Sheng Zhou

12.9k total citations · 5 hit papers
343 papers, 8.8k citations indexed

About

Sheng Zhou is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Sheng Zhou has authored 343 papers receiving a total of 8.8k indexed citations (citations by other indexed papers that have themselves been cited), including 232 papers in Electrical and Electronic Engineering, 199 papers in Computer Networks and Communications and 35 papers in Artificial Intelligence. Recurrent topics in Sheng Zhou's work include Advanced MIMO Systems Optimization (129 papers), Cooperative Communication and Network Coding (65 papers) and Advanced Wireless Network Optimization (60 papers). Sheng Zhou is often cited by papers focused on Advanced MIMO Systems Optimization (129 papers), Cooperative Communication and Network Coding (65 papers) and Advanced Wireless Network Optimization (60 papers). Sheng Zhou collaborates with scholars based in China, United States and Canada. Sheng Zhou's co-authors include Zhisheng Niu, Georgios B. Giannakis, Yuxuan Sun, Zhiyuan Jiang, Jie Xu, Jie Gong, Qian Liu, Xueying Guo, Pengfei Xia and Dongxu Cao and has published in prestigious journals such as Advanced Materials, Scientific Reports and IEEE Transactions on Information Theory.

In The Last Decade

Sheng Zhou

316 papers receiving 8.6k citations

Hit Papers

Cross-Layer Combining of Adaptive Modulation and Coding W... 2004 2026 2011 2018 2004 2017 2018 2019 2020 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
Sheng Zhou China 47 6.1k 5.9k 1.2k 745 649 343 8.8k
Zhisheng Niu China 48 6.4k 1.0× 6.1k 1.0× 973 0.8× 579 0.8× 656 1.0× 336 8.5k
Y. Thomas Hou United States 48 5.6k 0.9× 6.4k 1.1× 1.7k 1.5× 1.1k 1.5× 486 0.7× 329 9.6k
Shigang Chen United States 45 3.2k 0.5× 5.2k 0.9× 1.8k 1.5× 682 0.9× 194 0.3× 319 8.7k
Pingyi Fan China 39 4.3k 0.7× 3.4k 0.6× 579 0.5× 219 0.3× 1.3k 2.0× 434 6.2k
Wei Chen China 35 4.7k 0.8× 3.5k 0.6× 777 0.7× 284 0.4× 1.0k 1.6× 448 6.8k
Cailian Chen China 36 2.0k 0.3× 3.0k 0.5× 763 0.7× 257 0.3× 412 0.6× 439 5.2k
Shibo He China 44 3.4k 0.6× 3.5k 0.6× 1.1k 0.9× 450 0.6× 718 1.1× 259 7.0k
Lei Guo China 47 2.3k 0.4× 4.3k 0.7× 1.4k 1.2× 1.3k 1.7× 903 1.4× 286 7.2k
Chadi Assi Canada 52 7.1k 1.2× 5.5k 0.9× 739 0.6× 1.0k 1.4× 1.9k 2.9× 457 10.6k
Chen‐Nee Chuah United States 40 3.2k 0.5× 4.9k 0.8× 1.1k 1.0× 495 0.7× 219 0.3× 231 7.0k

