Huaiyu Dai

9.8k total citations · 2 hit papers
250 papers, 6.7k citations indexed

About

Huaiyu Dai is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Huaiyu Dai has authored 250 papers receiving a total of 6.7k indexed citations (citations by other indexed papers that have themselves been cited), including 166 papers in Computer Networks and Communications, 129 papers in Electrical and Electronic Engineering and 59 papers in Artificial Intelligence. Recurrent topics in Huaiyu Dai's work include Advanced MIMO Systems Optimization (56 papers), Cooperative Communication and Network Coding (50 papers) and Privacy-Preserving Technologies in Data (35 papers). Huaiyu Dai is often cited by papers focused on Advanced MIMO Systems Optimization (56 papers), Cooperative Communication and Network Coding (50 papers) and Privacy-Preserving Technologies in Data (35 papers). Huaiyu Dai collaborates with scholars based in United States, China and Canada. Huaiyu Dai's co-authors include H. Vincent Poor, Xiaofan He, Liang Xiao, Hongyuan Zhang, Richeng Jin, İsmail Güvenç, Peng Ning, Zhiguo Ding, Ali Rahmati and Arif I. Sarwat and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing.

In The Last Decade

Huaiyu Dai

240 papers receiving 6.5k citations

Hit Papers

A Survey on Low Latency Towards 5G: RAN, Core Network and... 2016 2026 2019 2022 2018 2016 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Huaiyu Dai United States 40 4.2k 4.2k 1.2k 1.2k 489 250 6.7k
Guanding Yu China 43 4.8k 1.1× 5.0k 1.2× 1.2k 1.0× 1.1k 0.9× 465 1.0× 279 7.4k
Changchuan Yin China 30 2.9k 0.7× 3.0k 0.7× 1.5k 1.3× 1.1k 1.0× 309 0.6× 185 5.1k
Yuanming Shi China 38 2.8k 0.7× 5.0k 1.2× 2.2k 1.8× 1.4k 1.2× 315 0.6× 226 7.5k
Feng Lyu China 34 2.8k 0.7× 2.3k 0.6× 1.1k 0.9× 1.4k 1.2× 627 1.3× 133 5.0k
Qiang Ni United Kingdom 54 6.1k 1.4× 6.6k 1.6× 1.1k 0.9× 1.4k 1.2× 848 1.7× 357 10.4k
Xiaofeng Tao China 39 3.5k 0.8× 4.3k 1.0× 799 0.7× 1.0k 0.9× 504 1.0× 540 6.4k
Zhisheng Niu China 48 6.1k 1.4× 6.4k 1.5× 973 0.8× 656 0.6× 579 1.2× 336 8.5k
Haipeng Yao China 41 3.9k 0.9× 2.1k 0.5× 1.0k 0.9× 1.5k 1.3× 1.1k 2.2× 230 5.7k
Lei Liu China 41 2.9k 0.7× 2.8k 0.7× 1.1k 1.0× 827 0.7× 999 2.0× 299 5.6k
Fengxiao Tang China 36 3.0k 0.7× 3.0k 0.7× 1.4k 1.2× 1.4k 1.2× 433 0.9× 88 6.2k

