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Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks

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Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high-data rate services and rapid increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff problem in interference-limited wireless networks. Different from previous researches, we try to optimize EE and SE simultaneously. Firstly, the problem is formulated as a multi-objective optimization problem (MOP), with the constraint of transmit power limit. Then, we convert the MOP to a single-objective optimization problem by the weighted linear sum method. We present an algorithm utilizing difference between two convex functions programming (DCP) to handle with SE optimization problem (SD). EE optimization problem can be solved by an algorithm (EFD) consists of fractional programming embedded with DCP. While for EE–SE tradeoff problem, a particle swarm optimization algorithm is proposed (ESTP) to deal with it. Simulation results validate that the proposed algorithm can efficiently balance EE and SE by adjusting the value of weighted coefficient, which could be used to design a flexible energy efficient network in the future.

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  1. 1.

    Wu, S., Biaz, S., & Wang, H. (2012). Rate adaptation with loss diagnosis on IEEE 802.11 networks. International Journal of Communication Systems, 25(4), 515–528.

  2. 2.

    Wang, H., Ko, K., & Woo, C. (2012). Maximized achievable rate of SINR-measurement-based spectrum sharing with binary feedback. International Journal of Communication Systems, 25(3), 404–413.

  3. 3.

    Miao, G., Himayat, N., Li, Y., & Swami, A. (2009). Cross-layer optimization for energy-efficient wireless communications: A survey. Wireless Communications and Mobile Computing, 9(4), 529–542.

  4. 4.

    Hasan, Z., Boostanimehr, H., & Bhargava, V. (2012). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 13(4), 520–540.

  5. 5.

    Miao, G., Himayat, N., Li, G., & Talwar, S. (2011). Distributed interference-aware energy-efficient power optimization. IEEE Transactions on Wireless Communications, 10(4), 1323–1333.

  6. 6.

    Feng, D., Jiang, C., Lim, G., Cimini, L., Feng, G., & Li, G. (2013). A survey of energy-efficient wireless communications. IEEE Communications Surveys & Tutorials, 15(1), 167–178.

  7. 7.

    Edler, T., & Lundberg, S. (2004). Energy efficiency enhancements in radio access networks. Ericsson Review, 81(1), 42–51.

  8. 8.

    Kumar, R., & Mieritz, L. (2007). Conceptualizing ‘green’ IT and data center power and cooling issues. Gartner, Research Paper G00150322.

  9. 9.

    Chen, Y., Zhang, S., XU, S., & Li, G. (2011). Fundamental trade-offs on green wireless networks. IEEE Communications Magazine, 49(6), 30–37.

  10. 10.

    Meshkati, F., Poor, H., & Schwartz, S. (2007). Energy-efficient resource allocation in wireless networks. IEEE Signal Processing Magazine, 24(3), 58–68.

  11. 11.

    Miao, G., Himayat, N., Li, G., & Bormann, D. (2008). Energy efficient design in wireless OFDMA. In Proceedings of IEEE ICC’08, pp. 3307–3312.

  12. 12.

    Jiang, C., Shi, Y., Hou, Y., & Kompella, S. (2011). On optimal throughput energy curve for multi-hop wireless networks. In Proceedings of IEEE INFOCOM, pp. 1341–1349.

  13. 13.

    Gur, G., & Alagoz, F. (2011). Green wireless communications via cognitive dimension: an overview. IEEE Network, 25(2), 50–56.

  14. 14.

    Cui, S., Goldsmith, A., & Bahai, A. (2004). Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE Journal on Selected Areas in Communications, 22(6), 1089–1098.

  15. 15.

    Kim, H., Chae, C., de Veciana, G., & Heath, R. (2009). A cross-layer approach to energy efficiency for adaptive mimo systems exploiting spare capacity. IEEE Transactions on Wireless Communications, 8(8), 4264–4275.

  16. 16.

    Miao, G., Himayat, N., & Li, G. (2010). Energy-efficient link adaptation in frequency-selective channels. IEEE Transactions on Communications, 58(2), 545–554.

  17. 17.

    Xiong, C., Li, G., Zhang, S., Chen, Y., & Xu, S. (2011). Energy and spectral-efficiency tradeoff in downlink ofdma networks. IEEE Transactions on Wireless Communications, 10(11), 3874–3886.

  18. 18.

