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Energy efficient dynamic optimal control of LTE base stations: solution and trade-off

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Abstract

The demand to reduce energy consumption in wireless networks has become popular recently. In this paper, aimed at the problem that how to reduce energy consumption through on-off control in wireless networks without losing system performance significantly, we present our solution both in a single base station and the multi-base station scenario. Under the assumption that the network arrival and departure process are Markov, we first model and solve the problem of optimal on-off control in single base station scenario using dynamic integer programming (DIP) method, then we extend the optimal solution to multi-base station scenario and raise an effective heuristic method in two layer networks, the trade-off between QoS level and energy consumption has been given according to our analysis and simulation. Numerical results are provided to demonstrate that the proposed method offer significant gain in terms of the energy efficiency.

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References

  1. 1.

    Vetter, P., Ayhan, T., Kanonakis, K., Lannoo, B., Lee, K. L., Lefevre, L., Monney, C., Saliou, F., & Yin, X. (2013). Towards energy effcient wireline networks, an update from greentouch. In Optoelectronics and communications conference held jointly with 2013 international conference on photonics in switching (OECC/PS) 2013 18th (pp. 1–2). IEEE.

  2. 2.

    Olsson, M., Fehske, A., Hevizi, L., Blume, O., Vidacs, A., Godor, I., et al. (2012). Integration strategy of earth energy effciency enablers. Future Network Mobile Summit (FutureNetw), 2012, 1–8.

  3. 3.

    Huq, K. M. S., Mumtaz, S., Rodriguez, J., & Aguiar, R. L. (2012). Comparison of energy-effciency in bits per joule on different downlink comp techniques. In 2012 IEEE international conference on communications (ICC) (pp. 5716–5720).

  4. 4.

    Buvaneswari, A., Drabeck, L., Nithi, N., Haner, M., Polakos, P., & Sawkar, C. (2010). Self-optimization of lte networks utilizing celnet xplorer. Bell Labs Technical Journal, 15(3), 99–117.

  5. 5.

    Dini, P., Miozzo, M., Bui, N., & Baldo, N. (2013). A model to analyze the energy savings of base station sleep mode in lte hetnets. In Green computing and communications (GreenCom), 2013 IEEE and internet of things (iThings/CPSCom), IEEE international conference on and IEEE cyber, physical and social computing (pp. 1375–1380).

  6. 6.

    Gao, Y., Li, Y., Yu, H., Wang, X., Gao, S., & Xue, P. (2015). Energy efficient cooperative sleep control using small cell for wireless networks. International Journal of Distributed Sensor Networks, 2015, 10. doi:10.1155/2015/903853.

  7. 7.

    Saker, L., Elayoubi, S. E., & Chahed, T. (2010). Minimizing energy consumption via sleep mode in green base station. In Wireless communications and networking conference (WCNC) 2010 (pp. 1–6). IEEE.

  8. 8.

    Meng, C., Li, X., Lu, X., Liang, T., Jiang, Y., & Heng, W. (2013). A low complex energy saving access algorithm based on base station sleep mode. In 2013 IEEE/CIC international conference on communications in China (ICCC) (pp. 491–495).

  9. 9.

    Oh, E., Son, K., & Krishnamachari, B. (2013). Dynamic base station switching-on/off strategies for green cellular networks. IEEE Transactions on Wireless Communications, 12(5), 2126–2136.

  10. 10.

    Bousia, A., Kartsakli, E., Alonso, L., & Verikoukis, C. (2012). Dynamic energy efficient distance-aware base station switch on/off scheme for LTE-advanced, In Proceedings of the IEEE GLOBECOM December 2012 (pp. 1532–1537).

  11. 11.

    Marsan, M. A., Chiaraviglio, L., Ciullo, D., & Meo, M. (2009). Optimal energy savings in cellular access networks. In IEEE international conference on communications workshops (ICC) June 2009.

  12. 12.

    Bousia, A., Kartsakli, E., Antonopoulos, A., Alonso, L., & Verikoukis, C. (2013). Game theoretic approach for switching off base stations in multi-operator environments. In IEEE international conference on communications (ICC) June 2013.

