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Energy-Efficient Channel Estimation in MIMO Systems


The emergence of MIMO communications systems as practical high-data-rate wireless communications systems has created several technical challenges to be met. On the one hand, there is potential for enhancing system performance in terms of capacity and diversity. On the other hand, the presence of multiple transceivers at both ends has created additional cost in terms of hardware and energy consumption. For coherent detection as well as to do optimization such as water filling and beamforming, it is essential that the MIMO channel is known. However, due to the presence of multiple transceivers at both the transmitter and receiver, the channel estimation problem is more complicated and costly compared to a SISO system. Several solutions have been proposed to minimize the computational cost, and hence the energy spent in channel estimation of MIMO systems. We present a novel method of minimizing the overall energy consumption. Unlike existing methods, we consider the energy spent during the channel estimation phase which includes transmission of training symbols, storage of those symbols at the receiver, and also channel estimation at the receiver. We develop a model that is independent of the hardware or software used for channel estimation, and use a divide-and-conquer strategy to minimize the overall energy consumption.



  1. Foschini GJ, Gans MJ: On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications 1998,6(3):311–335. 10.1023/A:1008889222784

    Article  Google Scholar 

  2. Rajagopal S, Bhashyam S, Cavallaro JR, Aazhang B: Efficient VLSI architectures for multiuser channel estimation in wireless base-station receivers. The Journal of VLSI Signal Processing 2002,31(2):143–156. 10.1023/A:1015393322264

    Article  MATH  Google Scholar 

  3. Dietl G, Utschick W: On reduced-rank approaches to matrix Wiener filters in MIMO systems. Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (ISSPIT '03), December 2003, Darmstadt, Germany 82–85.

  4. Sun Y, Honig ML, Tripathi V: Adaptive, iterative, reduced-rank equalization for MIMO channels. Proceedings of Military Communications Conference (MILCOM '02), October 2002, Anaheim, Calif, USA 2: 1029–1033.

  5. Molisch AF, Win MZ: MIMO systems with antenna selection. IEEE Microwave Magazine 2004,5(1):46–56. 10.1109/MMW.2004.1284943

    Article  Google Scholar 

  6. Cui S, Goldsmith AJ, Bahai A: Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE Journal on Selected Areas in Communications 2004,22(6):1089–1098. 10.1109/JSAC.2004.830916

    Article  Google Scholar 

  7. Viswanathan H, Balakrishnan J: Space-time signaling for high data rates in EDGE. IEEE Transactions on Vehicular Technology 2002,51(6):1522–1533. 10.1109/TVT.2002.804862

    Article  Google Scholar 

  8. Whaley RC, Dongarra JJ: Automatically tuned linear algebra software. Proceedings of 10th Anniversary. International Conference on High Performance Computing and Communications (SC '98), November 1998, Orlando, Fla, USA 33.

    Google Scholar 

  9. Whaley RC, Petitet A, Dongarra JJ: Automated empirical optimization of software and the ATLAS project. ATLAS project, 2000,

    Google Scholar 

  10. Henning R, Chakrabarti C: A quality/energy tradeoff approach for IDCT computation in MPEG-2 video decoding. Proceedings of IEEE Workshop on Signal Processing Systems (SiPS '00), October 2000, Lafayette, La, USA 90–99.

    Google Scholar 

  11. Catthoor F, de Greef E, Suytack S: Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design. Kluwer Academic, Norwell, Mass, USA; 1998.

    Book  MATH  Google Scholar 

  12. Waters D: Complexity analysis of MIMO detectors. on line publication, 2003,

    Google Scholar 

  13. Golub GH, Van Loan CF: Matrix Computations. 3rd edition. Johns Hopkins University Press, Baltimore, Md, USA; 1996.

    MATH  Google Scholar 

  14. Press WH, Flannery BP, Teukolsky SA, Vetterling WT: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK; 1992.

    Google Scholar 

  15. Hardy GH, Ramanujan S: Asymptotic formulae in combinatory analysis. Proceedings of the London Mathematical Society 1918,17(2):75–115.

    Article  MathSciNet  Google Scholar 

  16. Nocedal J, Wright SJ: Numerical Optimization. Springer, New York, NY, USA; 1999.

    Book  MATH  Google Scholar 

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Correspondence to Sarod Yatawatta.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yatawatta, S., Petropulu, A.P. & Graff, C.J. Energy-Efficient Channel Estimation in MIMO Systems. J Wireless Com Network 2006, 027694 (2006).

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