Yang Wang ( 王 洋 ) Master Complex Environmental Science Exploration Center School of Information and Electronics Beijing Institute of Technology Location: Central Building Room 629, No. 5, South Street, Zhongguancun, Haidian District, Beijing, China Latest | Research Interest | Publications | Patents | Services | Awards | Statistics | Email: yangwang20@bit.edu.cn (prior);      georgeyang2022@163.com [Google Scholar] [IEEE] [GitHub]

  Education

• 2020.9-20.6            M.E. in School of Information and Electronics, Beijing Institute of Technology University.         Supervisor: Prof. Jun Zhang & Zhen Gao
• 2016.9-2020.6        B.E. in School of Information and Electronics, Beijing Institute of Technology University.

  Latest News

• [2021.11] Awarded with "Xiaomi" Outstanding Scholarship (￥20,000) ( 小米特等奖学金 )
• [2021.11] Awarded with Third Prize in Wireless AI Competition( 无线通信AI竞赛三等奖 )
• [2021.08] One Paper was accepted by IEEE Internet of Things Journal ( 论文被IoTJ期刊(IF=9.471)录用 )
• [2021.07] Awarded with Second Prizes of Wireless Big Data Competition ( 第二届无线大数据竞赛-AI无线网络信道重构二等奖 )

  Research Interest

Yang Wang received the B.S. degree in information engineering from the Beijing Institute of Technology, Beijing, China, in July 2016, and the Ph.D. degree in communication and signal processing from the Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, China, in July 2016. From 2014 to 2015, he visited the Communications and Signal Processing Group, Imperial College London, U.K., for nearly one year. He is currently an Assistant Professor with the Advanced Research Institute of Multidisciplinary Science (ARIMS) and the School of Information and Electronics, Beijing Institute of Technology, Beijing, China. His research interests are in wireless communications, with a focus on multicarrier modulations, multiple antenna systems, and sparse signal processing.

• AI Enabling 6G Communications: Data-Driven or Model-Driven Learning?
  
  
  The recent development of various mobile applications, especially those powered by artificial intelligence (AI), has sparked a discussion about the future development of wireless communications. With the global popularization of 5G, industry and science have started their adventure of future 6G design. In wireless communications, Model-Driven [R1] and Data-Driven [R2] methods are proposed in designing channel estimation, precoding, and feedback, while multiple trials on combining AI with Intelligent Reflecting Surfaces (IRS) [R3] or Unmanned Aerial Vehicles (UAVs) [R4] are being explored here.
  Related Work

  [R1] X. Ma, Z. Gao, F. Gao, M. D. Renzo, "Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2388-2406, Aug. 2021.

  [R2] X. Ma and Z. Gao, "Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO," IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5677-5682, May 2020.

  [R3] S. Liu, Z. Gao, J. Zhang, M. D. Renzo and M. -S. Alouini, "Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 9223-9228, Aug. 2020.

  [R4] Y. Wang, Z. Gao, J. Zhang, X. Cao, D. Zheng, Y. Gao, D. W. K. Ng, M. D. Renzo, "Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach," in IEEE Internet of Things Journal.

• RIS-Assisted 6G MIMO Networks: Reflection? or Refraction? or Both?
  
  Reconfigurable intelligent surfaces (RISs) are regarded as one of the most promising techniques for enhancing the spectrum/energy efficiency of wireless systems. Channel estimation of traditional RIS-adied Broadband communication systems has been deeply investigated [R1], [R2], while the holographic RIS, the cutting-edge archetecture of RIS, that supports the next generation Terahertz Massive MIMO communications has also been studied [R3].
  Related Work

  [R1] S. Liu, Z. Gao, J. Zhang, M. D. Renzo and M.-S. Alouini, "Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 9223-9228, Aug. 2020.

  [R2] Z. Wan, Z. Gao and M.-S. Alouini, "Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems," 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6.