  13. 13.

    He, C., Li, G. Y., Zheng, F.-C., & You, X. (2014). Energy-effcient resource allocation in ofdm systems with distributed antennas. IEEE Transactions on Vehicular Technology, 63(3), 1223–1231.

  14. 14.

    Hu, S., Guo, H., Jin, C., Huang, Y., Yu, B., & Li, S. (2016). Frequency-domain oversampling for cognitive CDMA systems: Enabling robust and massive multiple access for internet of things. In IEEE Access (vol. 4, pp. 4583–4589).

  15. 15.

    Hu, S., Bi, G., Guan, Y. L., & Li, S. (2013). TDCS-based cognitive radio networks with multiuser interference avoidance. IEEE Transactions on Communications, 61(12), 4828–4835.

  16. 16.

    Hu, S., Liu, Z., Guan, Y. L., et al. (2014). Sequence design for cognitive CDMA communications under arbitrary spectrum hole constraint. IEEE Journal on Selected Areas in Communications, 32(11), 1974–1986.

  17. 17.

    Helmy, A., Musavian, L., & Le-Ngoc, T. (2013). Energy-effcient power adaptation over a frequency-selective fading channel with delay and power constraints. IEEE Transactions on Wireless Communications, 12(9), 4529–4541.

  18. 18.

    Xu, Datong, Ren, Pinyi, Sun, Li, & Song, Houbing. (2016). Precoder-and-receiver design scheme for multi-user coordinated multi-point in LTE-A and fifth generation systems. IET Communications, 10(3), 292–299.

  19. 19.

    Song, Houbing, Srinivasan, Ravi, Sookoor, Tamim, Jeschke, Sabina, & Cities, Smart. (2017). Foundations, principles and applications. Hoboken, NJ: Wiley.

  20. 20.

    Jeschke, S., Brecher, C., Song, H., & Rawat, D. (2017). Industrial internet of things. Cham: Springer.

  21. 21.

    Shojafar, M., Cordeschi, N., Abawajy, J. H., & Baccarelli, E. (2015). Adaptive energy-efficient qos-aware scheduling algorithm for TCP/IP mobile cloud. In 2015 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6). IEEE.

  22. 22.

    Ahmad, R. W., et al. (2016). A survey on energy estimation and power modeling schemes for smartphone applications. International Journal of Communication Systems. doi:10.1002/dac.3234.

  23. 23.

    Song, H., Rawat, D., Jeschke, S., & Brecher, C. (2016). Cyber-physical systems: Foundations, principles and applications. Boston, MA: Academic Press.

  24. 24.

    Jiang, D., Zhang, P., Lv, Z., & Song, H. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3(6), 1437–1447.

  25. 25.

    Shojafar, M., Canali, C., Lancellotti, R., & Abawajy, J. (2016). Adaptive computing-plus-communication optimization framework for multimedia processing in cloud systems. IEEE Transactions on Cloud Computing, 99, 1–1.

  26. 26.

    Shen, H., Xu, W., Jin, S., & Zhao, C. (2014). Joint transmit and receive beamforming for multiuser mimo downlinks with channel uncertainty. IEEE Transactions on Vehicular Technology, 63(5), 2319–2335.

  27. 27.

    Lee, H., Kim, S., & Lee, S. (2014). Combinatorial orthogonal beamforming for joint processing and transmission. IEEE Transactions on Communications, 62(2), 625–637.

  28. 28.

    Hoymann, C., Larsson, D., Koorapaty, H., & Cheng, J.-F. (2013). A lean carrier for lte. IEEE Communications Magazine, 51(2), 74–80.

  29. 29.

    Gao, Y., Li, Y., Yu, H., Wang, X., & Gao, S. (2014). Energy effcient cooperative cell control of LTE-advanced cellular networks. In Control and system graduate research colloquium (ICSGRC) 2014 (pp. 263–267) IEEE 5th.

  30. 30.

    Gao, Y., Li, Y., Yu, H., Wang, X., & Gao, S. (2012). System level performance of comp ir-harq over x2 interface in 3gpp lte-advanced system. In 2012 9th international conference on communications (COMM) (pp. 177–180).

