Higher order statistics-based inverse filter criteria (IFC) have been effectively used for blind equalization of single-input multiple-output (SIMO) systems. Recently, Chi and Chen reported a relationship between the unknown SIMO system and the optimum equalizer designed by the IFC for finite signal-to-noise ratio (SNR). In this paper, based on this relationship, an iterative fast Fourier transform (FFT)-based nonparametric blind system identification (BSI) algorithm and an FFT-based multiple-time-delay estimation (MTDE) algorithm are proposed with a given set of non-Gaussian measurements. The proposed BSI algorithm allows the unknown SIMO system to have common subchannel zeros, and its performance (estimation accuracy) is superior to that of the conventional IFC-based methods. The proposed MTDE algorithm can simultaneously estimate all the ( 1) time delays (with respect to a reference sensor) with space diversity of sensors exploited; therefore, its performance (estimation accuracy) is robust to the nonuniform distribution of SNRs of 2 sensors (due to channel fading). Some simulation results are presented to support the efficacy of the proposed BSI algorithm and MTDE algorithm.Index Terms-Blind system identification, cumulant-based inverse filter criteria, higher order statistics, time delay estimation.
Chi et al. proposed a fast kurtosis maximization algorithm (FKMA) for blind equalization/deconvolution of multiple-input multiple-output (MIMO) linear time-invariant systems. This algorithm has been applied to blind multiuser detection of singlerate direct-sequence/code-division multiple-access (DS/CDMA) systems and blind source separation (or independent component analysis). In this paper, the FKMA is further applied to blind multiuser detection for multirate DS/CDMA systems. The ideas are to properly formulate discrete-time MIMO signal models by converting real multirate users into single-rate virtual users, followed by the use of FKMA for extraction of virtual users' data sequences associated with the desired user, and recovery of the data sequence of the desired user from estimated virtual users' data sequences. Assuming that all the users' spreading sequences are given a priori, two multirate blind multiuser detection algorithms (with either a single receive antenna or multiple antennas), which also enjoy the merits of superexponential convergence rate and guaranteed convergence of the FKMA, are proposed in the paper, one based on a convolutional MIMO signal model and the other based on an instantaneous MIMO signal model. Some simulation results are then presented to demonstrate their effectiveness and to provide a performance comparison with some existing algorithms.
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