Modified Bi-SVD and Modified Gradient Algorithms for Noise Subspace Estimation with Full Rank Update for Blind CFO Estimator in OFDM Systems
Saravanan Subramanian, Govind R. Kadambi · International Journal on Communications Antenna and Propagation (IRECAP) · 2022
This paper proposes a modified Gradient algorithm and a modified Bi-SVD algorithm for the estimation of noise subspace and their utility in blind CFO estimation. The proposed modified Bi-SVD and modified Gradient algorithms have the dual advantageous features of not only the improved of noise subspace estimation but also the reduced computational complexities. The results obtained through the modified Gradient and modified Bi-SVD algorithms show excellent agreement with the results of the direct SVD technique requiring relatively more computational complexity. Another important contribution of this paper is the adoption of full-rank updated inverse autocorrelation matrix Rinv into the proposed noise subspace estimation algorithms. The improved noise estimation algorithms have been eventually used to aid the MUSIC algorithm to facilitate improved estimation of CFO required in OFDM systems. This paper substantiates the envisaged improved estimation accuracy of CFO through simulation results derived through proposed algorithms relative to conventional SVD.