Using ICA to improve blind subspace-based channel estimation for OFDM system under unknown noise fields

Abstract

Blind subspace-based channel estimation methods have received considerable attention for multicarrier communication systems with virtual carriers over white Gaussian noises. In this paper, orthogonal frequency division multiplexing (OFDM) system without any training symbols or any prior knowledge of the noise covariance matrix is considered under unknown noise fields. No single subspace feature extraction method outperforms others under all circumstances, but applying a dual-space feature extraction method can overcome the limits of single subspace. A robust dual-space feature extraction is put forward based on minor component analysis (MCA) and independent component analysis (ICA) to increase the channel estimate accuracy. Especially, the proposed feature extraction always starts the procedure as a pure-MCA and ends as a pure-ICA. Once the new noise basis vectors are obtained, which is as orthogonal as possible to the estimated noise basis vectors especially for highly correlated and impulse noises, the noise subspace can be reconstructed for the MCA-based estimator. Computer simulation is provided for illustrating the effectiveness of the proposed method.