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System Power Minimization in Non-contiguous Spectrum Access

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Handbook of Cognitive Radio

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Abstract

Wireless transmission using non-contiguous chunks of spectrum is becoming increasingly important due to a variety of scenarios such as secondary users avoiding incumbent users in TV white space, anticipated spectrum sharing between commercial and military systems, and spectrum sharing among uncoordinated interferers in unlicensed bands. multichannel multi-radio (MC-MR) platforms and non-contiguous orthogonal frequency division multiple access (NC-OFDMA) technology are the two commercially viable transmission choices to access these non-contiguous spectrum chunks. Fixed MC-MRs do not scale with increasing number of non-contiguous spectrum chunks due to their fixed set of supporting radio front ends. NC-OFDMA allows nodes to access these non-contiguous spectrum chunks and put null subcarriers in the remaining chunks. However, nulling subcarriers increases the sampling rate (spectrum span) which, in turn, increases the power consumption of radio front ends. Our work characterizes this trade-off from a cross-layer perspective, specifically by showing how the slope of ADC/DAC’s power consumption versus sampling rate curve influences scheduling decisions in a multi-hop network. Specifically, we provide a branch and bound algorithm-based mixed integer linear programming solution that performs joint power control, spectrum span selection, scheduling, and routing in order to minimize the system power of multi-hop NC-OFDMA networks. We also provide a low-complexity (O(E 2 M 2)) greedy algorithm where M and E denote the number of channels and links, respectively. Numerical simulations suggest that our approach reduces system power by 30% over classical transmit power minimization based cross-layer algorithms.

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References

  1. AD9467: 16 bit, 200 MSPS/250 MSPS, analog-to-digital converter. http://www.analog.com/media/en/technical-documentation/data-sheets/AD9467.pdf. Accessed Jan 2016

  2. AD9777: 16-Bit interpolating dual DAC converter. http://www.analog.com/media/en/technical-documentation/data-sheets/AD9777.pdf. Accessed Jan 2016

  3. ADC performance evolution; Walden figure-out-merit (FOM). http://converterpassion.wordpress.com/2012/08/21/. Accessed Jan 2016

  4. Boyd S, Vandenberghe L (1999) Convex optimization. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  5. Cao L, Yang L, Zheng H (2010) The impact of frequency-agility on dynamic spectrum sharing. In: IEEE DySPAN 2010, pp 1–12. doi:10.1109/DYSPAN.2010.5457889

    Google Scholar 

  6. Cordeiro C, Challapali, Birru D et al (2005) IEEE 802.22: the first worldwide wireless standard based on cognitive radios. In: IEEE DySPAN 2005 Nov, pp 328–337. doi:10.1109/DYSPAN.2005.1542649

  7. Cormen TH, Leiserson CE, Rivest RL et al (2009) Introduction to algorithms. The MIT Press, Cambridge

    MATH  Google Scholar 

  8. Cover TM, Thomas JA (2005) Elements of information theory. Wiley, Hoboken

    Book  MATH  Google Scholar 

  9. Cui S, Goldsmith A, Bahai A (2005) Energy-constrained modulation optimization. In: IEEE TWC 2005 Sep:2349–2360. doi:10.1109/TWC.2005.853882

    Google Scholar 

  10. CVX: Matlab software for disciplined convex programming. http://cvxr.com/cvx/. Accessed Jan 2016

  11. Dual channel, 14 bit, 125/105/80/65 MSPS ADC with DDR LVDS/CMOS outputs. http://www.ti.com/lit/ds/symlink/ads62p42.pdf. Accessed Jan 2016

  12. Dual-channel 10/12 bit 500 MSPS digital-to-analog converters. http://www.ti.com/lit/ds/symlink/dac3162.pdf. Accessed Jan 2016

  13. Dual-channel 14 bit 250 MSPS ultralow-power ADC. http://www.ti.com/lit/ds/symlink/ads4249.pdf. Accessed Jan 2016

  14. Enabling innovative small cell use in 3.5 GHz band NPRM and order. Available via FCC. http://www.fcc.gov/document/enabling-innovative-small-cell-use-35-ghz-band-nprm-order. Accessed Jan 2016

