Energy-Efficient User Association in Broadcast Transmission
This paper addresses the user association problem in a multi-cell broadcast transmission. We seek minimal total energy consumption by considering both transmission power and operational power cost. We propose a novel distributed solution based on network utility games and using so-called Markovian approximation we design the distributed base station (BS) selection algorithm. Extensive simulation results are provided and highlight the relative performance of the algorithm.
KeywordsEnergy efficiency Broadcast Potential game Markov approximation
This work was supported in part by The Leverhulme Trust.
- 2.N. Lev-Tov and D. Peleg, “Polynomial time approximation schemes for base station coverage with minimum total radii,” Computer Networks, vol.47, no.4, pp.489–501, Mar. 2005.Google Scholar
- 3.H. Alt, E. M. Arkin, H. Brönnimann, J. Erickson, S. P. Fekete, C. Knauer, J. Lenchner, J. S. B. Mitchell, and K. Whittlesey, “Minimum-cost coverage of point sets by disks,” In Proceedings of ACM Symposium on Computational Geometry (SCG ’06), pp. 449–458, New York, NY, USA, 2006.Google Scholar
- 4.S. Funke, S. Laue, Z. Lotker, and R. Naujoks, “Power assignment problems in wireless communication: Covering points by disks, reaching few receivers quickly, and energy-efficient travelling salesman tours,” In Proceedings of Ad Hoc Networks, pp.1028–1035, 2011.Google Scholar
- 5.Ö. Eğecioğlu, and T. Gonzalez, “Minimum-energy broadcast in simple graphs with limited node power,” IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2001), pp.334–338, Anaheim, CA, Aug. 2001.Google Scholar
- 6.M. Cagalj, J. P. Hubaux, and C. Enz, “Minimum-energy broadcast in all-wireless networks: NP-completeness and distribution issues,” In Proceedings of ACM international conference on Mobile computing and networking (MobiCom), New York, NY, USA, pp.172–182, 2002.Google Scholar
- 7.S. M. Perlaza, E. V. Belmega, S. Lasaulce, and M. Debbah, “On the base station selection and base station sharing in self-configuring networks”, Proc. of the ACM International Conference on Performance Evaluation Methodologies and Tool, Pisa, Italy, October 2009.Google Scholar
- 8.S. Lasaulce, H. Tembine, “Game theory and learning for wireless networks,” Elsevier, 2011.Google Scholar
- 9.M. Chen, S. C. Liew, Z. Shao and C. Kai, “Markov approximation for combinatorial network optimization,” in IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6301–6327, Oct. 2013.Google Scholar
- 10.X. Chen, X. Gong, L. Yang and J. Zhang, “A social group utility maximization framework with applications in database assisted spectrum access,” in IEEE INFOCOM 2014.Google Scholar