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Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS. Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks. 1 Zhongming Zheng, 1 Shibo He, 2 Lin X. Cai, and 1 Xuemin (Sherman) Shen 1 Department of Electrical and Computer Engineering

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Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks

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  1. HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks 1Zhongming Zheng, 1Shibo He, 2Lin X. Cai, and 1Xuemin (Sherman) Shen 1Department of Electrical and Computer Engineering University of Waterloo 2School of Engineering and Applied Science Princeton University

  2. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  3. Introduction • Energy Sources • Renewable Energy • Repeatedly replenished • Examples: hydropower, biomass • Non-renewable Energy: • Once depleted, no more available • Examples: coal, natural gas

  4. Introduction • Green Energy • Eco-friendly renewable energy • Example: wind, solar

  5. Introduction • Green Wireless Communication Networks • WLAN mesh network structure

  6. Introduction • Projects • EARTH • Energy Aware Radio and neTwork tecHnologies • PERANET • GREENRADIO

  7. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  8. Literature Review • Device Design • PV systems • [1] Probabilistic methods • [2] Simulation model • Energy charging and discharging models • [3] Battery/energy buffer • [4] Power consumption model of BSs [1] H. A. M. Maghraby, M. H. Shwehdi, and G. K. Al-Bassam, “Probabilistic assessment of photovoltaic (pv) generation systems,” Power Systems, IEEE Transactions on, vol. 17, no. 1, pp. 205–208, Feb. 2002. [2] E. Lorenzo and L. Navarte, “On the usefulness of stand-alone pv sizing methods,” Progress in Photovoltaics: Research and Applications, vol. 8, no. 4, pp. 391–409, Aug. 2000. [3] L. X. Cai, Y. Liu, H. T. Luan, X. Shen, J. W. Mark, and H. V. Poor, “Adaptive resource management in sustainable energy powered wireless mesh networks,” in IEEE Globecom, Houston, TX, USA, Dec. 5-9 2011, pp. 1–5. [4] O. Arnold, F. Richter, G. Fettweis, and O. Blume, “Power consumption modeling of different base station types in heterogeneous cellular networks,” in Future Network & Mobile Summit, Florence, IT, Jun. 16-18 2010, pp. 1–8.

  9. Literature Review • Minimal Device Deployment • Continuous Case • Direct search • [5] Quasi-Newton methods • Discrete Case • [6] Sustainability • [7] Outage free [5] G. L. Z. Wei and L. Qi, “New quasi-newton methods for unconstrained optimization problems,” Applied Mathematics and Computation, vol. 175, no. 2, pp. 1156–1188, Apr. 2006. [6] Z. Zheng, L. X. Cai, M. Dong, X. Shen, and H. V. Poor, “Constrained energyaware ap placement with rate adaptation in wlan mesh networks,” in IEEE GLOBECOM, Houston, TX, USA, Dec. 5-9 2011, pp. 1–5. [7] S. A. Shariatmadari, A. A. Sayegh, and T. D. Todd, “Energy aware basestation placement in solar powered sensor networks,” in IEEE WCNC, Sydney, AUS, Apr. 18-21 2010, pp. 1–6.

  10. Literature Review • Resource Allocation • Scheme Design • [8] Traffic scheduling • [9] Admission control and routing • [10] Power control [8] A. A. Hammad, G. H. Badawy, T. D. Todd, A. A. Sayegh, and D. Zhao, “Traffic scheduling for energy sustainable vehicular infrastructure,” in IEEE GLOBECOM, Miami, FL, USA, Dec. 6-10 2010, pp. 1–6. [9] L. Lin, N. B. Shroff, and R. Srikant, “Asymptotically optimal energy-aware routing for multihop wireless networks with renewable energy sources,” Networking, IEEE/ACM Transactions on, vol. 15, no. 5, pp. 1021–1034, Oct. 2007. [10] A. Farbod and T. D. Todd, “Resource allocation and outage control for solarpowered wlan mesh networks,” Mobile Computing, IEEE Transactions on, vol. 6, no. 8, pp. 960–970, Aug. 2007.

  11. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  12. System Model • Given a set of BSs, users and candidate locations • All users are associated with a BS • BSs are powered by renewable energy • BSs and users may have different power levels of charging and transmission • In a WLAN, BS and its associated users use the same transmission power

  13. System Model • No inter-WLAN interference with orthogonal channels assigned to BSs for inter-WLAN communication • BSs can only be placed at a given set of candidate locations • BSs at different candidate locations have different charging capabilities

  14. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  15. Problem Formulation The number of deployed BSs Full coverage & Each user is associated with only one BS Achieved throughput ≥ Traffic demand Harvested energy ≥ Consumed energy

  16. Problem Formulation • Initialization: • Output:

  17. Problem Formulation • Problem Analysis • Minimal BS placement problem with power allocation • NP-hard problem • Sub-problems are NP-hard • Optimal placement of BSs with a fixed power • Power allocation of BSs

  18. Problem Formulation • Algorithm Design Strategy • NP-hard → No solution in polynomial time • Design an effective heuristic algorithm • Achieve good performance • Reduce the time complexity

  19. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  20. TCGBP Algorithm • First Phase • Partition the whole network region into several VPs (Voronoi Polygons) • Place one BS in each candidate location • Connect users to the BS in the same VP region

  21. TCGBP Algorithm • First Phase

  22. TCGBP Algorithm • Second Phase • Connect BSs and users in neighboring VP regions until constraints can not be held • Return the result when all users are connected

  23. TCGBP Algorithm • Second Phase

  24. TCGBP Algorithm Phase II Phase I

  25. TCGBP Algorithm

  26. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  27. Numerical Results • Simulation Configurations

  28. Numerical Results Different numbers of users and traffic demands

  29. Numerical Results Different numbers of candidate locations and charging capabilities

  30. Outline • Introduction • Literature Review • System Model • Problem Formulation • TCGBP Algorithm • Numerical Results • Conclusion & Future Work

  31. Conclusion • Green energy sources • Formulate an optimal green BS placement problem • Propose TCGBP algorithm • Approach the optimal solution with significantly reduced time complexity

  32. Future Work • Study the impacts of dynamics in the energy charging and discharging process • Analyze the network capacity bounds under different deployment strategies

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