Countries citing papers authored by Huaiyu Dai

Since Specialization
Citations

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

Fields of papers citing papers by Huaiyu Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Huaiyu Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Huaiyu Dai. A scholar is included among the top collaborators of Huaiyu Dai 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 Huaiyu Dai. Huaiyu Dai 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.
Jia, Juncheng, et al.. (2025). Efficient federated learning with timely update dissemination. Knowledge and Information Systems. 67(11). 10691–10725. 1 indexed citations
2.
Tang, Jiaqi, Juan Liu, Xiaofan He, et al.. (2025). Deep Reinforcement Learning for AoI-Aware Trajectory and Phase-Shift Design in IRS-Assisted UAV Data Collection. IEEE Transactions on Wireless Communications. 24(12). 10613–10628.
3.
Liu, Ji, et al.. (2024). FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update. Proceedings of the AAAI Conference on Artificial Intelligence. 38(12). 13900–13908. 24 indexed citations
4.
Dai, Huaiyu, et al.. (2023). On the outage probability of uplink IRS-aided networks: NOMA and OMA. Physical Communication. 59. 102077–102077. 3 indexed citations
5.
Zhang, Jingzhe, Xiaofan He, & Huaiyu Dai. (2023). Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT. IEEE Internet of Things Journal. 10(12). 10605–10620. 6 indexed citations
6.
Zhang, Zhaoyang, et al.. (2023). Distributed Learning Over Networks With Graph-Attention-Based Personalization. IEEE Transactions on Signal Processing. 71. 2071–2086. 5 indexed citations
7.
He, Xiaofan, et al.. (2023). Location Privacy-Aware and Energy-Efficient Offloading for Distributed Edge Computing. IEEE Transactions on Wireless Communications. 22(11). 7975–7988. 6 indexed citations
8.
Liwang, Minghui, Zhibin Gao, Seyyedali Hosseinalipour, et al.. (2023). Graph-Represented Computation-Intensive Task Scheduling Over Air-Ground Integrated Vehicular Networks. IEEE Transactions on Services Computing. 16(5). 3397–3411. 16 indexed citations
9.
Jin, Richeng, Xiaofan He, & Huaiyu Dai. (2023). Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent. 13. 1–6. 1 indexed citations
10.
Nguyen, Nhan T., Kyungchun Lee, & Huaiyu Dai. (2022). Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems. IEEE Transactions on Vehicular Technology. 71(8). 8739–8755. 30 indexed citations
11.
Jin, Richeng, Xiaofan He, & Huaiyu Dai. (2022). Communication Efficient Federated Learning With Energy Awareness Over Wireless Networks. IEEE Transactions on Wireless Communications. 21(7). 5204–5219. 35 indexed citations
12.
Hosseinalipour, Seyyedali, et al.. (2019). Smart Information Spreading for Opinion Maximization in Social Networks. 2251–2259. 15 indexed citations
13.
Xiao, Liang, et al.. (2019). Deep Reinforcement Learning-Enabled Secure Visible Light Communication Against Eavesdropping. IEEE Transactions on Communications. 67(10). 6994–7005. 78 indexed citations
14.
Nguyen, Nhan T., Kyungchun Lee, & Huaiyu Dai. (2019). QR-Decomposition-Aided Tabu Search Detection for Large MIMO Systems. IEEE Transactions on Vehicular Technology. 68(5). 4857–4870. 13 indexed citations
15.
Parvez, Imtiaz, Ali Rahmati, İsmail Güvenç, Arif I. Sarwat, & Huaiyu Dai. (2018). A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions. IEEE Communications Surveys & Tutorials. 20(4). 3098–3130. 621 indexed citations breakdown →
16.
He, Xiaofan, Richeng Jin, & Huaiyu Dai. (2018). Deep PDS-Learning for Privacy-Aware Offloading in MEC-Enabled IoT. IEEE Internet of Things Journal. 6(3). 4547–4555. 66 indexed citations
17.
Xiao, Liang, et al.. (2017). A Secure Mobile Crowdsensing Game With Deep Reinforcement Learning. IEEE Transactions on Information Forensics and Security. 13(1). 35–47. 149 indexed citations
18.
Hosseinalipour, Seyyedali, et al.. (2017). Dynamic Advertising in VANETs Using Repeated Auctions. 1–6. 4 indexed citations
19.
Xiao, Liang, Caixia Xie, Tianhua Chen, Huaiyu Dai, & H. Vincent Poor. (2016). A Mobile Offloading Game Against Smart Attacks. IEEE Access. 4. 2281–2291. 90 indexed citations
20.

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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