Recursive Least Square ( RLS ) Based Channel Estimation for MIMO - OFDM System

Channel State information can be determined by adaptive filtering algorithms for wireless channels. For slow fading channels, simplified channel estimators can be exploited such as Least Square Error (LSE) and Linear Minimum Mean Square Error (LMMSE). But for fast fading channels, the matrix inversion required in case of LMMSE has to be taken recursively which increase the complexity. Under such conditions adaptive filtering algorithms are used to reduce the complexity with better performance. LMS, RLS and Kalman Filtering techniques can be used. But in wireless MIMO channels normally RLS and Kalman Filter are used at the cost of more complexity as compared to LMS which has better computational efficiency and feasibility. For initialization of adaptive filter, the channel can be estimated by LSE or LMMSE initially. In this paper the performance of RLS for both initially estimated LSE and LMMSE channel is compared in terms of Mean Square Error (MSE) and complexity is evaluated in terms of computational time. Optimization of LSE-RLS and LMMSE-RLS is performed as a function of wireless channel taps and Channel Impulse Response (CIR) samples. Monte-Carlo Simulations are carried for RLS channel estimation algorithm. [Saqib Saleem, Qamar-ul-Islam. Recursive Least Square (RLS) Based Channel Estimation for MIMO-OFDM System. Life Science Journal 2012;9(2);14-19]. (ISSN: 1097-8135). http://www.lifesciencesite.com. 3