  15. Fettwis G, Krondorf M, Bittner S (2009) GFDM – generalized frequency division multiplexing. In: IEEE VTC 2009 June, pp 1–4. doi:10.1109/VETECS.2009.5073571

  16. Fredriksson K, Guhl M (2011) Multi-channel, multi-radio in wireless mesh networks. Available via Chalmers institute of technology, Goteborg. http://publications.lib.chalmers.se/records/fulltext/138138.pdf. Accessed Nov 2016

    Google Scholar 

  17. Gerami C, Mandayam NB, Greenstein LJ (2010) Backhauling in TV white space. In: IEEE Globecom 2010, pp 1–6. doi:10.1109/GLOCOM.2010.5684131

    Google Scholar 

  18. Grover P, Woyach KA, Sahai A (2011) Towards a communication theoretic understanding of system level power consumption. In: IEEE JSAC 2011, vol 29, pp 1744–1755. doi:10.1109/JSAC.2011.110922

    Google Scholar 

  19. Hou W, Yang L, Zhang L et al (2009) Understanding the impact of cross-band interference. In: ACM CoRoNet 2009, pp 19–24. doi:10.1145/1614235.1614241

    Article  Google Scholar 

  20. Isheden C, Fettweis GP (2010) Energy efficient multi-carrier link adaptation with sum rate-dependent circuit power. In: IEEE GLOBECOMM 2010, pp 1–6. doi:10.1109/GLOCOM.2010.5683700

    Google Scholar 

  21. Islam MN, Mandayam NB, Kompella S (2012) Optimal resource allocation and relay selection in bandwidth exchange based cooperative forwarding. In: IEEE WiOpt 2012, pp 192–199. http://ieeexplore.ieee.org/document/6260454/. Accessed Nov 2016

    Google Scholar 

  22. Islam MN, Sampath A, Maharshi A et al (2014) Wireless backhaul node placement for small cell networks. In: IEEE CISS 2014, pp 1–6. doi:10.1109/CISS.2014.6814156

    Google Scholar 

  23. Jia J, Zhuang W (2011) Capacity of multi-hop wireless network with frequency agile software defined radio. In: IEEE INFOCOM WKSHPs 2011 Apr, pp 41–46. doi:10.1109/INFCOMW.2011.5928850

  24. Kodialam M, Nandagopal T (2005) Characterizing the capacity region in multi-radio multi-channel wireless mesh networks. In: ACM MOBICOM 2005 Aug, pp 73–87. doi:10.1145/1080829.1080837

  25. Kyasanur P, Vaidya NH (2005) Capacity of multi-channel wireless networks: impact of number of channels and interfaces. In: ACM MOBICOM 2005 Aug, pp 43–57. doi:10.1145/1080829.1080835

  26. Li Y, Bakkaloglu B, Chakrabarti C (2007) A system level energy model and energy quality evaluation for integrated transceiver front-ends. In: Proceedings of IEEE Transactions on VLSI 2007, vol 15, no 1, pp 90–103. doi:10.1109/TVLSI.2007.891095

    Google Scholar 

  27. Li G, Xu Z, Xiong C et al (2011) Energy efficient wireless communications: tutorial, survey, and open issues. In: IEEE TWC 2011, vol 18, no 6, pp 28–35. doi:10.1109/MWC.2011.6108331

    Google Scholar 

  28. Lin YP, Vaidyanathan PP (1998) Periodically nonuniform sampling of bandpass signals. In: IEEE TCS II 1998, vol 43, no 3, pp 340–351. doi:10.1109/82.664240

    Google Scholar 

  29. Liu HC, Min JS, Samueli H (1996) A low power baseband receiver IC for frequency-hopped spread spectrum communications. In: Proceedings of IEEE Journal of SSC 1996, vol 31, no 3, pp 384–394. doi:10.1109/4.494200

    Google Scholar 

  30. Mishali M, Eldar YC (2011) From theory to practice: sub-Nyquist sampling of sparse wideband analog signals. In: IEEE JSTSP 2010, vol 4, no 2, pp 375–391. doi:10.1109/JSTSP.2010.2042414

    Google Scholar 

  31. Mosek optimization. http://www.mosek.com/. Accessed Jan 2016

  32. Ochiai H, Imai H (2001) On the distribution of peak-to-average power ratio in OFDM signals. In: IEEE Transactions on COM 2001, vol 49, no 2, pp 282–289. doi:10.1109/26.905885

