Maximum Likelihood Independent Component Analysis Research Articles

Multi-Dimensional, Wide-Range, and Modulation-Format-Transparent Transceiver Imbalance Monitoring

We propose and experimentally demonstrate a dual-polarization (DP) transceiver imbalance monitoring scheme, which is applicable for the coherent system using square QAM signals. The scheme can separate/monitor various kinds of transceiver imbalances and has technical advantages of multi-dimension, wide monitoring range, and modulation-format-transparency. In the proposed scheme, a polarization scrambler is configured between transmitter (Tx) and receiver (Rx). After coherent detection, the proposed digital signal processing (DSP) modules are implemented to monitor transceiver imbalance based on the received signals. To simplify the DSP modules, the implementation sequence of DSP modules is in reverse order compared to the order in which the sequence of impairments is introduced. Firstly, Godard timing error detection (TED) and Gram-Schmidt orthogonalization procedure (GSOP) is used to monitor and compensate Rx imbalances. After compensating the Rx imbalances, Tx imbalance monitoring is implemented. Complex/real maximum likelihood independent component analysis (ML-ICA) and Godard-TED are used to monitor Tx imbalance. Finally, the amplitude ratios of the final output signal and Tx in-phase/quadrature (IQ) amplitude imbalance are fed back to mitigate the interaction of Tx imbalances on the signal power and improve monitoring accuracy. The transceiver IQ and x-polarization/y-polarization (XY) imbalances are separated for the first time by using the frequency offset (FO) naturally existing in the system and the continuous polarization rotation induced by a polarization scrambler. The separation method combined with the reverse algorithm design and amplitude ratio feedback mechanism makes the proposed scheme achieve the most multi-dimensional imbalance monitoring by far. The simulation and experimental results verify that the proposed scheme is modulation-format-transparent and can separate/monitor multi-dimensional transceiver imbalances within a wide range.

Modulation-format-transparent IQ imbalance estimation of dual-polarization optical transmitter based on maximum likelihood independent component analysis.

We propose and experimentally demonstrate a modulation-format-transparent dual-polarization (DP) transmitter (Tx) in-phase/quadrature (IQ) imbalance estimation scheme based on maximum likelihood independent component analysis (ML-ICA). The proposed scheme can separate Tx IQ imbalance from polarization crosstalk and phase noise and achieve accurate IQ imbalance estimation without training data and the information of modulation format. Firstly, the complex-ML-ICA is used to implement format-transparent polarization de-multiplexing to remove polarization crosstalk; then the real-ML-ICA is employed to estimate inverse IQ mixing matrix and compensate Tx IQ imbalance/phase noise on each polarization channel. Inverse IQ mixing matrix contains the information of phase noise and Tx IQ imbalance; Finally, Tx IQ imbalance is derived from the inverse matrix by analytic method. The impact of Tx IQ imbalance on polarization demultiplexing and carrier phase recovery (CPE) is investigated by numerical simulation from three aspects of Jones space, Stokes space, and Kurtosis. The simulation results demonstrate the proposed scheme has strong robustness to phase noise, quantization noise, and amplified spontaneous emission (ASE) noise. The proposed ML-ICA algorithm is verified experimentally in polarization division multiplexing (PDM) quadrature phase-shift keying (QPSK)/8 quadrature amplitude modulation (QAM)/16QAM/64QAM systems. The experimental results show the scheme can accurately estimate Tx IQ imbalance within wide range in a format transparent manner.

Languages across the world are efficiently coded by the auditory system

Independent Component Analysis (ICA) is a powerful method for uncovering statistical structure in natural stimuli. Lewicki (2002 Nature Neuroscience) used ICA to examine statistical properties of human speech. Filters that optimally encoded speech were an excellent match for frequency tuning in the cat auditory nerve, leading to suggestions that speech makes efficient use of coding properties in the mammalian auditory system. However, Lewicki only examined American English, which is neither normative nor representative of the world's languages. Here, fifteen languages were examined (Dutch, Flemish, Greek, Javanese, Ju|'hoan, Mandarin Chinese, Norwegian, Swedish, Tagalog, Tahitian, Urhobo, Vietnamese, Wari', Xhosa, Yeyi). Each recording contained speech tokens from native speakers without any background noise for at least seven minutes. Maximum likelihood ICA was used to create statistically optimal filters for encoding sounds from each language. These filters were then compared to the same physiological measures analyzed in Lewicki (2002). Languages produced a range of ICA solutions, as expected, but were highly consistent with both statistically optimal filters for American English and physiological measures. Results significantly extend Lewicki (2002) by revealing agreement between response properties of the auditory system and speech sounds in a wide range of languages.

ICA BASED DIGITAL IMAGE WATERMARKING BASED ON REDUNDANT DISCRETE WAVELET TRANSFORM

In recent years, access to multimedia data has become much easier due to rapid growth of the internet. While this is usually considered an improvement of everyday life, it also makes unauthorized copying and distributing of multimedia data much easier, therefore presenting a field of watermarking. Many literatures have reported about Discrete Wavelet Transform watermarking techniques for data security. However, DWT based watermarking schemes are found to be less robust against image processing attacks and the shift variance of Wavelet Packet Transform causes inaccurate extraction. In this paper, an attempt is made to develop a watermarking scheme based on RDWT and extraction using Independent Component Analysis (ICA). Various ICA like Fast ICA, Radical ICA, Pearson ICA, Jade ICA, Maximum Likelihood ICA, Mean Field ICA and Kernel ICA are proposed and implemented for extraction. Simulation results reveal that Fast ICA produces better Similarity Measure and Mutual Information over other ICA methods.

MLICA-Based Separation Algorithm for Complex Sinusoidal Signals with PDF Parameter Optimization

Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations. key words: radar signal processing, maximum likelihood independent component analysis (MLICA), complex sinusoidal signals, PDF parameter optimization

Algorithms for Complex ML ICA and Their Stability Analysis Using Wirtinger Calculus

We derive a class of algorithms for independent component analysis (ICA) based on maximum likelihood (ML) estimation and perform stability analysis of natural gradient ML ICA with and without the constraint for unitary demixing matrix. In the process, we demonstrate how Wirtinger calculus facilitates derivations, and most importantly, performing second-order analysis in the complex domain and eliminates the need for making simplifying assumptions. We derive natural gradient complex ML ICA update rule and its variant with a unitary constraint, as well as a Newton algorithm for better convergence behavior. The conditions for local stability are derived and studied using a generalized Gaussian density (GGD) source model. When the sources are circular and non-Gaussian, we show analytically that both the ML and ML-unitary ICA update rules converge to the inverse of mixing matrix subject to a phase shift. When the sources are noncircular and non-Gaussian, we show that the nonunitary ML ICA update rule is more stable than the ML-unitary ICA update rule. When the sources are noncircular Gaussians, both update rules are stable only when the sources have distinct noncircularity indices. Simulation results are given to support these results.

Adaptive Score Functions for Maximum Likelihood ICA

We propose Blind Source Separation (BSS) techniques that are applicable to a wide class of source distributions that may be skewed or symmetric and may even have zero kurtosis. Skewed distributions are encountered in many important application areas such as communications and biomedical signal processing. The methods stem from maximum likelihood approach. Powerful parametric models based on the Extended Generalized Lambda Distribution (EGLD) and the Pearson system are employed in finding the score function. Model parameters are adaptively estimated using conventional moments or linear combinations of order statistics (L-moments). The developed methods are compared with the existing methods quantitatively. Simulation examples demonstrate the good performance of the proposed methods in the cases where the standard Independent Component Analysis (ICA) methods perform poorly.