Optimum-Weighted RLS Channel Estimation for Rapid Fading MIMO Channels

• Published in: IEEE transactions on wireless communications Vol. 7; no. 11; pp. 4248 - 4260
• Main Author: Akino, T.K.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.11.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive noise; Algorithms; Applied sciences; Channel estimation; Channels; Computational complexity; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Fading; fast fading channel; Information, signal and communications theory; Least squares method; MIMO; Mobile communication; multiple-input multiple-output (MIMO); Optimization; optimum-weighting; Parameter estimation; Polynomials; Radio communication; Recursive; recursive algorithm; Resonance light scattering; Signal and communications theory; Signal, noise; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Tracking; Transmission and modulation (techniques and equipments); Weighted least-squares (LS) channel estimation; Wireless communication; Fading channels; Performance evaluation; Fading; MIMO system; Zero order; Parameter estimation; Forgetting factor; Wireless telecommunication; Time correlation; Mobile communication; multiple-input multiple-output (MIMO); optimum-weighting; Recursive algorithm; Computational complexity; Mean square error; Weighting; Algorithm performance; Least squares method; Channel estimation; Weighting coefficient; Recursive method; fast fading channel; Multiple channel; Weighted least-squares (LS) channel estimation
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/T-WC.2008.070497

This paper investigates on an accurate channel estimation scheme for fast fading channels in multiple-input multiple-output (MIMO) mobile communications. A high-order exponential-weighted recursive least-squares (EW-RLS) method has been known as a good channel estimation scheme in rapid fading. However, there exists a drawback that we need to properly adjust the estimation parameters of a forgetting factor and an estimation order according to the channel environment. In this paper, we theoretically derive an optimum-weighted LS (OW-LS) channel estimation based on the statistical knowledge of the spatio-temporal channel correlation. Through the analysis, we reveal that the zero-th order polynomial becomes optimal when the optimum-weighting is employed. Furthermore, we propose an efficient recursive algorithm for channel tracking in order to reduce the computational complexity. Since the proposed scheme automatically adapts the weighting coefficients to the channel condition, it has a significant advantage in mean-square error (MSE) performance compared to the EW-RLS scheme.