• Personnel:

    • PI: Sujit Dey

    • Ph.D. Students: Yu-Jen Ku, Po-Han Chiang

  • Description:

               The proliferation of mobile data traffic will lead to drastically increasing energy consumption and carbon dioxide equivalent (CO2e) emission in future cellular networks. Since base stations (BSs) consume 80% of the total power in cellular networks, the need to reduce the power consumption of the BSs is crucial for energy-efficient cellular networks. Renewable energy (RE) is a promising solution to reduce grid energy consumption and carbon emissions of BSs. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature, resulting in low savings in grid energy. While we refer to grid energy consumption in this report, we note such techniques will also lead to reduction in diesel consumption for off-grid or poor-grid BSs.
               To address the mismatch between fluctuating harvested RE at the BS and its power consumption need due to mobile traffic demand, we propose solutions to 1) intra-cell scenario: adapting BS resource allocation, and hence its power consumption, depending on the amount of harvested RE at the BS, and channel condition and buffer level of user equipment (UE) during data download, 2) inter-cell scenario: adapting user association among neighboring BSs according to the geographic distribution of harvested RE and traffic demand by UEs in heterogeneous networks comprising of small cells and macro BSs. Our objective is to maximize utilization of harvested RE at the BSs with minimal use of energy storage (battery), thereby enabling the use of renewable energy feasible at low-cost.

    Project 1: Renewable Energy-Aware Video Download in Cellular Networks

              In project 1, our objective is to minimize grid energy consumption of BSs as well as maximize QoS of users while optimally utilizing harvested RE. The focus of the proposed approach will be to modulate BS energy consumption with the use of both energy storage at the BS and data buffer of the UEs in order to mitigate the mismatch between harvested RE and BS power consumption. The proposed approach transforms the excess RE to excess data delivered to the users and stored in UE data buffer, to draw from in periods when harvested RE is less than BS requirements to reduce grid energy consumption. Though the proposed approach is applicable to any application that utilizes data storage of user devices, we consider the problem for video download/streaming, which will constitute a very large proportion of mobile traffic and hence the BS energy consumption.

    RE UE buffer.png


    Project 2: Quality of Service Optimization for Vehicular Edge Computing with Solar-Powered Road Side Units

          The objective of project 2 is to demonstrate the feasibility of small cells powered only by renewable energy, maximizing the longevity of such RE-powered small cells while ensuring satisfaction of application QoS and minimize service outage of edge-computing based services performed by the MECs associated with such small cells. We decided to focus on a demanding use case for small cells with MEC:  vehicular edge computing for future assisted and autonomous vehicles.
           In this emerging use case, solar-powered roadside units (SRSUs), consisting of small cells with MEC, is to be used to provide ultra-low latency services to passing vehicles. We aim to develop techniques to minimize the QoS loss in terms of service outage for the vehicular edge computing applications. Our proposed approach is to mitigate the temporal and spatial mismatch of the solar power generation and power consumption of SRSUs through SRSUs’ battery charging/discharging management and vehicle association strategies. The proposed approach 1) schedules charging/discharging of the batteries of SRSUs based on prediction of the solar generation and the power consumption profile of SRSUs, 2) offloads users (vehicles) associated with SRSUs which are under power deficiency to neighboring SRSUs, and 3) minimizes the power consumption of each SRSU by communication and computation resource allocation given offloaded workloads. 

    vehicle scenario 3.png

  • Publications:

    • Y. Ku, P. Chiang and S. Dey, "Quality of Service Optimization for Vehicular Edge Computing with Solar-Powered Road Side Units," 2018 27th International Conference on Computer Communication and Networks (ICCCN), Hangzhou, 2018, pp. 1-10 pdf

    • P. Chiang, R. B. Guruprasad and S. Dey, "Optimal Use of Harvested Solar, Hybrid Storage and Base Station Resources for Green Cellular Networks," in IEEE Transactions on Green Communications and Networking, vol. 2, no. 3, pp. 707-720, Sept. 2018 pdf

    • P. H. Chiang, R. Guruprasad and S. Dey, "Renewable energy-aware video download in cellular networks," 2015 IEEE 26th PIMRC, Hong Kong, 2015, pp. 1622-1627 pdf

  • Outreach:

    • Posters and Presentations:

      Y. Ku, "Quality of Service Optimization for Vehicular Edge Computing with Solar-Powered Road Side Units," 2018 San Diego Regional Proving Ground (RPG) Consortium meeting, June 20, 2018

      Y. Ku, "Quality of Service Optimization for Vehicular Edge Computing with Solar-Powered Road Side Units," 2018 UC San Diego Center of Wireless Communications 5G and Beyond Forum (http://5g.ucsd.edu/), May 31, 2018

      P. Chiang, "Renewable energy-aware video download in cellular networks," 2017 UC San Diego Center of Wireless Communications 5G and Beyond Forum(http://5g.ucsd.edu/), May. 12, 2017

  • Broader Impact:

    Our research has had the following broader impact outcomes:

    • We have introduced the use of data storage in UEs, besides energy storage at base stations, for effective utilization of solar energy, paving the way for new approaches for green communication networks 

    • We have developed a novel online algorithm which simultaneously performs base station power control and battery charging/discharging for minimization of grid power consumption

    • We have developed renewable energy aware service off-loading methods for small cells with MECs for vehicular traffic

    • For the first time, our research demonstrated the feasibility of powering small cells only with harvested solar energy without any shutdowns for realistic communication traffic and computing loads

           We believe that these advances in theory, algorithms and practice will provide new building blocks for green computing and communications infrastructure research.

           Furthermore, our ability to develop techniques that enable the use of solar-powered small cells and MECs with minimal QoS loss has helped us decide to incorporate their use for clean computing and communication infrastructure for smart streets that we are designing and prototyping as part of our Smart Transportation Innovation Program (STIP) in partnership with the cities of San Diego and Ulsan http://jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=2507.

  • Simulation and Software Code:

            The following simulation data, simulation framework and software code (developed in Matlab) are provided below: 

    • Historical solar irradiance and BS utilization data

    • The proposed Lyapunov-based online algorithm (L-SPAR) for grid power minimization, and simulation results

    • A practical traffic flow model based on historical traffic data

    • Framework to simulate mobility and service requests of vehicular users with different communication and computing needs

    • QLM algorithm including battery scheduling and user association, and simulation results


This material is based upon work supported by the National Science Foundation under Grant No. CNS - 1619184 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.