From a mixed signal in which a first signal and a second signal are mixed, the second signal is removed at low processing cost and without delay. As a result, an estimated first signal which has low residue of the second signal and low distortion is obtained. An estimated first signal is generated by subtracting a pseudo second signal which is estimated to be mixed in a first mixed signal in which a first signal and a second signal are mixed from the first mixed signal. The pseudo second signal is obtained by a first adaptive filter using a second mixed signal in which the first signal and the second signal are mixed in a different proportion from the first mixed signal. A coefficient update amount of the first adaptive filter is made smaller as compared with a case when the estimated first signal is smaller than the first mixed signal, in case the estimated first signal is larger than the first mixed signal.

A signal-of-interest (SOI) may be separated from interference and/or noise using repeated reduced rank minimum mean-square error Fractional Fourier Transform (MMSE-FrFT) filtering and a low rank adaptive multistage Wiener filter (MWF). A number of stages in the MWF, L, may be chosen such that at the L.sup.th stage, the MSE between the SOI estimate and the true SOI is less than or equal to an error threshold (e.g., =0.001). By combining these filtering techniques, significant improvement in reducing the mean-square error (MSE) may be realized over single stage MMSE-FrFT, repeated MMSE-FrFT, and MMSE-FFT algorithmsindeed, by an order of magnitude or more.

A signal-of-interest (SOI) may be separated from interference and/or noise using repeated reduced rank minimum mean-square error Fractional Fourier Transform (MMSE-FrFT) filtering and a low rank adaptive multistage Wiener filter (MWF). A number of stages in the MWF, L, may be chosen such that at the L.sup.th stage, the MSE between the SIM estimate and the true SW is less than or equal to an error threshold E (e.g., =0.001). By combining these filtering techniques, significant improvement in reducing the mean-square error (MSE) may be realized over single stage MMSE-FrFT, repeated MMSE-FrFT, and MMSE-FFT algorithms indeed, by an order of magnitude or more.

An adaptation hardware accelerator comprises a calculation unit to receive inputs at predefined time interval(s) that correspond to a calculation iteration, the inputs associated with adaptive filters having taps, and determine correlation and cross-correlation data based thereon for a given iteration. The correlation data comprises a correlation matrix. Determining the matrix comprises determining submatrices in an upper triangular portion and a diagonal portion of the matrix. The accelerator comprises an adaptation core unit to determine adaptive weights associated with the adaptive filters, respectively, based on an adaptive algorithm, utilizing the correlation and cross correlation data. The accelerator unit comprises a convergence detector unit to determine a convergence parameter; and a controller to generate an iteration signal for each time interval based on the parameter. The iteration signal communicates to continue or conclude; the conclusion indicates determination of a final value of adaptive weights by the core unit.

An adaptation hardware accelerator comprises a calculation unit configured to receive a plurality of inputs at one or more predefined time intervals, wherein each time interval corresponds to a calculation iteration, the plurality of inputs being associated with a plurality of adaptive filters each having a plurality of taps, and determine a correlation data and a cross-correlation data based thereon for a given calculation iteration. The correlation data comprises a correlation matrix comprising a plurality of sub-matrices, wherein determining the correlation matrix comprises determining only the submatrices in an upper triangular portion and a diagonal portion of the correlation matrix. Further, the adaptation hardware accelerator comprises an adaptation core unit configured to determine a plurality of adaptive weights associated with the plurality of adaptive filters, respectively, based on an optimized RLS based adaptive algorithm, by utilizing the correlation data and the cross correlation data. In addition, the hardware accelerator unit comprises a convergence detector unit configured to determine a convergence parameter; and a controller configured to generate an iteration signal for each of the predefined time intervals based on the convergence parameter. The iteration signal communicates to the calculation unit and the adaptation core unit to continue with a next calculation iteration or to conclude, wherein the conclusion indicates a determination of a final value of the plurality of the adaptive weights by the adaptation core unit.