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Widely Linear Complex-Valued Least Mean M-Estimate Algorithms: Design and Performance Analysis

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Abstract

To utilize the full second-order statistical information of the complex-valued signal, a widely linear complex-valued LMM (WL-CLMM) algorithm is proposed by using different M-estimate functions. The proposed WL-CLMM algorithm can process both circular and noncircular complex-valued signals in impulsive noise environments. Moreover, a novel adaptive threshold adjustment method for the M-estimate function is designed according to the probability density function of the complex-valued error signal. In addition, to decrease the sensitivity of the input signal to the performance of the algorithm, the normalized version of WL-CLMM (WL-CNLMM) has been developed. Then, we carry out the mean behavior and mean square behavior analysis of the proposed algorithms. Simulation results show that the proposed algorithms outperform some existing complex-valued algorithms and the theoretical results are well matched.

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Data Availability

The data that support the finding of this study are available from the first author on request.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China under Grants 62171303, China South Industries Group Corporation (Chengdu) Fire Control Technology Center Project (non-secret) under Grants HK20-03, and the National Key Research and Development Program Foundation of China under Grants 2018YFC0830300.

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Appendix

Appendix

In this Appendix, Wirtinger calculus is briefly described. Wirtinger calculus provides a framework for differentiating nonanalytic functions and it allows performing all the derivations in the complex domain, in a manner very similar to the real-valued case. For a complex-valued function \(f(z)\), where \(z = z_{r} + jz_{i}\), two generalized complex derivatives can be defined as

$$ \frac{\partial f}{{\partial z}} \triangleq \frac{1}{2}\left( {\frac{\partial f}{{\partial z_{r} }} - j\frac{\partial f}{{\partial z_{i} }}} \right) $$
(50)
$$ \frac{\partial f}{{\partial z^{*} }} \triangleq \frac{1}{2}\left( {\frac{\partial f}{{\partial z_{r} }}{ + }j\frac{\partial f}{{\partial z_{i} }}} \right) $$
(51)

In fact, these generalized complex derivatives can be formally implemented by regarding \(f(z)\) as a bivariate function \(f(z,z^{*} )\) and treating \(z\) and \(z^{*}\) as independent variables. In other words, when applying \(\frac{\partial f}{{\partial z}}\), we take the derivative with respect to \(z\), while formally treating \(z^{*}\) as a constant. Similarly, \(z\) is formally treated as a constant when deriving \(\frac{\partial f}{{\partial z^{*} }}\). In particular, if \(\frac{\partial f}{{\partial z^{*} }} = 0\), the complex-valued function \(f(z)\) becomes complex derivative (analytic). So the Cauchy–Riemann equations can simply be stated as \(\frac{\partial f}{{\partial z^{*} }} = 0\). Moreover, Wirtinger calculus contains standard complex calculus as a special case. Finally, according to the rules of CR calculus, its chain rule can be summarized as

$$ \frac{\partial g(f)}{{\partial z}} = \frac{\partial g}{{\partial f}}\frac{{\partial f}}{\partial z} + \frac{\partial g}{{\partial f^{*} }}\frac{{\partial f^{*} }}{\partial z} $$
(52)
$$ \frac{\partial g(f)}{{\partial z^{*} }} = \frac{\partial g}{{\partial f}}\frac{{\partial f}}{{\partial z^{*} }} + \frac{\partial g}{{\partial f^{*} }}\frac{{\partial f^{*} }}{{\partial z^{*} }} $$
(53)

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Li, L., Pu, YF. Widely Linear Complex-Valued Least Mean M-Estimate Algorithms: Design and Performance Analysis. Circuits Syst Signal Process 41, 5785–5806 (2022). https://doi.org/10.1007/s00034-022-02053-z

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