On the robustness of FIR channel identification from fractionally spaced received signal second-order statistics

Endres, T.J.; Anderson, Brian D. O.; Johnson, C.R.; Tong, Lang

doi:10.1109/97.491659

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…See Appendix A for the proof of Property 1. Property 1 and C1) imply that can be zero phase, which further leads to the following property about the system given by (13). Property 2: Assume that is zero-phase and that ) the number of zeros of on the unit circle is finite.…”

Section: A Analysis About the Systems Solved From (11) Under Constramentioning

confidence: 95%

“…With our experience, assumption generally holds true in practical applications and is never an issue in the design of BSI algorithms. Again, one can see, from (5) and (13), that the left-hand side of (11) is a highly nonlinear function of , implying that a closed-form solution of (11) for is almost formidable under constraint C1). Next, let us present an iterative FFT-based nonparametric algorithm for estimating under the constraint C1).…”

Section: A Analysis About the Systems Solved From (11) Under Constramentioning

confidence: 96%

“…T2) Update by . Update , where zero phase (20) and then obtain by (16) and (18) where is the -point FFT of obtained by (13). Normalize such that .…”

Section: B Algorithm For Obtaining the System Estimate From (11) Undmentioning

confidence: 99%

“…However, some of these methods require the knowledge of the system order, and their performance is sensitive to the system order mismatch [3]. Furthermore, a common identifiability condition referred to as the channel disparity condition, which is required by these subspace methods, is that the subchannels must be coprime (i.e., the subchannels have no common zeros), and these methods tend to fail when this condition is nearly violated [12], [13]. Recently, the NS method [7] that was modified by Ali et al [14] was shown to be robust in the presence of common subchannel zeros and the system order overestimation errors by virtue of exploiting the transmitter redundancy through the use of a trailing zero precoder.…”

Section: Introductionmentioning

confidence: 99%

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Blind Identification of SIMO Systems and Simultaneous Estimation of Multiple Time Delays From HOS-Based Inverse Filter Criteria

Chi

Chen

et al. 2004

IEEE Trans. Signal Process.

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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.

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Section: A Analysis About the Systems Solved From (11) Under Constramentioning

confidence: 95%

Section: A Analysis About the Systems Solved From (11) Under Constramentioning

confidence: 96%

“…T2) Update by . Update , where zero phase (20) and then obtain by (16) and (18) where is the -point FFT of obtained by (13). Normalize such that .…”

Section: B Algorithm For Obtaining the System Estimate From (11) Undmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Identification of SIMO Systems and Simultaneous Estimation of Multiple Time Delays From HOS-Based Inverse Filter Criteria

Chi

Chen

et al. 2004

IEEE Trans. Signal Process.

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“…10 However, not all channels can be equalized using the F-spaced setting. 11 There are other problems such as higher power consumption due to oversampling, parameter drift, 7 increased equalizer complexity and estimation of the minimum equalizer order. 12 Furthermore, the performance of F-spaced blind algorithms in fading channels is not well-understood.…”

Section: Introductionmentioning

confidence: 99%

Soft constraint satisfaction (SCS) blind channel equalization algorithms

Tanrikulu

Baykal

Constantinides

et al. 1998

Int. J. Adapt. Control Signal Process.

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SUMMARYThe constant modulus adaptive blind equalization algorithms presented in this paper are shown to correspond to an error performance surface which is much improved upon that of existing algorithms such as the wellknown constant modulus (or Godard) algorithm. Many undesirable local solutions (ULSs) are avoided by careful derivation. We use a deterministic optimization criterion with a soft constraint to obtain an update equation which contains a normalized gradient vector and a particular continuous non-linearity. This approach is extended to multiple constraints to yield faster converging algorithms. An autoregressive (AR) channel model is studied to demonstrate analytically the absence of a class of ULSs. Finally, the ÿndings are veriÿed experimentally for various AR and moving-average (MA) channels. ? 1998 John Wiley & Sons, Ltd.

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MIMO Blind Equalization Algorithms

Blind Equalization and System Identification

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On the robustness of FIR channel identification from fractionally spaced received signal second-order statistics

Cited by 9 publications

References 6 publications

Blind Identification of SIMO Systems and Simultaneous Estimation of Multiple Time Delays From HOS-Based Inverse Filter Criteria

Blind Identification of SIMO Systems and Simultaneous Estimation of Multiple Time Delays From HOS-Based Inverse Filter Criteria

Soft constraint satisfaction (SCS) blind channel equalization algorithms

MIMO Blind Equalization Algorithms

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