Open Access

Energy-Efficient Channel Estimation in MIMO Systems

  • Sarod Yatawatta1Email author,
  • Athina P Petropulu1 and
  • Charles J Graff2
EURASIP Journal on Wireless Communications and Networking20062006:027694

DOI: 10.1155/WCN/2006/27694

Received: 14 February 2005

Accepted: 5 December 2005

Published: 20 March 2006


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.


Authors’ Affiliations

Electrical and Computer Engineering Department, Drexel University


  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:1008889222784View ArticleGoogle 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:1015393322264MATHView ArticleGoogle 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.2004.1284943View ArticleGoogle 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.2004.830916View ArticleGoogle 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.2002.804862View ArticleGoogle 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, 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.MATHView ArticleGoogle Scholar
  12. Waters D: Complexity analysis of MIMO detectors. on line publication, 2003, Scholar
  13. Golub GH, Van Loan CF: Matrix Computations. 3rd edition. Johns Hopkins University Press, Baltimore, Md, USA; 1996.MATHGoogle 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.MathSciNetView ArticleGoogle Scholar
  16. Nocedal J, Wright SJ: Numerical Optimization. Springer, New York, NY, USA; 1999.MATHView ArticleGoogle Scholar


© Sarod Yatawatta et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.