Excellence in Research: Research Capacity and Partnerships Building in Next-Generation Communication Ecosystems with Vertical Intelligence

NSF Award Number: 2302469

Abstract

The evolution of next-generation wireless communications known as the sixth generation (6G) will enable not only super-fast and ultra-reliable communications services, but also new use cases and intelligence to benefit society and redefine how we live and work. In this project, a team of researchers and educators from two HBCUs, Prairie View A&M University and Texas Southern University, collaborating with the team of the University of Houston, propose a comprehensive plan to enhance research capacity in the next-generation communications ecosystem with vertical intelligence and to build strong partnership. The proposed project will leverage the research capabilities and existing collaborations among the three participating institutions, and greatly enhance the research capacity and educational excellence at all institutions. Partnerships with industry and government will engage HBCUs in future critical 6G and beyond infrastructure and maintain a broad workforce. The proposed research will explore theoretical advancement as well as experimental design. If successful, the proposed research will help accelerate the development and commercialization of promising technologies for 6G and ensure continued U.S. leadership in the telecommunications industry. The education goals include enabling broad participation of students in cutting-edge research, building a pipeline to attract and retain them to pursue careers in STEM, and training future workforce in critical areas especially 6G for the nation.

The proposed research will target integration of 6G terrestrial cellular networks with aerial access networks (AANs) with many satellites, high altitude platforms (HAPs), and unmanned air vehicles (UAVs) through integrated sensing, communications, and computing to gain vertical intelligence in the next-generation communications ecosystem. Three research thrusts are proposed: 1) Novel dynamic, stochastic and distributed optimization methods will be investigated for robust resource planning and allocation and mobility management; 2) A hierarchical sensing architecture will be designed to provide mutual benefits between communications and sensing; 3) Efficient privacy-preserving edge computing and computation offloading strategies will be studied to jointly optimize communications and computing. The proposed framework and strategies will not only optimize resource usage but also integrate sensing, communications, and computing seamlessly, while protecting user privacy and data security. The proposed methods will be validated through extensive simulations and experiments using USRPs and the NSF POWDER, COSMOS and AERPAW testbeds. This project will significantly enhance research, education, and outreach in all participating institutions and enable broad participation of students in advanced research and provide the next-generation workforce in 6G and beyond.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Principal Investigators

  • Dr. Lijun Qian
  • Dr. Zhu Han
  • Dr. Wei Li
  • Dr. Xiangfang Li
  • Dr. Ahmed Ahmed
  • Salma Sultana

