MIMO Channel Estimation by LMS and RLS Algorithms and QR Decomposition Method

Document Type : Original Article

Authors

1 Semnan University

2 Faculty of Electrical and Computer Engineering, University of Semnan

Abstract

Different method are used for channel estimation in MIMO systems, Each of which has different functionality. MIMO systems are like equations with multiple variables, variables are transmitted symbols by transmitter (M) and equations are received symbols in receiver (N). Different mathematical methods are for decomposition of channel matrix, than Q&R Decomposition is one of these methods. Using decomposition for channel matrix with adaptive algorithms such as LMS and RLS in MIMO systems will simplify and reduce complexity. In this paper, the MIMO channel will be modeled by LMS and RLS algorithms and instead directly of estimated the channel matrix H that very complexity, in QR matrix, first estimated Q and then estimated R, that the channel matrix is estimated. According result, in MIMO systems, channel modeling with LMS and RLS adaptive algorithms and applying QR decomposition will result in the error reduction.

Keywords


[1] M. Alamouti, “A Simple Transmit Diversity Technique for Wireless Communications,” IEEE J. Sel. Areas Commun, vol. 16, no. 8, pp. 1451- 1458, 1998.
[2] Cicerone, O. Simeone, and U. Spagnolini. "Channel estimation for MIMO-OFDM systems by modal analysis/filtering." IEEE Trans. on Commun, vol. 54, no. 11, pp.: 2062-2074, 2006.
[3] Volker, et al. "How often channel estimation is needed in MIMO systems." IEEE Global Telecommunications Conference (IEEE Cat. No. 03CH37489). vol. 2, 2003.
[4] Yamaguchi, et al. "Channel prediction techniques for a multi-user MIMO system in time-varying environments." IEICE Trans. on Commun., vol. 97, no. 12, pp. 2747-2755, 2014.
[5] Honma, "Method of MIMO channel estimation between parasitic antenna arrays." IEEE transactions on antennas and propagation, vol. 61, no. 5, pp. 2792-2800, 2013.
[6] Pasangi, M. Atashbar, and M. Mohassel Feghhi. "Blind downlink channel estimation of multi-user multi-cell massive MIMO system in presence of the pilot contamination." AEU-International Journal of Electronics and Communications, vol. 117, pp. 153099, 2020.‏
[7] Shalavi, M. Atashbar, and M. Mohassel Feghhi. "Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling." Physical Communication, vol. 42, pp. 101138, 2020.‏
[8] Pasangi, M. Atashbar, and M. Mohassel Feghhi. "Blind downlink channel estimation for TDD‐based multiuser massive MIMO in the presence of nonlinear HPA." ETRI Journal, vol. 41, no. 4, pp. 426-436, 2019.
[9] Gao, et al. "Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO." IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6169-6183, 2015.
Sun, and T. S. Rappaport. "Millimeter wave MIMO channel estimation based on adaptive compressed sensing." IEEE International Conference on Communications Workshops (ICC Workshops), 2017.
Ge, et al. "Compression-based lmmse channel estimation with adaptive sparsity for massive MIMO in 5G systems." IEEE Systems Journal, vol. 13, no. 4, pp. 3847-3857, 2019.‏
Md. Masud. "Performance comparison of LMS and RLS channel estimation algorithms for 4G MIMO OFDM systems." 14th International Conference on Computer and Information Technology (ICCIT), 2011.‏
A. Ghauri, and M. F. Sohail. "System identification using LMS, NLMS and RLS." IEEE Student Conference on Research and Development, 2013.
Lo, and V. Tarokh. "Space-time block coding-from a physical perspective." IEEE Wireless Communications and Networking Conference (Cat. No. 99TH8466). vol. 1, 1999.
Liu, Tsung-Hsien, et al. "Block-wise QR-decomposition for the layered and hybrid alamouti STBC MIMO systems: Algorithms and hardware architectures." IEEE Transactions on Signal Processing, vol. 62, no. 18, pp. 4737-4747, 2014.
Cescato, and H. Bölcskei. "Algorithms for interpolation-based QR decomposition in MIMO-OFDM systems." IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1719-1733, 2011.
Golub, and F. Uhlig. "The QR algorithm: 50 years later its genesis by John Francis and Vera Kublanovskaya and subsequent developments." IMA Journal of Numerical Analysis, drp012, 2009.
Farhang-Boroujeny, Adaptive filters: theory and applications. John Wiley & Sons, 1999.