A Novel Frequency-Domain Independent Component Analysis Approach for Wireless Communications

In this paper, a novel Frequency-Domain Independent Component Analysis (ICA-F) approach is proposed to blindly separate and deconvolve the convolutive combinations of digitally modulated signals in wireless communications. This approach relies on the simple observation that if signals are independent in one domain, their corresponding components in a linearly transformed domain are also independent. The proposed ICA-F lends itself to computationally efficient Fast Fourier Transform (FFT) implementation, which converts the convolutive combination in the time domain into multiple instantaneous combinations in the frequency domain. Then, the natural-gradient Independent Component Analysis (ICA) algorithm is employed in each frequency bin to the separate frequency components of source signals. The permutation and gain ambiguities associated with the ICA algorithm are successfully solved. The ICA-F has lower computational complexity and faster convergence than the existing time-domain approach. Simulation results confirm the effectiveness of the proposed ICA-F. Key-Words: Blind Equalizer, Blind Source Separation, Constant Modulus Algorithm, Gain Ambiguity, Independent Component Analysis, Permutation Ambiguity, Short-Time Fourier Transform