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Closed Loop Power Control Using Third Order Quadratic Approximator

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Abstract

In this paper we introduce a novel bit error rate (BER) feedback transmit power control (TPC) system using a first-time third order quadratic approximation of the power–time curve. This approximation improves the power efficiency of a dual-rate TPC algorithm in terms of reduced total transmit power by smoothing transmit power transients during adaptive iterations. We show that the third order approximator outperforms linear and second order approximators in terms of transmit power savings, sensitivities, error magnitude, and better tracking performance in following reference desired power curves. For the approximator, we determine operational bounds for stability, and demonstrate algorithm behavior using critical valued inputs. In addition we demonstrate value in using a dynamic, rather than static, performance benchmark for quality of service approximation (obtained used scaled maximum acceptable BER), and provide heuristic estimates for the input parameters for the dynamic benchmark.

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References

  1. T. Zahariadis, Personal area networks, Communications Engineering, Vol. 1, No. 3, pp. 12–15, 2003.

    Article  Google Scholar 

  2. M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. C. M. Leung, Body area networks: a survey, Mobile Networks and Applications, Vol. 16, No. 2, pp. 171–193, 2010.

    Article  Google Scholar 

  3. E. Dall’Anese, S.-J. Kim, G. B. Giannakis, and S. Pupolin, Power control for cognitive radio networks under channel uncertainty, IEEE Transactions on Wireless Communications, Vol. 10, No. 10, pp. 3541–3551, 2011.

    Article  Google Scholar 

  4. M. I. Razzak, B. A. Elmogy, M. K. Khan, and K. Alghathbar, Efficient distributed face recognition in wireless sensor network, International Journal of Innovative Computing, Information and Control, Vol. 8, No. 4, pp. 2811–2822, 2012.

    Google Scholar 

  5. H.-B. Li, and R. Kohno, Body area network and its standardization at IEEE 802.15. BAN, in Advances in Mobile and Wireless Communications, Springer, Berlin, 2008, pp. 223–238.

  6. A. Mayers, P. Benavidez, G. Raju, D. Akopian, and M. Jamshidi, A novel BER-feedback power control algorithm for Personal Area Network Devices, in Systems Conference (SysCon), 2013 IEEE International, 2013, pp. 370–375.

  7. E. C. Peh, Y.-C. Liang, and Y. Zeng, Sensing and power control in cognitive radio with location information, in 2012 IEEE International Conference on Communication Systems (ICCS), 2012, pp. 255–259.

  8. M. Yu, X. Ma, W. Su, and L. Tung, A new joint strategy of radio channel allocation and power control for wireless mesh networks, Computer Communications, Vol. 35, No. 2, pp. 196–206, 2012.

    Article  Google Scholar 

  9. N. Hoven, and A. Sahai, Power scaling for cognitive radio, in 2005 International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, 2005, pp. 250–255.

  10. G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, Energy conservation in wireless sensor networks: a survey, Ad Hoc Networks, Vol. 7, No. 3, pp. 537–568, 2009.

    Article  Google Scholar 

  11. A. Paul, M. Akar, M. G. Safonov, and U. Mitra, Adaptive power control for wireless networks using multiple controllers and switching, IEEE Transactions on Neural Networks, Vol. 16, No. 5, pp. 1212–1218, 2005.

    Article  Google Scholar 

  12. R. Tandra, and A. Sahai, SNR walls for signal detection, IEEE Journal of Selected Topics in Signal Processing, Vol. 2, No. 1, pp. 4–17, 2008.

    Article  Google Scholar 

  13. S. Jin, W. Yue, and Q. Sun, Performance analysis of the sleep/wakeup protocol in a wireless sensor network, International Journal of Innovative Computing Information and Control, Vol. 8, No. 5, pp. 3833–3844, 2012.

    Google Scholar 

  14. M. C. Vuran, Ö. B. Akan, and I. F. Akyildiz, Spatio-temporal correlation: theory and applications for wireless sensor networks, Computer Networks, Vol. 45, No. 3, pp. 245–259, 2004.

    Article  MATH  Google Scholar 

  15. C. Han, T. Harrold, S. Armour, I. Krikidis, S. Videv, P. M. Grant, H. Haas, J. S. Thompson, I. Ku, and C.-X. Wang, Green radio: radio techniques to enable energy-efficient wireless networks, IEEE Communications Magazine, Vol. 49, No. 6, pp. 46–54, 2011.

    Article  Google Scholar 

  16. A. Papadopoulos, A. Navarra, J. A. McCann, and C. M. Pinotti, VIBE: an energy efficient routing protocol for dense and mobile sensor networks, Journal of Network and Computer Applications, Vol. 35, No. 4, pp. 1177–1190, 2012.

    Article  Google Scholar 

  17. S. Videv, and H. Haas, Energy-efficient scheduling and bandwidth-energy efficiency trade-off with low load, in 2011 IEEE International Conference on Communications (ICC), 2011, pp. 1–5.

  18. N. Devroye, M. Vu, and V. Tarokh, Cognitive radio networks, IEEE Signal Processing Magazine, Vol. 25, No. 6, pp. 12–23, 2008.

    Article  Google Scholar 

  19. S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp. 201–220, 2005.

    Article  Google Scholar 

  20. A. Batra, S. Lingam, and J. Balakrishnan, Multi-band OFDM: a cognitive radio for UWB, in presented at the Conference Proceeding of IEEE International Symposium on Circuits and Systems (ISCAS), 2006, pp. 4094–4097.

  21. V. D. Chakravarthy, Z. Wu, A. Shaw, M. A. Temple, R. Kannan, and F. Garber, A general overlay/underlay analytic expression representing cognitive radio waveform, in 2007 International Waveform Diversity and Design Conference, 2007, pp. 69–73.

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Acknowledgments

This work was supported in part by NSF Grants 0942852, 0932339 and CRI-0551501.

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Authors and Affiliations

  1. The University of Texas at San Antonio, San Antonio, TX, USA

    Andre M. Mayers, Patrick J. Benavidez, G. V. S. Raju, David Akopian & Mo M. Jamshidi

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  1. Andre M. Mayers
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  2. Patrick J. Benavidez
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  3. G. V. S. Raju
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  4. David Akopian
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  5. Mo M. Jamshidi
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Correspondence to Andre M. Mayers.

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Mayers, A.M., Benavidez, P.J., Raju, G.V.S. et al. Closed Loop Power Control Using Third Order Quadratic Approximator. Int J Wireless Inf Networks 21, 125–132 (2014). https://doi.org/10.1007/s10776-014-0236-x

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  • DOI: https://doi.org/10.1007/s10776-014-0236-x

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