KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Electricity Cost Minimization for Delay-tolerant Basestation Powered by Heterogeneous Energy Source

  • Deng, Qingyong (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications) ;
  • Li, Xueming (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications) ;
  • Li, Zhetao (College of Information Engineering, Xiangtan University) ;
  • Liu, Anfeng (School of Information Science and Engineering, Central South University) ;
  • Choi, Young-june (Department of Software, Ajou University)
  • Received : 2017.03.21
  • Accepted : 2017.08.20
  • Published : 2017.12.31
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Abstract

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

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References

  1. C. Xiong, G. Y. Li, S. Zhang, Y. Chen and S. Xu, "Energy-Efficient Resource Allocation in OFDMA Networks," IEEE Transactions on Communications, vol. 60, no. 12, pp. 3767-3778, Dec. 2012. https://doi.org/10.1109/TCOMM.2012.082812.110639
  2. H. Shao et al., "Joint Optimization of Quality of Experience and Power Consumption in OFDMA Multicell Networks," IEEE Communications Letters, vol. 20, no. 2, pp. 380-383, Feb. 2016. https://doi.org/10.1109/LCOMM.2015.2508924
  3. H. Zhang, Y. Nie, J. Cheng, V. C. M. Leung and A. Nallanathan, "Sensing Time Optimization and Power Control for Energy Efficient Cognitive Small Cell With Imperfect Hybrid Spectrum Sensing," IEEE Transactions on Wireless Communications, vol. 16, no. 2, pp. 730-743, Feb. 2017. https://doi.org/10.1109/TWC.2016.2628821
  4. A. Abbasi and M. Ghaderi, "Online algorithms for energy cost minimization in cellular networks," in Proc. of 2014 IEEE 22nd International Symposium of Quality of Service (IWQoS), Hong Kong, pp. 302-307, 2014.
  5. S. Bu, F. R. Yu, Y. Cai and X. P. Liu, "When the Smart Grid Meets Energy-Efficient Communications: Green Wireless Cellular Networks Powered by the Smart Grid," IEEE Transactions on Wireless Communications, vol. 11, no. 8, pp. 3014-3024, Aug. 2012. https://doi.org/10.1109/TWC.2012.052512.111766
  6. X. Chai; Z. Zhang; K. Long, "Joint Spectrum-Sharing and Base Station Sleep Model for Improving Energy Efficiency of Heterogeneous Networks," IEEE Systems Journal, vol.PP, no.99, pp.1-11.
  7. Han, Guangjie, et al. "Cross-layer optimized routing in wireless sensor networks with duty cycle and energy harvesting," Wireless Communications & Mobile Computing, vol. 15, Issue 16, pp. 1957-1981, 2015. https://doi.org/10.1002/wcm.2468
  8. H. Zhang; S. Huang; C. Jiang; K. Long; V. C. M. Leung; H. V. Poor, "Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations," IEEE Journal on Selected Areas in Communications , vol. 35, no. 9, pp. 1936-1947, 2017. https://doi.org/10.1109/JSAC.2017.2720898
  9. Dong, Y., et al, "Full Duplex Distributed Antenna Systems With Energy Harvesting," in Proc. of IEEE Global Communications Conference. IEEE, 2015.
  10. H. Zhang, J. Du, J. Cheng and V. C. M. Leung, "Resource Allocation in SWIPT Enabled Heterogeneous Cloud Small Cell Networks with Incomplete CSI," 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, pp. 1-5, 2016.
  11. L. Yu, T. Jiang and Y. Cao, "Energy Cost Minimization for Distributed Internet Data Centers in Smart Microgrids Considering Power Outages," IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 120-130, Jan. 2015. https://doi.org/10.1109/TPDS.2014.2308223
  12. Chenrui Jin, Xiang Sheng and P. Ghosh, "Energy efficient algorithms for Electric Vehicle charging with intermittent renewable energy sources," 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, pp. 1-5, 2013.
  13. Josip, Lorincz, G. Tonko, and P. Goran. "Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads," Sensors, 12(4), 4181-4310, 2012. https://doi.org/10.3390/s120404281
  14. Michael Neely, "Stochastic Network Optimization with Application to Communication and Queueing Systems," Stochastic Network Optimization with Application to Communication and Queueing Systems, 1, Morgan & Claypool, pp. 1-211, 2010.
  15. Y. Guo, Y. Fang and P. P. Khargonekar, "Optimal Workload and Energy Storage Management for Cloud Data Centers," in Proc. of MILCOM 2013 - 2013 IEEE Military Communications Conference, San Diego, CA, pp. 1850-1855, 2013.
  16. M. J. Neely, A. Saber Tehrani and A. G. Dimakis, "Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers," in Proc. of 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, pp. 549-554, 2010.
  17. C. Jin, X. Sheng and P. Ghosh, "Optimized Electric Vehicle Charging With Intermittent Renewable Energy Sources," IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 6, pp. 1063-1072, Dec. 2014. https://doi.org/10.1109/JSTSP.2014.2336624
  18. Western Wind resources Dataset. The national renewable energy laboratory.