  31. 31.

    Li, X., Toseef, U., Weerawardane, T., Bigos, W., Dulas, D., Goerg, C., Timm-Giel, A., & Klug, A. (2010). Dimensioning of the LTE s1 interface. In Wireless and mobile networking conference (WMNC), 2010 Third Joint IFIP (pp. 1–6).

  32. 32.

    Soh, Y. S., Quek, T. Q. S., Kountouris, M., & Shin, H. (2013). Energy effcient heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications, 31(5), 840–850.

  33. 33.

    Govindasamy, S., Bliss, D. W., & Staelin, D. H. (2013). Asymptotic spectral effciency of the uplink in spatially distributed wireless networks with multi-antenna base stations. IEEE Transactions on Communications, 61(7), 100–112.

  34. 34.

    Kong, P.-Y. (2014). Optimal probabilistic policy for dynamic resource activation using markov decision process in green wireless networks. IEEE Transactions on Mobile Computing, 13(10), 2357–2368.

  35. 35.

    Desset, C., Debaillie, B., & Louagie, F. (2013). Towards a fexible and future-proof power model for cellular base stations. In 2013 24th Tyrrhenian international workshop on digital communications–green ICT (TIWDC) (pp. 1–6).

  36. 36.

    Holtkamp, H., Auer, G., Giannini, V., & Haas, H. A. (2013). Parameterized base station power model. IEEE Communications Letters, 17(11), 2033–2035.

  37. 37.

    Chitti, K., Kuang, Q., & Speidel, J. (2013). Joint base station association and power allocation for uplink sum-rate maximization. In 2013 IEEE 14th workshop on signal processing advances in wireless communications (SPAWC) (pp. 6–10).

  38. 38.

    Incebacak, D., Tavli, B., Bicakci, K., & Altin-Kayhan, A. (2013). Optimal number of routing paths in multi-path routing to minimize energy consumption in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2013(1), 252.

  39. 39.

    Abramowitz, M., & Stegun, I. A. (2006). Handbook of mathematical functions with formulas, graphs, and mathematical tables. In Conference on XYZ Dover.

  40. 40.

    Subramanian, S., Shea, J. M., Pasiliao, E. L., Carvalho, M. M., & Dixon, W. E. (2014). Effcient spectrum allocation in multiband csma networks. In 2014 IEEE wireless communications and networking conference (WCNC) (pp. 1591–1596).

  41. 41.

    Pinola, J., Perala, J., Jurmu, P., et al. (2013). A systematic and flexible approach for testing future mobile networks by exploiting a wrap-around testing methodology. IEEE Communications Magazine, 51(3), 160–167.

  42. 42.

    Gao, Y., et al. (2015). A novel energy aware dynamic on-off control of base stations in wireless networks. In 5 IEEE 16th international conference on communication technology (ICCT) Hangzhou (pp. 132–137).

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Author information

Correspondence to Wei Wei.

Additional information

This work is supported by: National Basic Research Program of China (2012CB316002); National Natural Science Foundation of China (61201192); Beilin District 2012 High-tech Plan, Xi’an, China (No. GX1504);Xi’an Science and Technology Project (CXY1440(6)); Shaanxi Scientific Research (2014k07-11); the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20136118120010).The work of Su Hu was supported jointly by National Key Research and Development Program (Grant No. 2016YFE0123200), National Natural Science Foundation of China (Grant No. 61471100/61101090/61571082), Open research fund of Science and Technology on Electronic Information Control Laboratory (Grant No. 6142105040103) and Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J012/ ZYGX2014Z005). The author would also like to thank all the reviewers, their suggestions help improve my work a lot. Part of this work is accepted and presented in IEEE ICCT 2015, Hangzhou, China [42].

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Huang, D., Wei, W., Gao, Y. et al. Energy efficient dynamic optimal control of LTE base stations: solution and trade-off. Telecommun Syst 66, 701–712 (2017). https://doi.org/10.1007/s11235-017-0318-z

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Keywords

  • Energy efficient
  • On-off control
  • Trade-off
  • Flexible coverage
  • Integer programming