Collaborative Research: Energy-Efficient Heterogeneous Network Virtualization with Spectrum-Power Trading

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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.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 making use of fractional programming theory, duality methods of mixed integer programming, and graph theoretic tools to find the 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.