Modified Gradient Algorithm based Noise Subspace Estimation with Full Rank Update for Blind CSI Estimator in OFDM Systems

This paper presents a modified Gradient-based method to directly compute the noise subspace iteratively from the received Orthogonal Frequency Division Multiplexing (OFDM) symbols to estimate Channel State Information (CSI). By invoking the matrix inversion lemma which is extensively used in Recursive Least Square (RLS) algorithms, the proposed computationally efficient method enables direct computation of noise subspace using the inverse of the autocorrelation matrix of the received OFDM symbols. In the case of a vector input, the modified Gradient algorithm uses rank one update to calculate noise subspace recursively. For an input in the matrix form, the modified Gradient algorithm uses a full rank update. The validity, efficacy, and accuracy of the proposed modified Gradient algorithm have been substantiated through a relative comparison of the results with the conventional Singular Value Decomposition (SVD) algorithm, which is in wide use in the estimation of the subspaces. The simulation results obtained through the modified Gradient algorithm show a satisfactory correlation with the results of SVD, even though the computational complexity involved in modified Gradient is relatively less. Apart from the results encompassing various power levels of the multipath channel, this paper also discusses the adaptive tracking of CSI and presents a comparative study.