Xiaoli Ma (Previous PI Prof. Geoffrey Ye Li)

Professor @ Georgia Tech

NSF

Energy-Efficient Heterogeneous Network Virtualization with Spectrum-Power Trading

Award Number: 1731017

Project Description

To accommodate the significant growth in wireless traffic and services, it is beneficial and important to decouple wireless network infrastructure and type of the services, resulting in network virtualization where different types of services can share the same infrastructure and network utilization. Moreover, wireless network virtualization makes it easy to migrate spectrum power trading to balance traffic flow in heterogeneous networks and effectively reduce network energy consumption. Those facts motivate us to investigate the spectrum-power trading mechanism under the heterogeneous network virtualization frameworks. The proposed research will improve design methodology by providing new perspectives and solution concepts. The unique angles of the proposed cross-layer approaches integrate interdisciplinary and transformative concepts in different areas, including economics, decision theory, optimization, and social science. The results will be publicly available through publications and open source software release, to facilitate technology dissemination. The research can also significantly boost the quality of undergraduate and graduate programs, through curriculum development and engaging students in related research. The outreach activities will encourage high school students, especially female and minority students, to pursue science and engineering careers

Synopsis

With network virtualization, wireless equipment, such as base stations (BSs), might be controlled by individuals instead of by one or a few service providers. As a result, network economy, especially for spectrum-power trading, has to be carefully investigated to implement network virtualization. In this cross disciplinary proposal, the intellectual merits include: 1) The network virtualization framework will be constructed to facilitate the spectrum-power trading. The separation of physical and virtual networks requires the game theoretical analysis due to different interests for different scenarios. 2) The key challenge is the trading strategy, i.e., how to efficiently allocate physical resources to different virtual wireless networks, including BS association, spectrum and power allocation. The problems will be approached through exploiting its special structures and make use of fractional programming theory, duality methods of mixed integer programming, and graph theoretic tools to find good solutions with low-complexity. 3) The device-to-device (D2D) communications can provide services with low latency and reduced power consumption. Spectrum and power trading will be performed in virtualized D2D networks for further performance improvement. 4) To facilitate network virtualization, spectrum-power trading game theoretical approaches, such as auction theory and contract theory approaches, will be studied. Furthermore, big data scale optimization algorithms will be developed to conduct parallel computing.

Personnel

  • Faculty
    1. Dr. Xiaoli Ma
    2. Dr. Zhu Han (University of Houston)
  • Graduate Students
    1. Le Liang (graduated)
    2. Hao Ye (graduated)
    3. Ziyan He
    4. Kaiwen Zheng

 

Collaborators

  • University of Houston
  • Dr. Geoffrey Ye Li at Imperial College London

 

Publications and Codes

  1. J.-P. Niu, G. Y. Li, Y.-Y. Li, D.-Y. Fang, X.-J. Chen,J. Zheng, and X. Li, “Resource allocation in reverse TDD wireless backhaul HetNets with 3D massive antennas,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 30 – 33, February 2018.
  2. L. Lu, D.-W. He, Q.-X. Xie, G. Y. Li, X.-X. Yu, “Graph-based path selection and power allocation for DF relay-aided transmission,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 138 – 141, February 2018.
  3. R. Yin, G. Y. Li, and A. Maaref, “Spatial reuse for coexisting LTE and WiFi systems in unlicensed spectrum,” IEEE Transactions on Wireless Communications, vol. 17, no. 2, pp. 1187 – 1198, February 2018.
  4. H. Ye, L. Liang, G. Y. Li, L. Lu, J.-B. Kim, and M. Wu, “Machine learning for vehicular networks: Recent advances and application examples,” IEEE Vehicular Technology Magazine. vol. 13, no. 2, pp. 94 – 101, June 2018. (once the most popular article of all papers in the journal from IEEE Xplore)
  5. L. Liang, S.-J. Xie, G. Y. Li, Z. Ding, and X.-X. Yu, “Graph-based resource sharing for vehicular communication,” IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4579 – 4592, July 2018. (once a popular article of all papers in the journal from IEEE Xplore)
  6. Z.-J. Zhang, L.-Y. Song, Z. Han, G. Y. Li, and V. H. Poor, “Game theory for big data processing: multi-leader and multi-follower game-based ADMM,” IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 3933 – 3945, August 2018. (once a popular article of all papers in the journal from IEEE Xplore)
  7. Z.-J. Zheng, L.-Y. Song, Z. Han, G. Y. Li, and V. H. Poor, “Fast Stackelberg game for proactive caching in large-scale mobile edge networks,” IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5198 – 5211, August 2018. (once a popular article of all papers in the journal from IEEE Xplore)
  8. H.-X. Peng, L. Liang, X.-M. Shen, and G. Y. Li, “Vehicular communications: a network layer perspective,” to appear/early access in IEEE Transactions on Vehicular Technology. (once a popular article of all papers in the journal from IEEE Xplore)
  9. B.-Y. Di, H.-L. Zhang, L.-Y. Song, Y.-H. Li, G. Y. Li, H. V. Poor, “Integrating terrestrial-satellite networks into 5G and beyond for data offloading,” IEEE Transactions on Wireless Communications, Vol,18, no. 1 , Jan. 2019.
  10. Z.-J. Qin, F. Y. Li, G. Y. Li, J. A. McCann, and Q. Ni, “Low-power wide-area networks for green IoT,” to appear in IEEE Wireless Communications.
  11. R. Yin, Y.-F. Zhang, and G. Y. Li, “Energy efficiency in LTE-U based small cell systems,” to appear in IEEE Access.
  12. X.-W. Zhou, M.-X. Sun, G. Y. Li, and B.-H. F. Juang, “Intelligent wireless communications enabled by cognitive radio and machine learning,” IEEE China Communications, vol 15, no. 12 , Dec. 2018.
  13. H. Ye, G. Y. Li, B.-H. F. Juang, “Deep reinforcement learning based resource allocation for V2V communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, March 2019. (source codes)
  14. A. Frøreytlog, T. Foss, O. Bakker, G. Jevne, M. A. Haglund, F. Y. Li, J. Oller, and G. Y. Li, “Ultra-low power wake-up radio for 5G IoT,” IEEE Communication Magazine., vol. 57, no. 3, pp. 111-117, Mar. 2019.
  15. Y.-W. Huang, Y. Liu, and G. Y. Li, “Energy efficiency of distributed antenna systems with wireless power transfer,” IEEE Journal on Selected Areas in Communications , vol. 37, no. 1, pp. 89-99, Jan. 2019.
  16. C.-J. Zheng, D.-Q. Feng, S.-L. Zhang, X.-G. Xia, G.-B. Qian, and G. Y. Li, “Energy efficient V2X-enabled communications in cellular networks,” IEEE Transactions on Vehicular Technology., vol. 68, no. 1, pp. 554-564, Jan. 2019.
  17. L. Liang, H. Ye, and G. Y. Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,” to appear in IEEE Journal on Selected Areas in Communications. (source codes)

 

Broader Impacts

The research findings are likely to significantly improve the energy efficiency of the internet of things and vehicular networks. In addition, with the machine learning approach applied, high quality-of-service can be guaranteed for various applications of the IoT.

Educational Activities

The PI has highly committed to teaching and integrating research with STEM education. The PI has restructured wireless communication courses currently taught to engage students in more hands-on projects comprised of intensive experiments and programming with emphasis on the vehicular networks and IoT.