  [R3] Z. Wan, Z. Gao, F. Gao, M. D. Renzo, M.-S. Alouini, "Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces," in IEEE Transactions on Communications, vol. 69, no. 7, pp. 4732-4750, July 2021.

• MIMO for Integrated space-air-ground-sea networks
  
  The emerging space-air-ground integrated network (SAGINs) has attracted intensive research and necessitates reliable and efficient aeronautical communications. We have investigated Terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels [R1]. We have also proposed an edge computing paradigm for massive IoT connectivity over High-Altitude Platform Networks (HAPNs) [R2].
  Related Work

  [R1] A. Liao, Z. Gao, D. Wang, H. Wang, H. Yin, D. W. K. Ng, M.-S. Alouini, "Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1741-1767, June 2021.

  [R2] M. Ke, Z. Gao, Y. Huang, G. Ding, D. W. K. Ng, Q. Wu, J. Zhang, "An Edge Computing Paradigm for Massive IoT Connectivity over High-Altitude Platform Networks," arXiv preprint arXiv:2106.13476 (2021). (Pre-Print)

  [R3] Reserved.

• Toward Ultra Large-Scale MIMO: From Millimeter-Wave to Terahertz
  
  The massive MIMO systems provide considerable beamforming gain and fine-grain spatial multiplexing for mmWave and Terahertz communications. Both the hardware design and software design are critical for mmWave/Terahertz massive MIMO. The lens-array and reconfigurable intelligent surface with antenna switching network [R1] [R2] is adopted for mmWave communications to cope with the high hardware complexity and power consumption of traditional massive MIMO. Moreover, in the face of severe spatial wideband effective in the broadband Terahertz massive MIMO, an architecture based on true-time-delay line is presented in [R3]. With the novel hardware architectures above, advanced algorithms, such as ESPRIT [R3] [R4], is also proposed to estimate the large-scale channels via limited pilot overhead.
  Related Work

  [R1] Z. Wan, Z. Gao, B. Shim, K. Yang, G. Mao and M. Alouini, "Compressive Sensing Based Channel Estimation for Millimeter-Wave Full-Dimensional MIMO With Lens-Array," IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 2337-2342, Feb. 2020.

  [R2] Z. Wan, Z. Gao, F. Gao, M. D. Renzo, M.-S. Alouini, "Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces," in IEEE Transactions on Communications, vol. 69, no. 7, pp. 4732-4750, July 2021.

  [R3] A. Liao, Z. Gao, D. Wang, H. Wang, H. Yin, D. W. K. Ng, M.-S. Alouini, "Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1741-1767, June 2021.

  [R4] A. Liao, Z. Gao, H. Wang, S. Chen, M.-S Alouini and H. Yin, "Closed-Loop Sparse Channel Estimation for Wideband Millimeter-Wave Full-Dimensional MIMO Systems," IEEE Transactions on Communications , vol. 67, no. 12, pp. 8329-8345, Dec. 2019.

  Publications

Journals :

[1] Y. Wang, Z. Gao, J. Zhang, X. Cao, D. Zheng, Y. Gao, D. W. K. Ng, M. D. Renzo, "Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach," in IEEE Internet of Things Journal (SCI Q1 Top, IF=9.417, Early Access) [URL] [Code]

Conferences :

[1] Y. Wang and Z. Gao, "Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach," 2021 IEEE/CIC ICCC, 2021, pp. 1-5. [URL] [Slides]

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  Patents

• Y. Wang, K. Ying, S. Liu, Z. Gao, D. Zheng, J. Zhang, 一种物联网数据收集中的无人机轨迹优化方法及系统 Patent No. 202110635429.8, Publication No. CN113382060B.

  Services

Journal Reviewer :

• IEEE Communications Letters
• IEEE Transactions on Vehicular Technology
• IEEE Transactions on Communications
• China Communications

  Awards

• 2019, Ceyear Scholarship, Beijing Institute of Technology | 思仪奖学金
• 2018, Outstanding Undergraduate, Beijing Institute of Technology | 优秀本科毕业生

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