226 0.5× 339 1.0× 206 1.3× 141 3.2k
Pinyi Ren China 25 2.1k 0.7× 1.7k 0.8× 310 0.6× 231 0.7× 129 0.8× 264 2.6k
Robert G. Maunder United Kingdom 25 1.9k 0.6× 1.5k 0.7× 365 0.8× 757 2.3× 169 1.1× 158 2.5k
Baoyu Zheng China 23 1.5k 0.5× 1.4k 0.7× 540 1.1× 106 0.3× 246 1.6× 223 2.5k

Countries citing papers authored by Il‐Min Kim

Since Specialization
Citations

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

Fields of papers citing papers by Il‐Min Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Il‐Min Kim. 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 Il‐Min Kim. The network helps show where Il‐Min Kim may publish in the future.

Co-authorship network of co-authors of Il‐Min Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Il‐Min Kim. A scholar is included among the top collaborators of Il‐Min Kim 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 Il‐Min Kim. Il‐Min Kim 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.
Saeed, Faisal, et al.. (2025). Generative-Diffusion-Model-Based Deep-Learning Framework for Remaining Useful Life Prediction. IEEE Internet of Things Journal. 12(11). 18431–18434. 2 indexed citations
2.
Kang, Jae‐Mo, Sangseok Yun, & Il‐Min Kim. (2025). CaMPASS-Net: A Deep Learning Framework on Capacity Maximization for MIMO Pinching Antenna Systems in IoT. IEEE Internet of Things Journal. 12(21). 45917–45920.
3.
Yun, Sangseok, et al.. (2023). Redundancy Management in Federated Learning for Fast Communication. IEEE Transactions on Communications. 71(11). 6332–6347. 3 indexed citations
4.
Ge, Yiqun, et al.. (2023). On the Effectiveness of Activation Noise in Both Training and Inference for Generative Classifiers. IEEE Access. 11. 131623–131638. 1 indexed citations
5.
Yun, Sangseok, et al.. (2022). Deep Neural Network-Based Precoder for Fairness Aware Secure NOMA Scheme. IEEE Transactions on Vehicular Technology. 71(5). 5615–5620. 2 indexed citations
6.
Yun, Sangseok, et al.. (2021). Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN–CNN Approach. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 5 indexed citations
7.
Yun, Sangseok, Jae‐Mo Kang, Il‐Min Kim, & Jeongseok Ha. (2020). Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme. IEEE Transactions on Vehicular Technology. 69(3). 3465–3469. 24 indexed citations
8.
Yun, Sangseok, et al.. (2019). Secure Communications With a Full-Duplex Relay Network Under Residual Self-Interference. IEEE Communications Letters. 24(3). 496–500. 7 indexed citations
9.
Jang, Youngrok, Gyuyeol Kong, Minchae Jung, Sooyong Choi, & Il‐Min Kim. (2019). Deep Autoencoder Based CSI Feedback With Feedback Errors and Feedback Delay in FDD Massive MIMO Systems. IEEE Wireless Communications Letters. 8(3). 833–836. 56 indexed citations
10.
Yun, Sangseok, Il‐Min Kim, & Jeongseok Ha. (2019). Artificial Noise Scheme for Correlated MISO Wiretap Channels. IEEE Transactions on Vehicular Technology. 68(9). 9323–9327. 8 indexed citations
11.
Liu, Peng, Saeed Gazor, & Il‐Min Kim. (2014). Ergodic Capacity Analysis for Ubiquitous Cooperative Networks Employing Amplify-and-Forward Relaying. 263–267.
12.
Kim, Jinsul & Il‐Min Kim. (2014). A Study of User-Customized Social TV Platform. 182–185. 1 indexed citations
13.
Kim, Jinsul, et al.. (2014). Virtual assembly system for robot actor in performing art. International Journal of Software Engineering and Its Applications. 8(6). 339–350. 2 indexed citations
14.
Kim, Jong Wook, et al.. (2013). Efficient Range-based Query Processing on the Hadoop Distributed File System. 110–113. 1 indexed citations
15.
Kim, Il‐Min. (2011). Android phone app. development for large scale classes. Journal of Digital Convergence. 9(6). 343–354. 1 indexed citations
16.
Ju, MinChul, et al.. (2010). SER Analysis and PDF Derivation for Multi-Hop Amplify-and-Forward Relay Systems. IEEE Transactions on Communications. 58(8). 2413–2424. 13 indexed citations
17.
Ju, MinChul, Hyoung‐Kyu Song, & Il‐Min Kim. (2010). Joint Relay-and-Antenna Selection in Multi-Antenna Relay Networks. IEEE Transactions on Communications. 58(12). 3417–3422. 32 indexed citations
18.
Kim, Il‐Min, et al.. (2009). Designing and Implementing 3D Virtual Face Aesthetic Surgery System Based on Korean Standard Facial Data. Journal of Korea Multimedia Society. 12(5). 737–744.
19.
Lee, Cheol-Woong, et al.. (2008). Designing and Implementing 3D Virtual Face Aesthetic Surgery System. Journal of Digital Contents Society. 9(4). 751–758. 1 indexed citations
20.
Kim, H., Il‐Min Kim, Y. Lee, & B. Kahng. (2002). Scale-Free Network in Stock Markets. Journal of the Korean Physical Society. 40(6). 1105–1108. 47 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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