    Deng, L., Rui, Y., Cheng, P., Zhang, J., Zhang, Q., & Li, M. (2013). A unified energy efficiency and spectral efficiency tradeoff metric in wireless networks. IEEE Communications Letters, 17(1), 55–58.

  19. 19.

    Verdu, S. (2002). Spectral efficiency in the wideband regime. IEEE Transactions on Information Theory, 48(6), 1319–1343.

  20. 20.

    Shamai, S., & Verdu, S. (2001). The impact of frequency-flat fading on the spectral efficiency of CDMA. IEEE Transactions on Information Theory, 47(4), 1302–1327.

  21. 21.

    Onireti, O., Heliot, F., & Imran, M. (2012). On the energy efficiency-spectral efficiency trade-off in the uplink of comp system. IEEE Transactions on Wireless Communications, 11(2), 556–561.

  22. 22.

    Heliot, F., Imran, M., & Tafazolli, R. (2012). On the energy efficiency spectral efficiency trade-off over the MIMO rayleigh fading channel. IEEE Transactions on Wireless Communications, 60(5), 1345–1356.

  23. 23.

    Ng, D., Lo, E., & Schober, R. (2013). Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas. IEEE Transactions on Wireless Communications, 62(4), 1801–1814.

  24. 24.

    Li, Z., Jiang, H., Pan, Z., Liu, N., & You, X. (2015). Energy spectral efficiency tradeoff in downlink OFDMA network. International Journal of Communication Systems, 28(8), 1450–1461.

  25. 25.

    Rao, J., & Fapojuwo, A. (2009). On the tradeoff between spectral efficiency and energy efficiency of homogeneous cellular networks with outage constraint. IEEE Transactions on Vehicular Technology, 8(3), 1553C1563.

  26. 26.

    Kha, H., Tuan, H., & Nguyen, H. (2012). Fast global optimal power allocation in wireless networks by local DC programming. IEEE Transactions on Wireless Communications, 11(2), 510–515.

  27. 27.

    Cui, S., Goldsmith, A., & Bahai, A. (2005). Energy-constrained modulation optimization. IEEE Transactions on Wireless Communications, 4(5), 2349–2360.

  28. 28.

    Jibukumar, M., Datta, R., & Biswas, P. (2012). Busy tone contention protocol: A new high-throughput and energy-efficient wireless local area networkmedium access control protocol using busy tone. International Journal of Communication Systems, 25(8), 991–1014.

  29. 29.

    Kwon, H., & Birdsall, T. (1986). Channel capacity in bits per joule. IEEE Journal of Oceanic Engineering, 11(1), 97–99.

  30. 30.

    Marler, R., & Arora, J. (2004). Survey of multiobjective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395.

  31. 31.

    Vucic, N., Shi, S., & Schubert, M. DC programming approach forresource allocation in wireless networks. In Proceedings of 2010 international symposium on on modeling and optimization in mobile, ad hoc and wirelessnetworks, pp. 380–386.

  32. 32.

    Tuy, H. (1998). Convex analysis and global optimization. Dordrecht: Kluwer.

  33. 33.

    Boyd, S. Sequential convex programming, Lecture slides and notes. http://www.stanford.edu/class/ee364b/lectures.html

  34. 34.

    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

  35. 35.

    Dinkelbach, W. (1967). On nonlinear fractional programming. Management Science, 13(7), 492–498.

  36. 36.

    Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IJCNN’95, pp. 1942–1948.

  37. 37.

    Eberhart, R., Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of MHS’95, pp. 39–43.

  38. 38.

    Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence, 1(1), 33–57.

  39. 39.

    Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In Proceedings of WCCI’98, pp. 69–73.

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Author information

Correspondence to Zhihang Li.

Additional information

This work was supported by the National Special Key Program (Grant Nos. 2011ZX03003-002-02, 2012ZX03003-010-002), the National Basic Research Program of China (973 Program 2012CB316004), the National Natural Science Foundation of China under Grant 61201170, the Research Fund of National Mobile Communications Research Laboratory, Southeast University (Nos. 2014A02, 2014A02), the Liuda Rencai Gaofeng of Jiangsu Province, Jiangsu Provincal Key Technology R&D Program (BE2012165), and Huawei Corp. Ltd.

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Li, Z., Jiang, H., Li, P. et al. Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks. Wireless Pers Commun 96, 5515–5532 (2017). https://doi.org/10.1007/s11277-017-4223-2

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  • Energy efficiency
  • Spectral efficiency
  • Particle swarm optimization
  • Fractional programming
  • Interference-limited