Countries citing papers authored by Sheng Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Sheng Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sheng Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Sheng Zhou. A scholar is included among the top collaborators of Sheng Zhou 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 Sheng Zhou. Sheng Zhou 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.
Chen, T., et al.. (2025). Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning. IEEE Transactions on Wireless Communications. 24(11). 9373–9390. 5 indexed citations
2.
Zhou, Sheng, et al.. (2025). Lower-Extremity Muscle Strength Symmetry Assessment Through Isokinetic Dynamometry. Life. 15(2). 318–318.
3.
Weng, X., et al.. (2025). Trajectory planning strategy for obstacle avoidance based on D-APF. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 47(2). 1 indexed citations
4.
Xu, Yining & Sheng Zhou. (2024). Resource Allocation for Channel Estimation in Reconfigurable Intelligent Surface-Aided Multi-Cell Networks. Journal of Communications and Information Networks. 9(1). 64–79.
5.
Qiu, Weiqiang, Sheng Zhou, Xiaoying Lv, et al.. (2023). Application Prospect, Development Status and Key Technologies of Shared Energy Storage toward Renewable Energy Accommodation Scenario in the Context of China. Energies. 16(2). 731–731. 11 indexed citations
6.
Qiu, Bo, Ying Liang, Xiaorong Tang, et al.. (2023). Role of TRPV1 in electroacupuncture‐mediated signal to the primary sensory cortex during regulation of the swallowing function. CNS Neuroscience & Therapeutics. 30(3). e14457–e14457. 10 indexed citations
8.
Sun, Yuxuan, et al.. (2022). Coded Computation Across Shared Heterogeneous Workers With Communication Delay. IEEE Transactions on Signal Processing. 70. 3371–3385. 9 indexed citations
9.
Shi, Wenqi, Yuxuan Sun, Sheng Zhou, & Zhisheng Niu. (2021). Device Scheduling and Resource Allocation for Federated Learning under Delay and Energy Constraints. 596–600. 3 indexed citations
10.
Sun, Yuxuan, Sheng Zhou, Zhisheng Niu, & Denız Gündüz. (2021). Dynamic Scheduling for Over-the-Air Federated Edge Learning With Energy Constraints. IEEE Journal on Selected Areas in Communications. 40(1). 227–242. 97 indexed citations
11.
Sun, Yuxuan, et al.. (2020). Edge Learning with Timeliness Constraints: Challenges and Solutions. IEEE Communications Magazine. 58(12). 27–33. 22 indexed citations
12.
Shi, Wenqi, Sheng Zhou, & Zhisheng Niu. (2020). Device Scheduling with Fast Convergence for Wireless Federated Learning. 1–6. 160 indexed citations
13.
Shi, Wenqi, Sheng Zhou, Zhisheng Niu, Miao Jiang, & Lu Geng. (2020). Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning. IEEE Transactions on Wireless Communications. 20(1). 453–467. 270 indexed citations breakdown →
14.
Wang, Guangchao, Sheng Zhou, Shan Zhang, Zhisheng Niu, & Xuemin Shen. (2020). SFC-Based Service Provisioning for Reconfigurable Space-Air-Ground Integrated Networks. IEEE Journal on Selected Areas in Communications. 38(7). 1478–1489. 128 indexed citations
15.
Liu, Leibo, et al.. (2020). Energy- and Area-Efficient Recursive-Conjugate-Gradient-Based MMSE Detector for Massive MIMO Systems. IEEE Transactions on Signal Processing. 68. 573–588. 34 indexed citations
16.
Liu, Leibo, et al.. (2019). A 2.92-Gb/s/W and 0.43-Gb/s/MG Flexible and Scalable CGRA-Based Baseband Processor for Massive MIMO Detection. IEEE Journal of Solid-State Circuits. 55(2). 505–519. 30 indexed citations
17.
Shi, Wenqi, Yunzhong Hou, Sheng Zhou, et al.. (2019). Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning. arXiv (Cornell University). 1–6. 60 indexed citations
18.
Liu, Leibo, et al.. (2019). Near-Optimal MIMO-SCMA Uplink Detection With Low-Complexity Expectation Propagation. IEEE Transactions on Wireless Communications. 19(2). 1025–1037. 22 indexed citations
19.
Liu, Leibo, et al.. (2017). A 1.58 Gbps/W 0.40 Gbps/mm2 ASIC Implementation of MMSE Detection for $128\times 8~64$ -QAM Massive MIMO in 65 nm CMOS. IEEE Transactions on Circuits and Systems I Regular Papers. 65(5). 1717–1730. 39 indexed citations
20.
Zhou, Sheng. (2001). The Assessment of Toddler Temperament Scale.. 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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