Changliang Deng

A fast algorithm for the separation of dependent sources based on bounded component analysis

In order to separate the mixture of bounded signals faster and more accurately, this paper proposes a Bounded Component Analysis (BCA) algorithm based on the detection and removing of steady state learning curve oscillations (DRSSLCO-BCA). In this algorithm, the assumption of statistic independence is replaced by the source boundedness side information with a weaker assumption so that it can separate both independent and dependent sources up to permutation, scaling and phase ambiguities. The algorithm detects the oscillations of the steady state learning curve by the correlation extent at the steady state and uses the iteration of variable step to remove the oscillations of the steady state learning curve. This leads to a faster computation speed and better separation quality. Computer simulations show that the proposed DRSSLCO-BCA algorithm can separate both the dependent and independent sources efficiently with superior performance of computational cost and the separation quality in comparison with the existing BCA algorithms obviously in the noisy or noiseless case.

Fast Extraction for Skewed Source Signals Using Conditional Expectation

The extraction of the stochastic source signals whose probability density functions (PDFs) are skewed is very important in many applications such as biomedical signal processing and mechanical fault diagnosis. This paper shows that the skewed source signal with the maximal absolute value of skewness can be fast extracted by a proposed algorithm using conditional expectation. Compared with the existing conditional expectation-based algorithms, the proposed one possesses two main advantages. One is that it does not require the prior knowledge of the positive support of the desired source, namely the time indices where the source of interest is positive. The other is that it can be employed both in the determined and underdetermined cases. Furthermore, the proposed algorithm is mainly based on the first- and second-order statistics and does not need the preprocessing so that the computational cost is significantly low. Simulation results show the superiority of the proposed algorithm over the existing methods and indicate that the proposed algorithm also performs well in the underdetermined case when the number of sensors is slightly less than that of sources.

A bias removal blind modulation classification algorithm for MIMO channel

With the development of wireless communication, modulation classification (MC) has wide applications both in military and civilian communication, such as anti-interference, adaptive modulation and spectrum management. This paper proposes a bias removal blind MC algorithm (B-MCA) based on the preprocessing of the mixtures, the bias removal equivariant adaptive separation via independence (EASI) algorithm, the bias cancellation of the estimation signals and the high order cumulants (HOC) based classification algorithm. The advantage of the proposed algorithm B-MCA is that, by considering the actual communication scenarios, B-MCA not only can realize the MC of the mixtures in the multiple-input-multiple-out (MIMO) channel, but also has a good recognition rate in the low signal-to-noise ratio (SNR) case. Simulation results show that B-MCA has good performance of convergence rate and classification accuracy.

SSP-Based UBI Algorithms for Uniform Linear Array

The underdetermined blind identification (UBI) for uniform linear array (ULA) with complex-valued mixing matrix can be processed effectively by the algorithms based on single-source-point (SSP) detection. In this paper, we propose two novel SSP-based methods of UBI in the time–frequency (TF) domain for ULA. One method, called UBI based on linear TF transform (UBI-LT), presents a new SSP detection criterion based on short-time Fourier transform, which modifies IME-RSSP (proposed by Li) by exploiting the phase information of mixture. The other method proposes a new SSP detection criterion based on a cross-term suppression quadratic TF distribution called UBI based on modified quadratic TF distribution (UBI-MQD), which can be seen as an improved version of the SSP-based algorithm proposed by Su. After performing these SSP detection criteria, two methods employ the peak detection and a clustering algorithm to estimate the complex-valued mixing matrix. Two methods have their own advantages and can be chosen by robust systems or high-performance systems. Numerical simulation results show that (1) the proposed methods have better performance than the existing methods with the same means of TF analysis (linear TF transform or quadratic TF distribution), and (2) UBI-LT is more robust than UBI-MQD even on the condition that the source number is large and the signal-to-noise (SNR) is low, while UBI-MQD has higher performance than UBI-LT when the source number is small and the SNR is high.

Online Source Recovery Algorithm with Dynamic Source Number for WSDM in the Noisy Case

This paper proposes an online source recovery algorithm in the noisy case with dynamic source number for wireless statistic division multiplexing (WSDM). Firstly, the proposed algorithm estimates the real-time source number by the presented online Gerschgorin disk estimator (OGDE) which employs the instant covariance matrix and the variable adjustment factor. Then, it recovers the original sources online by the variable-step natural gradient algorithm with bias removal. Compared to the existing source recovery algorithms with dynamic source number, the proposed algorithm has better recovery performance and faster convergence speed in the noisy circumstance. Simulation results validate the effectiveness and superiority of the proposed algorithm in the noisy case with both static and dynamic source number.

A fast online separation algorithm for convolutive mixture model in WSDM

Wireless statistic division multiplexing (WSDM) is a multiplexing technology that can transmit multiple signals simultaneously in the same frequency band over wireless channels. Aiming at the convolutive mixture model in WSDM, this paper proposes a fast online separation algorithm. This algorithm firstly transforms noisy convolutive mixture into a noiseless instantaneous linear mixture by employing a Time-Domian Blind Deconvolution Method (TD-BDM). Then, the sources are recovered online by a modified variable step-size Equivariant Adaptive Separation via Independence (EASI) algorithm using the difference value of kurtosis to adjust step-size. Several simulation results show that the proposed separation algorithm has the faster convergence rate and better separation performance than the conventional fixed step-size and the classical variable step-size algorithm. The results prove that the proposed separation algorithm can be better applied to the WSDM system.

An improved cognitive radio system based on WSDM

This paper proposes an improved cognitive radio system based on Wireless Statistic Division Multiplexing (WSDM) which is the modified version of the new-style cognitive radio system proposed by Li. The maximal user number is limited by the antenna number at receiver in Lis system. In this paper, we relax this limitation in multiplexing subsystem and propose a modified minimum description length (MDL) algorithm based on the fourth-order cumulant by adding the virtual noise which can estimate the source number even when the source number is more than antenna number at receiver. After estimating the source number, mixing matrix is obtained by Second-Order Blind Identification of Underdetermined Mixtures (SOBIUM) and then the sources can be recovered by minimum mean-squared error-based (MMSE) beamforming approach. Simulation results show the feasibility of the proposed cognitive radio system and the superiority of the proposed source number estimation algorithm over the existing source number estimation algorithm based on fourth-order cumulant.