Longbo Huang

3.8k total citations · 1 hit paper
95 papers, 2.1k citations indexed

About

Longbo Huang is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Longbo Huang has authored 95 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Computer Networks and Communications, 49 papers in Electrical and Electronic Engineering and 19 papers in Artificial Intelligence. Recurrent topics in Longbo Huang's work include Advanced Wireless Network Optimization (23 papers), Advanced MIMO Systems Optimization (17 papers) and Age of Information Optimization (15 papers). Longbo Huang is often cited by papers focused on Advanced Wireless Network Optimization (23 papers), Advanced MIMO Systems Optimization (17 papers) and Age of Information Optimization (15 papers). Longbo Huang collaborates with scholars based in China, United States and Hong Kong. Longbo Huang's co-authors include Michael J. Neely, Eytan Modiano, Kun Chen, Zhixuan Fang, Leana Golubchik, Yuan Yao, Adam Wierman, Xin Liu, Pingzhong Tang and Qingpeng Cai and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory and IEEE Journal on Selected Areas in Communications.

In The Last Decade

Longbo Huang

93 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optimizing age-of-information in a multi-class queueing system

2015 238 citations Longbo Huang et al. profile →

Peers

Longbo Huang

CNC

EEE

IS

EPIDE

AI

György Dán Sweden

Fengli Zhang China

Ramón Cáceres United States

Linqi Song Hong Kong

Xiumin Wang China

Konstantinos Pelechrinis United States

Leonidas Georgiadis Greece

Yaohui Jin China

Bo Ji United States

Yuanxiong Guo United States

György Dán Sweden

Longbo Huang

2.0k ×1.4

CNC

1.1k ×1.0

EEE

488 ×1.6

IS

50 ×0.2

EPIDE

538 ×3.0

AI

Citations per year, relative to Longbo Huang Longbo Huang (= 1×) peers György Dán

Countries citing papers authored by Longbo Huang

Since Specialization

Citations

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

Fields of papers citing papers by Longbo Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Longbo Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Longbo Huang. A scholar is included among the top collaborators of Longbo Huang 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 Longbo Huang. Longbo Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Huang, Longbo, et al.. (2024). Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 8(3). 1–48. 1 indexed citations

2.

Golubchik, Leana, et al.. (2024). When Lyapunov Drift Based Queue Scheduling Meets Adversarial Bandit Learning. IEEE/ACM Transactions on Networking. 32(4). 3034–3044. 4 indexed citations

3.

Chen, Yu, et al.. (2024). Multi-User Delay-Constrained Scheduling With Deep Recurrent Reinforcement Learning. IEEE/ACM Transactions on Networking. 32(3). 2344–2359. 3 indexed citations

4.

Zhu, Lin, et al.. (2023). Network Topology Optimization via Deep Reinforcement Learning. IEEE Transactions on Communications. 71(5). 2847–2859. 26 indexed citations

5.

Zhou, Hongyi, et al.. (2022). Quantum Network: Security Assessment and Key Management. IEEE/ACM Transactions on Networking. 30(3). 1328–1339. 31 indexed citations

6.

Cai, Qingpeng, et al.. (2021). Exploration in policy optimization through multiple paths. Autonomous Agents and Multi-Agent Systems. 35(2). 1 indexed citations

7.

Rashid, Tabish, et al.. (2021). Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning.. arXiv (Cornell University). 1 indexed citations

8.

Cai, Qingpeng, et al.. (2020). Multi-Path Policy Optimization. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1001–1009. 1 indexed citations

9.

Cai, Qingpeng, et al.. (2020). Softmax Deep Double Deterministic Policy Gradients. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 33. 11767–11777. 1 indexed citations

10.

Huang, Longbo. (2019). Fast-Convergent Learning-Aided Control in Energy Harvesting Networks. IEEE Transactions on Mobile Computing. 19(12). 2793–2803. 5 indexed citations

11.

Yu, Yue, Jiaxiang Wu, & Longbo Huang. (2019). Double Quantization for Communication-Efficient Distributed Optimization. Neural Information Processing Systems. 32. 4438–4449. 4 indexed citations

12.

Chen, Kun & Longbo Huang. (2018). Timely-Throughput Optimal Scheduling With Prediction. IEEE/ACM Transactions on Networking. 26(6). 2457–2470. 22 indexed citations

13.

Huang, Longbo, Minghua Chen, & Yunxin Liu. (2018). Learning-Aided Stochastic Network Optimization With State Prediction. IEEE/ACM Transactions on Networking. 26(4). 1810–1820. 5 indexed citations

14.

Huang, Longbo. (2017). Intelligence of Smart Systems: Model, Bounds, and Algorithms. IEEE/ACM Transactions on Networking. 25(5). 2960–2973. 7 indexed citations

15.

Huang, Longbo. (2016). The Value-of-Information in Matching With Queues. IEEE/ACM Transactions on Networking. 25(1). 29–42. 8 indexed citations

16.

Fang, Zhixuan, Longbo Huang, & Adam Wierman. (2016). Prices and Subsidies in the Sharing Economy: Profit Versus Welfare. arXiv (Cornell University). 1 indexed citations

17.

Fang, Zhixuan & Longbo Huang. (2016). Boosting Sharing Economy: Social Welfare or Revenue Driven?. arXiv (Cornell University). 1 indexed citations

18.

Fang, Zhixuan & Longbo Huang. (2016). Market Share Analysis with Brand Effect: (Extended Abstract). Adaptive Agents and Multi-Agents Systems. 1335–1336. 1 indexed citations

19.

Yao, Yuan, et al.. (2012). Data centers power reduction: A two time scale approach for delay tolerant workloads. 1431–1439. 182 indexed citations

20.

Liu, Hua, Longbo Huang, Bhaskar Krishnamachari, & Qing Zhao. (2008). A negotiation game for multichannel access in cognitive radio networks. 70. 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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