    • Publications

    1. Tong, Jingwen and Zhang, Hongliang and Fu, Liqun and Leshem, Amir and Han, Zhu "Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-player Bandits" IEEE Transactions on Communications , v.70 , 2022. [PDF]
    2. Li, Lixin and Ma, Donghui and Ren, Huan and Wang, Peijue and Lin, Wensheng and Han, Zhu "Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization" IEEE Transactions on Green Communications and Networking , v.6 , 2022. [PDF]
    3. Zhao, Jingjing and Yu, Lanchenhui and Cai, Kaiquan and Zhu, Yanbo and Han, Zhu "RIS-Aided Ground-Aerial NOMA Communications: A Distributionally Robust DRL Approach" IEEE Journal on Selected Areas in Communications , v.40 , 2022. [PDF]
    4. Sun, Shiyuan and Wang, Tengjiao and Yang, Fang and Song, Jian and Han, Zhu "Intelligent Reflecting Surface-Aided Visible Light Communications: Potentials and Challenges" IEEE Vehicular Technology Magazine , v.17 , 2022. [PDF]
    5. Elmossallamy, Mohamed A. and Sultan, Radwa and Seddik, Karim G. and Li, Geoffery Ye and Han, Zhu "Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels" Intelligent and Converged Networks , v.3 , 2022. [PDF]
    6. Zhang, Haobo and Zhang, Hongliang and Di, Boya and Bian, Kaigui and Han, Zhu and Song, Lingyang "MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-User Wireless Indoor Localization" IEEE Transactions on Wireless Communications , v.20 , 2021. [PDF]
    7. Liu, Yiming and Liu, Erwu and Wang, Rui and Han, Zhu and Lu, Binyu "Asymptotic Achievability of the Cramér-Rao Lower Bound of Channel Estimation for Reconfigurable Intelligent Surface Aided Communication Systems" IEEE Wireless Communications Letters , v.10 , 2021. [PDF]
    8. Cao, Xuelin and Yang, Bo and Zhang, Hongliang and Huang, Chongwen and Yuen, Chau and Han, Zhu "Reconfigurable-Intelligent-Surface-Assisted MAC for Wireless Networks: Protocol Design, Analysis, and Optimization" IEEE Internet of Things Journal , v.8 , 2021. [PDF]
    9. Cao, Xuelin and Yang, Bo and Huang, Chongwen and Yuen, Chau and Zhang, Yan and Niyato, Dusit and Han, Zhu "Converged Reconfigurable Intelligent Surface and Mobile Edge Computing for Space Information Networks" IEEE Network , v.35 , 2021. [PDF]
    10. Cao, Xuelin and Yang, Bo and Huang, Chongwen and Alexandropoulos, George C. and Yuen, Chau and Han, Zhu and Poor, H. Vincent and Hanzo, Lajos "Massive Access of Static and Mobile Users via Reconfigurable Intelligent Surfaces: Protocol Design and Performance Analysis" IEEE Journal on Selected Areas in Communications , v.40 , 2022. [PDF]
    11. Zhang, Shuhang and Zhang, Hongliang and Di, Boya and Tan, Yunhua and Di Renzo, Marco and Han, Zhu and Vincent Poor, H. and Song, Lingyang "Intelligent Omni-Surfaces: Ubiquitous Wireless Transmission by Reflective-Refractive Metasurfaces" IEEE Transactions on Wireless Communications , v.21 , 2022. [PDF]
    12. Hu, Jingzhi and Zhang, Hongliang and Bian, Kaigui and Renzo, Marco Di and Han, Zhu and Song, Lingyang "MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning" IEEE Journal on Selected Areas in Communications , v.39 , 2021. [PDF]
    13. Soric, Jason and Ra’di, Younes and Farfan, Diego and Alu, "Radio-transparent dipole antenna based on a metasurface cloak," Nat Commun, v. 13, 2022. [PDF]
    14. Nagulu, Aravind and Ni, Xiang and Kord, Ahmed and Tymchenko, Mykhailo and Garikapati, Sasank and Alu, "Chip-scale Floquet topological insulators for 5G wireless systems" Nature Electronics, v. 5, 2022. [PDF]
    15. Nguyen, Nhan Thanh and Nguyen, Ly V. and Huynh-The, Thien and Nguyen, Duy H. and Lee Swindlehurst, A. and Juntti, Markku. "Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems" in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021. [PDF]
    16. A. L. Swindlehurst, G. Zhou, R. Liu, C. Pan and M. Li, "Channel Estimation With Reconfigurable Intelligent Surfaces--A General Framework," in Proceedings of the IEEE, 2022. [PDF]
    17. G. Zhou, C. Pan, H. Ren, P. Popovski and A. L. Swindlehurst, "Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems," in IEEE Transactions on Signal Processing, v. 70, 2022. [PDF]
    18. Liu, Rang and Li, Ming and Luo, Honghao and Liu, Qian and Swindlehurst, A. Lee (2023). Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions. IEEE Wireless Communications, v. 30, pp. 50-57, 2023. [PDF]
    19. Liu, Rang and Li, Ming and Swindlehurst, A. Lee (2022). Joint Beamforming and Reflection Design for RIS-assisted ISAC Systems. 022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, 2022. [PDF]
    20. Pan, Cunhua and Zhou, Gui and Zhi, Kangda and Hong, Sheng and Wu, Tuo and Pan, Yijin and Ren, Hong and Renzo, Marco Di and Lee Swindlehurst, A. and Zhang, Rui and Zhang, Angela Yingjun (2022). An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems. IEEE Journal of Selected Topics in Signal Processing, v. 16, no. 5, pp. 883-917. 2022. [PDF]
    21. Peng, Zhendong and Zhou, Gui and Pan, Cunhua and Ren, Hong and Swindlehurst, A. Lee and Popovski, Petar and Wu, Gang (2022). Channel Estimation for RIS-Aided Multi-User mmWave Systems With Uniform Planar Arrays. IEEE Transactions on Communications, v. 70. no. 12, pp. 8105-8122, 2022. [PDF]
    22. Tabeshnezhad, Azadeh and Swindlehurst, A. Lee and Svensson, Tommy (2023). RIS-Assisted Interference Mitigation for Uplink NOMA. 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, United Kingdom, 2023. [PDF]
    23. Wang, Fangzhou and Li, Hongbin and Swindlehurst, A. Lee (2023). Clutter Suppression for Target Detection Using Hybrid Reconfigurable Intelligent Surfaces. 2023 IEEE Radar Conference (RadarConf23), San Antonio, TX, USA, 2023. [PDF]
    24. Wang, Fangzhou and Swindlehurst, A. Lee (2023). Hybrid Ris-Assisted Interference Mitigation for Spectrum Sharing. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023. [PDF]