Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Energy Efficient Architecture Using Hardware Acceleration for Software Defined Radio Components

  • Liu, Chen (Dept. of Electrical and Computer Engineering, Florida International University) ;
  • Granados, Omar (Dept. of Electrical and Computer Engineering, Florida International University) ;
  • Duarte, Rolando (Dept. of Electrical and Computer Engineering, Florida International University) ;
  • Andrian, Jean (Dept. of Electrical and Computer Engineering, Florida International University)
  • Received : 2011.07.28
  • Accepted : 2011.09.05
  • Published : 2012.03.31
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Abstract

In order to make cognitive radio systems a practical technology to be deployed in real-world scenarios, the core Software Defined Radio (SDR) systems must meet the stringent requirements of the target application, especially in terms of performance and energy consumption for mobile platforms. In this paper we present a feasibility study of hardware acceleration as an energy-efficient implementation for SDR. We identified the amplifier function from the Software Communication Architecture (SCA) for hardware acceleration since it is one of the functions called for most frequently and it requires intensive floating-point computation. Then, we used the Virtex5 Field-Programmable Gate Array (FPGA) to perform a comparison between compiler floating-point support and the on-chip floating-point support. By enabling the on-chip floating-point unit (FPU), we obtained as high as a 2X speedup and 50% of the overall energy reduction. We achieved this with an increase of the power consumption by no more than 0.68%. This demonstrates the feasibility of the proposed approach.

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References

  1. Falahati, S.; Svensson, A.; Ekman, T.; Sternad, M.; , "Adaptive modulation systems for predicted wireless channels," Communications, IEEE Transactions on , Vol.52, No.2, February, 2004, pp.307- 316. https://doi.org/10.1109/TCOMM.2003.822715
  2. "FCC - Notice of proposed rulemaking and order, facilitating opportunities for flexible, efficient and reliable spectrum use employing cognitive radio technologies," FCC Document ET Docket No.03-108, December, 2003.
  3. Feng Ge; Qinqin Chen; Ying Wang; Bostian, C.W.; Rondeau, T.W.; Bin Le; , "Cognitive Radio: From Spectrum Sharing to Adaptive Learning and Reconfiguration," Aerospace Conference, 2008 IEEE , Vol., No., 1-8 March, 2008, pp.1-10.
  4. I. Mitola, J. and J. Maguire, G. Q., "Cognitive radio: making software radios more personal," IEEE Personal Commun. Mag., Vol.6, No.4, August, 1999, pp.13-18. https://doi.org/10.1109/98.788210
  5. Jondral, F.K.; , "Cognitive Radio: A Communications Engineering View," Wireless Communications, IEEE , Vol.14, No.4, August, 2007, pp.28-33.
  6. Shukla, A.; Burbidge, E.; Usman, I.; , "Cognitive Radios - What are They and Why are the Military and Civil Users Interested in Them," Antennas and Propagation, 2007. EuCAP 2007. The Second European Conference on , Vol., No., 11-16 November, 2007, pp.1-10.
  7. D. Lau, J. Blackburn, and C. Jenkins, "Using c-to-hardware acceleration in FPGAs for waveform baseband processing," in 2006 Software Defined Radio Technical Conf. Product Exposition, November, 2006.
  8. Millage, J.G., "GPU Integration into a Software Defined Radio Framework," Master's Thesis, Iowa State University, 2010.
  9. Haghighat, A.;, "A review on essentials and technical challenges of software defined radio," MILCOM 2002. Proceedings , Vol.1, No., Vol.1, 7-10 October, 2002, pp.377-382.
  10. Tucker, D.C.; Tagliarini, G.A.; , "Prototyping with GNU radio and the USRP - where to begin," Southeastcon, 2009. SOUTHEASTCON '09. IEEE , Vol., No., 5-8 March, 2009, pp.50-54.
  11. Chapin, J.; Lum, V.; Muir, S.; , "Experiences implementing GSM in RDL (the Vanu Radio Description $Language^{TM}$),"Military Communications Conference, 2001. MILCOM 2001. Communications for Network-Centric Operations: Creating the Information Force. IEEE , Vol.1, No., Vol.1, 2001, pp.213- 217.
  12. Gailliard, G.; Nicollet, E.; Sarlotte, M.; Verdier, F.; , "Transaction Level Modelling of SCA Compliant Software Defined Radio Waveforms and Platforms PIM/PSM," Design, Automation & Test in Europe Conference & Exhibition, 2007. DATE '07 , Vol., No., 16-20 April, 2007, pp.1-6.
  13. Reinhart, R.C.; Johnson, S.K.; Kacpura, T.J.; Hall, C.S.; Smith, C.R.; Liebetreu, J.; , "Open Architecture Standard for NASA's Software-Defined Space Telecommunications Radio Systems," Proceedings of the IEEE , Vol.95, No.10, October, 2007, pp.1986-1993.
  14. P. Isomäki, N. Avessta, An overview of software defined radio technologies, Tech. Rep. 652, Turku Centre for Computer Science, Finland, 2004.
  15. Hayes, N.; , "The JTRS SCA specification-the past, the present, and the future," Military Communications Conference, 2005. MILCOM 2005. IEEE , Vol., No., Vol.5, 17-20, October, 2005, pp.2713- 2719.
  16. Aguayo Gonzalez, C.R.; Dietrich, C.B.; Reed, J.H.; , "Understanding the software communications architecture," Communications Magazine, IEEE , Vol.47, No.9, September, 2009, pp.50-57.
  17. Virtex-5 FPGA User Guide. [Online]. Available: http://www.xilinx.com/support/documentation/user_guides/ug190.pdf
  18. MicroBlaze Processor Reference Guide. [Online]. Available: http://www.xilinx.com/support/ documentation/sw_manuals/ mb_ref_guide.pdf
  19. Xilinx Xpower Analyzer [Online]. Available: http://www.xilinx.com/products/design_tools/logic_design/ verification/xpower_an.htm

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