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Energy-Efficient Channel Estimation in MIMO Systems
EURASIP Journal on Wireless Communications and Networking volume 2006, Article number: 027694 (2006)
Abstract
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.
References
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
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
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.
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.
Molisch AF, Win MZ: MIMO systems with antenna selection. IEEE Microwave Magazine 2004,5(1):46–56. 10.1109/MMW.2004.1284943
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
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
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.
Whaley RC, Petitet A, Dongarra JJ: Automated empirical optimization of software and the ATLAS project. ATLAS project, 2000, http://netlib.org/atlas/
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.
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.
Waters D: Complexity analysis of MIMO detectors. on line publication, 2003, http://users.ece.gatech.edu/~deric/Projects/MIMO/Complexity.pdf
Golub GH, Van Loan CF: Matrix Computations. 3rd edition. Johns Hopkins University Press, Baltimore, Md, USA; 1996.
Press WH, Flannery BP, Teukolsky SA, Vetterling WT: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK; 1992.
Hardy GH, Ramanujan S: Asymptotic formulae in combinatory analysis. Proceedings of the London Mathematical Society 1918,17(2):75–115.
Nocedal J, Wright SJ: Numerical Optimization. Springer, New York, NY, USA; 1999.
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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). https://doi.org/10.1155/WCN/2006/27694
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DOI: https://doi.org/10.1155/WCN/2006/27694