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
- Dr. Xiaoli Ma
- Dr. Zhu Han (University
of Houston)
- Le Liang (graduated)
- Hao Ye (graduated)
- Ziyan He
- Kaiwen Zheng
Collaborators
- University of Houston
- Dr. Geoffrey Ye Li at Imperial College London
Publications and
Codes
- 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.
- 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.
- 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.
- 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)
- 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)
- 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)
- 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)
- 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)
- 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.
- 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.
- R. Yin, Y.-F. Zhang, and
G. Y. Li, “Energy efficiency in LTE-U based small cell systems,” to appear
in IEEE Access.
- 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.
- 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)
- 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.
- 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.
- 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.
- 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.