Research Assistant Professor Department of Electrical and Electronic Engineering, Department of Computer Science and Engineering   Research Group

Dr. Shuai Wang received the Ph.D. degree in Electrical and Electronic Engineering from the University of Hong Kong in 2018. From 2018 to 2019, he has been a Postdoctoral Fellow at the University of Hong Kong. Currently, he is a Research Assistant Professor with Southern University of Science and Technology. Dr. Wang received the Shenzhen overseas high-level talent program.

Personal Profile


Dr. Shuai Wang is the PI of 4 research projects. He served as the Core Member of the SUSTech Lab of Wireless Communications and System Optimization, the SUSTech Lab for Intelligent Sensing and Unmanned Systems, the SUSTech-Haylion Center for Intelligent Transportation, the SUSTech-FXB Lab for Intelligent Networked Vehicles. Shuai received the Best Paper Award from the IEEE Signal Processing and Computing for Communications, the IEEE International Conference on Communications, the Radio Communications Technology. Shuai received the Exemplary Reviewer Award from the IEEE Transactions on Wireless Communications and the IEEE Wireless Communications Letters. Shuai served as the Session Chair for IEEE ICC 2019 and IEEE WCNC 2021, and the TPC member for IEEE PIMRC 2021. Shuai has published 21 papers in top IEEE journals (JCR Q1 Q2) and 17 papers in major IEEE conferences. Shuai is the primary contributor to CARLA INVS, Robo WET, Robo Edge simulation platforms. Shuai has participated in 3GPP standardization with Hitachi, IEEE standardization with Huawei, and course design with Shenzhen-FXB Co. Ltd. Currently, his interest includes communications, machine learning, and autonomous driving.


digital communications、numerical methods and optimization

Publications Read More

 First and Corresponding Author Journal Paper:

[1] S. Wang, Y. Hong, R. Wang, Q. Hao, Y.-C. Wu, and D. W. K. Ng, “Edge federated learning via unit-modulus over-the-air computation,”  IEEE Transactions on Communications, 2021. [Online]. Available:

[2] S. Wang, Y.-C. Wu, M. Xia, R. Wang, and H. V. Poor, “Machine intelligence at the edge with learning centric power allocation,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, Jul. 2020. (this achievement has received best paper awards from IEEE ICC and IEEE SPCC)

[3] S. Wang, M. Wen, M. Xia, R. Wang, Q. Hao and Y.-C. Wu, “Angle aware user cooperation for secure massive MIMO in Rician fading channel,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 2182-2196, Sept. 2020.

[4] S. Wang, M. Xia, and Y.-C. Wu, “Backscatter data collection with unmanned ground vehicle: Mobility management and power allocation,” IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2314-2328, Apr. 2019.

[5] S. Wang, M. Xia, and Y.-C. Wu, “Multicast wirelessly powered network with large number of antennas via first-order method,” IEEE Transactions on Wireless Communications, vol. 17, no. 6, pp. 3781-3793, Jun. 2018.

[6] S. Wang, M. Xia, and Y.-C. Wu, “Space-time signal optimization for SWIPT: Linear versus nonlinear energy harvesting model,” IEEE Communications Letters, vol. 22, no. 2, pp. 408-411, Feb. 2018.

[7] S. Wang, M. Xia, K. Huang, and Y.-C. Wu, “Wirelessly powered two-way communication with nonlinear energy harvesting model: Rate regions under fixed and mobile relay,” IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 8190-8204, Dec. 2017.

[8] S. Wang, M. Xia and Y.-C. Wu, “Multi-pair two-way relay network with harvest-then-transmit users: resolving pairwise uplink-downlink coupling,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 8, pp. 1506-1521, Dec. 2016.

[9] S. Wang, Z. Wen, and L. Liu, “High speed QPP generator with optimized parallel architecture in LTE-Advanced,” International Journal of Advancements in Computing Technology, vol. 4, no. 23, pp. 355-364, Dec. 2012.

[10] L. Zhou, Y. Hong, S. Wang*(corresponding author), R. Han, D. Li, R. Wang, and Q. Hao, “Learning centric wireless resource allocation for edge computing: Algorithm and experiment,” IEEE Transactions on Vehicular Technology, vol. 70, no, 1, pp. 1035-1040, Jan. 2021.

[11] S. Huang, S. Wang*(corresponding author), R. Wang, M. Wen, and K. Huang, “Reconfigurable intelligent surface assisted mobile edge computing with heterogeneous learning tasks”, IEEE Transactions on Cognitive Communications and Networking, to appear, Jan. 2021. DOI: 10.1109/TCCN.2021.3056707.

[12] D. Liu, S. Wang*(corresponding author), Z. Wen, L. Cheng, M. Wen, and Y.-C. Wu, “Edge learning with unmanned ground vehicle: Joint path, energy, and sample size planning,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2959-2975, Feb. 2021.

[13] Z. Wen, S. Wang*(corresponding author), X. Liu, and J. Zou, “Joint relay-user beamforming design in full-duplex two-way relay channel,” IEEE Transactions on Vehicular Technology, vol. 66, no. 3, pp. 2874-2879, Mar. 2017.