    MathSciNet  MATH  Google Scholar 

  33. Sherali HD, Adams WP (1999) A reformulation linearization technique for solving discrete and continuous nonconvex problems. Kluwer Academic Publishers, Dordrecht/Boston/London

    Book  MATH  Google Scholar 

  34. Shi Y, Hou YT (2007) Optimal power control for multi-hop software defined radio networks. In: IEEE INFOCOM 2007, pp 1694–1702. doi:10.1109/INFCOM.2007.198

    Article  Google Scholar 

  35. Shi Y, Hou T et al (2008) A distributed optimization algorithm for multi-hop cognitive radio networks. In: IEEE INFOCOM 2008, pp 1292–1300. doi:10.1109/INFOCOM.2008.186

    Google Scholar 

  36. Shi Y, Hou T, Komeplla S et al (2011) Maximizing capacity in multihop cognitive radio networks under the SINR model. In: IEEE TMC 2011, vol 10, no 7, pp 954–967. doi:10.1109/TMC.2010.204

    Google Scholar 

  37. Show my white space. http://whitespaces.spectrumbridge.com/whitespaces. Accessed Jan 2016

  38. Xu H, Li B (2010) Efficient resource allocation with flexible channel cooperation in OFDMA cognitive radio networks. In: IEEE INFOCOM 2010, pp 1–9. doi:10.1109/INFCOM.2010.5462169

    Google Scholar 

  39. Yang L, Hou W, Cao L et al (2010) Supporting demanding wireless applications with frequency agile radios. In: USENIX Symposium on NSDI 2010, pp 1–5

    Google Scholar 

  40. Yang L, Zhao BY, Zheng H (2010) The spaces between us: setting and maintaining boundaries in wireless spectrum access. In: ACM MOBICOM 2010, pp 37–48. doi:10.1145/1859995.1860001

    Google Scholar 

  41. Yang L, Zhang Z, Hou W et al (2011) Papyrus: a software platform for distributed dynamic spectrum sharing using SDRs. In: ACM SIGCOMM 2011, vol 41, no 1, pp 31–37. doi:10.1145/1925861.1925866

    Google Scholar 

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Acknowledgements

This work is supported in part by a grant from the US Office of Naval Research (ONR) under grant number N000014-15-1-2168. The work of S. Kompella is supported directly by the Office of Naval Research.

Author information

Authors and Affiliations

  1. Qualcomm Corporate R&D, 500 Somerset Corporate Boulevard, 08807, Bridgewater, NJ, USA

    Muhammad Nazmul Islam

  2. WINLAB, Department of ECE, Rutgers University, 671, Route 1 South, 08902-3390, North Brunswick, NJ, USA

    Narayan B. Mandayam

  3. WINLAB, Rutgers University, 671, Route 1 South, North Brunswick, NJ, USA

    Ivan Seskar

  4. Information Technology Division, Naval Research Laboratory, 4555 Overlook Avenue SW, 20375, Washington, DC, USA

    Sastry Kompella

Authors
  1. Muhammad Nazmul Islam
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  2. Narayan B. Mandayam
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  3. Ivan Seskar
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  4. Sastry Kompella
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Corresponding author

Correspondence to Muhammad Nazmul Islam .

Editor information

Editors and Affiliations

  1. Electrical Engg Bldg, Rm 325, The University of New South Wales, Sydney, New South Wales, Australia

    Wei Zhang

Section Editor information

  1. Department of Information Engineering, The Chinese University of Hong Kong, Room 834, Ho Sin Hang Engineering Building, Shatin, New Territory, Hong Kong, China SAR

    Jianwei Huang

  2. School of Data and Computer Science, Sun Yat-sen University, 510006, Guangzhou, Guangdong, 中国

    Xu Chen

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Islam, M.N., Mandayam, N.B., Seskar, I., Kompella, S. (2017). System Power Minimization in Non-contiguous Spectrum Access. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1389-8_24-1

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  • DOI: https://doi.org/10.1007/978-981-10-1389-8_24-1

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  • Print ISBN: 978-981-10-1389-8

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