Mehul Motani

7.1k total citations · 3 hit papers
277 papers, 4.9k citations indexed

About

Mehul Motani is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Mehul Motani has authored 277 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 177 papers in Electrical and Electronic Engineering, 173 papers in Computer Networks and Communications and 49 papers in Artificial Intelligence. Recurrent topics in Mehul Motani's work include Cooperative Communication and Network Coding (76 papers), Energy Harvesting in Wireless Networks (63 papers) and Advanced MIMO Systems Optimization (61 papers). Mehul Motani is often cited by papers focused on Cooperative Communication and Network Coding (76 papers), Energy Harvesting in Wireless Networks (63 papers) and Advanced MIMO Systems Optimization (61 papers). Mehul Motani collaborates with scholars based in Singapore, United States and China. Mehul Motani's co-authors include Vineet Srivastava, Vikram Srinivasan, Xin Kang, Rui Zhang, Panida Jirutitijaroen, Wee-Seng Soh, Hon-Fah Chong, Hari Krishna Garg, Kok-Kiong Yap and Anirudh Raju Natarajan and has published in prestigious journals such as IEEE Transactions on Information Theory, Journal of neurosurgery and IEEE Access.

In The Last Decade

Mehul Motani

259 papers receiving 4.6k citations

Hit Papers

Cross-layer design: a survey and the road ahead 2005 2026 2012 2019 2005 2012 2015 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mehul Motani Singapore 31 3.5k 2.9k 509 429 399 277 4.9k
Dirk Timmermann Germany 23 2.3k 0.7× 1.6k 0.5× 405 0.8× 254 0.6× 273 0.7× 265 3.4k
Gregory J. Pottie United States 27 6.1k 1.7× 3.8k 1.3× 302 0.6× 569 1.3× 411 1.0× 144 7.2k
Stefano Basagni United States 40 5.9k 1.7× 3.5k 1.2× 784 1.5× 297 0.7× 356 0.9× 150 7.2k
Cailian Chen China 36 3.0k 0.9× 2.0k 0.7× 667 1.3× 763 1.8× 203 0.5× 439 5.2k
Adam Wolisz Germany 43 5.7k 1.6× 4.4k 1.5× 272 0.5× 224 0.5× 309 0.8× 321 7.3k
Anish Arora United States 32 3.6k 1.0× 1.6k 0.5× 187 0.4× 527 1.2× 188 0.5× 160 4.4k
Saurabh Ganeriwal United States 19 4.3k 1.2× 1.5k 0.5× 237 0.5× 330 0.8× 618 1.5× 36 4.7k
Hyuk Lim South Korea 29 2.3k 0.7× 1.5k 0.5× 347 0.7× 492 1.1× 85 0.2× 161 3.4k
Yi Shi United States 42 4.4k 1.3× 4.7k 1.6× 164 0.3× 614 1.4× 149 0.4× 235 6.3k
Ignas Niemegeers Netherlands 26 2.0k 0.6× 2.8k 1.0× 553 1.1× 171 0.4× 154 0.4× 197 3.7k

Countries citing papers authored by Mehul Motani

Since Specialization
Citations

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

Fields of papers citing papers by Mehul Motani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mehul Motani

This figure shows the co-authorship network connecting the top 25 collaborators of Mehul Motani. A scholar is included among the top collaborators of Mehul Motani 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 Mehul Motani. Mehul Motani 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.
Lubbe, Stephanie C. C. van der, et al.. (2025). Classifying Patient Complaints Using Artificial Intelligence–Powered Large Language Models: Cross-Sectional Study. Journal of Medical Internet Research. 27. e74231–e74231.
2.
Huang, Zhitong, et al.. (2025). Channel Modeling, Performance Analysis, and Probabilistic Shaping for Underwater Wireless Optical Communications. IEEE Journal on Selected Areas in Communications. 43(5). 1568–1581. 1 indexed citations
4.
Motani, Mehul, et al.. (2024). Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic Regression-Discovered Splits. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 37–38. 1 indexed citations
5.
Motani, Mehul, et al.. (2024). Multi-Task Generalizable Communication: Beyond the Information Bottleneck. abs/1703.00810. 980–985. 1 indexed citations
6.
Zhou, Lin & Mehul Motani. (2023). Finite Blocklength Lossy Source Coding for Discrete Memoryless Sources. 20(3). 157–389. 8 indexed citations
7.
8.
Motani, Mehul, et al.. (2023). Short-range medium-rate fleet communications using SPAD arrays. 1 indexed citations
9.
Zhao, Junhui, Lihua Yang, Minghua Xia, & Mehul Motani. (2021). Unified Analysis of Coordinated Multipoint Transmissions in mmWave Cellular Networks. IEEE Internet of Things Journal. 9(14). 12166–12180. 48 indexed citations
10.
Yang, Lihua, Teng Joon Lim, Junhui Zhao, & Mehul Motani. (2021). Modeling and Analysis of HetNets With Interference Management Using Poisson Cluster Process. IEEE Transactions on Vehicular Technology. 70(11). 12039–12054. 15 indexed citations
11.
Motani, Mehul, et al.. (2021). Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization. Neural Information Processing Systems. 34. 1 indexed citations
12.
Motani, Mehul, et al.. (2020). DropNet: Reducing Neural Network Complexity via Iterative Pruning. International Conference on Machine Learning. 1. 9356–9366. 9 indexed citations
13.
Zhou, Lin, et al.. (2019). On Lossy Multi-Connectivity: Finite Blocklength Performance and Second-Order Asymptotics. IEEE Journal on Selected Areas in Communications. 37(4). 735–748. 9 indexed citations
14.
Yao, Jia, et al.. (2019). Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling. Computing in Cardiology Conference. 1–4. 1 indexed citations
15.
Zhou, Lin, Vincent Y. F. Tan, & Mehul Motani. (2018). Second-order asymptotically optimal statistical classification. Information and Inference A Journal of the IMA. 6 indexed citations
16.
Zhou, Lin, Vincent Y. F. Tan, Lei Yu, & Mehul Motani. (2018). Exponential Strong Converse for Content Identification With Lossy Recovery. IEEE Transactions on Information Theory. 64(8). 5879–5897. 8 indexed citations
17.
Zhou, Lin, Vincent Y. F. Tan, & Mehul Motani. (2017). Achievable Moderate Deviations Asymptotics for Streaming Compression of Correlated Sources. IEEE Transactions on Information Theory. 64(5). 3756–3780. 1 indexed citations
18.
Zhou, Lin, Vincent Y. F. Tan, & Mehul Motani. (2017). Second-Order and Moderate Deviation Asymptotics for Successive Refinement. IEEE Transactions on Information Theory. 1–1. 29 indexed citations
19.
Jirutitijaroen, Panida, et al.. (2015). Detecting False Data Injection Attacks in AC State Estimation. IEEE Transactions on Smart Grid. 6(5). 2476–2483. 321 indexed citations breakdown →
20.
Shahabudeen, Shiraz & Mehul Motani. (2012). Short Paper: Performance Analysis of a MACA based Protocol for Adhoc Underwater Networks. 1 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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