Other papers:

[14] S. Wang, R. Wang, Q. Hao, Y.-C. Wu, and H. V. Poor, “Learning centric power allocation for edge intelligence,” IEEE ICC, Dublin, Ireland, Jun. 2020. (Best Paper Award, 15/2100)

[15] S. Yu, X. Chen, S. Wang, L. Pu, and D. Wu, “An edge computing-based photo crowdsourcing framework for real-time 3D reconstruction,” to appear in IEEE Transactions on Mobile Computing, early access, Jun. 2020. DOI: 10.1109/TMC.2020.3007654.

[16] L. Cheng, X. Tong, S. Wang, Y.-C. Wu, and H. V. Poor, “Learning nonnegative factors from tensor data: Probabilistic modeling and inference algorithm,” IEEE Transactions on Signal Processing, early access, Feb. 2020. DOI: 10.1109/TSP.2020.2975353.

[17] Z. Li, S. Wang, P. Mu, and Y.-C. Wu, “Probabilistic constrained secure transmission: Variable rate design and performance analysis,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2543-2557, Apr. 2020.

[18] Q. Wu, J. Lu, P. Wu, S. Wang, L. Chen, and M. Xia, “Edge Learning: technologies, applications, and opportunities,” invited paper in Radio Communications Technology, vol. 46, no. 1, pp. 6-25, Jan. 2020.

[19] Z. Wen, X. Liu, N. C. Beaulieu, R. Wang, and S. Wang, “Joint source and relay beamforming design for full-duplex MIMO AF relay SWIPT systems,” IEEE Communications Letters, vol. 20, no. 2, pp. 320-323, Feb. 2016.

[20] Y. Chen, Z. Wen, N. C. Beaulieu, S. Wang, and J. Sun, “Joint source-relay design in a MIMO two-hop power-splitting based relaying network,” IEEE Communications Letters, vol. 19, no. 10, pp. 1746-1749, Oct. 2015.

[21] Z. Wen, S. Wang, C. Fan, and W. Xiang, “Joint transceiver and power splitter design over two-way relaying channel with lattice codes and energy harvesting,” IEEE Communications Letters, vol. 18, no. 11, pp. 2039-2042, Nov. 2014.

[22] Z. Xu, D. Liu, S. Wang, and Z. Wen, “Joint beamforming and power-splitting optimization for SWIPT-enabled MISO full-duplex two-way cooperative NOMA systems,” Physical Communications, to appear, Dec. 2020.

[23] T. Zhang, Y. Xu, S. Wang, M. Wen, and R. Wang, “On secure degrees of freedom of the MIMO interference channel with local output feedback,” IEEE Internet of Things Journal, to appear, 2021.

[24] Q. Li, M. Wen, S. Wang, G. C. Alexandropoulos, and Yik-Chung Wu, “Space shift keying with reconfigurable intelligent surfaces: Phase configuration designs and performance analysis,” IEEE Open Journal of the Communications Society, vol. 2, pp. 322-333, Oct. 2020.

[25] Z. Zhang, S. Wang, Y. Hong, L. Zhou, and Q. Hao, “Distributed dynamic map fusion via federated learning for intelligent networked vehicles,” in Proc. IEEE ICRA’2021, Xi’an, China, May 2021.

[26] G. Li, J. Li, S. Wang, R. Wang, X. Peng, and T. X. Han, “Wireless sensing with deep spectrogram network and primitive based autoregressive hybrid channel model,’’ in Proc. IEEE SPAWC, Sept. 2021. (invited paper)

[27] M. Zhang, G. Zhu, S. Wang, J. Jiang, C. Zhong, and S. Cui, “Accelerating federated edge learning via optimized probabilistic device scheduling,’’ in Proc. IEEE SPAWC, Sept. 2021. (invited paper)

[28] Z. Li, S. Wang, M. Wen, and Y.-C. Wu, “Secure Massive RIS aided Multicast with Uncertain CSI: Energy-Efficiency Maximization via Accelerated First-Order Algorithms,” submitted to IEEE for possible publication, 2021.

[29] Z. Li, S. Wang, Y. Li, M. Wen, Y.-C. Wu, and H. V. Poor “Resource optimization for RIS empowered wireless networks: Past, present, and future, ” submitted to IEEE for possible publication, 2021.

[30] X. Li, S. Wang, G. Zhu, Z. Zhou, K. Huang, and Y. Gong,“Data partition and rate control for learning and energy efficient edge intelligence,” submitted to IEEE for possible publication, 2021.

[31] T. Zhang, S. Wang, G. Li, F. Liu, G. Zhu, R. Wang, “Accelerating edge intelligence via integrated sensing and communication,” submitted to IEEE for possible publication, 2021.

[32] H. Zhao, F. Ji, Q. Li, Q. Guan, S. Wang, and Miaowen Wen, “Federated meta-learning enhanced acoustic radio cooperative framework for ocean of things,” submitted to IEEE for possible publication, 2021.

[33] S. Huang, S. Wang, R. Wang, and K. Huang, “Accelerating federated edge learning via topology optimization,” submitted to IEEE for possiblepublication, 2021.

News More

  • Two papers have been accepted by IEEE SPAWC 2021! Congratulations to Guoliang!

  • One paper has been accepted to IEEE ICRA 2021! (with Zijian, Yuncong, Liangkai, and Prof. Qi Hao)

  • One paper has been accepted to IEEE Transactions on Cognitive Communications and Networking! (with Shanfeng, Prof. Rui Wang, Prof. Miaowen Wen, and Prof. Kaibin Huang)


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