Pengcheng Xu

Statistic division multiplexing for wireless communication systems

In this paper, wireless statistic division multiplexing (WSDM) is proposed for wireless communication systems, which is a multiplexing scheme that transmits multiple signals simultaneously in the same frequency band over wireless channels. Therefore, the spectrum efficiency of WSDM is high compared to that of time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM). WSDM signal is different from TDM, FDM and CDM signal, which is limited in time interval or frequency band or code. The multiple source signals transmitted in WSDM based wireless communication systems are only required to be statistical independent or statistical distinguished. Source signals are recovered at the multiple-antenna receiver by statistical independence or statistical distinction from the received signals. We show theoretically that the information content of all the signal inputs can be recovered by WSDM system. Computer simulation and realistic experimental results validate the performance of our new WSDM system.

Underdetermined Blind Identification for Uniform Linear Array by a New Time---Frequency Method

This paper proposes a novel underdetermined blind identification method with several new single-source points (SSPs) detection criteria for uniform linear array, where the mixing matrix is complex-valued. These new criteria are based on quadratic time–frequency distribution and employed to detect the SSPs so that the complex-valued mixing matrix can be estimated more precisely. To further enhance the estimation accuracy, a modified peak detection method is presented by exploiting the known source number. Finally, the complex-valued mixing matrix can be obtained by performing a clustering algorithm on samples at selected SSPs. One of the outstanding superiorities for the proposed algorithm is that the new criteria are strict enough for the points that are not the SSPs, which ensures the estimation accuracy of the mixing matrix. The other is that the performance of estimation precision is high even in the noisy case. Numerical simulation results verify the superiority of the proposed algorithm over the existing algorithms.

A Novel Method for Complex-Valued Signals in Independent Component Analysis Framework

This paper deals with the separation problem of complex-valued signals in the independent component analysis (ICA) framework, where sources are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast criteria based on kurtosis, a new contrast function is put forward by introducing the reference-based scheme to negentropy, based on which a novel fast fixed-point (FastICA) algorithm is proposed. This method is similar in spirit to the classical negentropy-based FastICA algorithm, but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. Furthermore, compared with the kurtosis-based FastICA algorithms, this method is more robust against unexpected outliers, which is particularly obvious when the sample size is small. The local consistent property of this new negentropic contrast function is analyzed in detail, together with the derivation of this novel algorithm presented. Performance analysis and comparison are investigated through computer simulations and realistic experiments, for which a simple wireless communication system with two transmitting and receiving antennas is constructed.

Efficient Optimization of Reference-Based Negentropy for Noncircular Sources in Complex ICA

Bingham proposed a complex fast independent component analysis (c-FastICA) algorithm to approximate the nengentropy of circular sources using nonlinear functions. Novey proposed extending the work of Bingham using information from a pseudo-covariance matrix for noncircular sources, particularly for sub-Gaussian noncircular signals such as binary phase-shift keying signals. Based on this work, in the present paper we propose a new reference-based contrast function by introducing reference signals into the negentropy, upon which an efficient optimization FastICA algorithm is derived for noncircular sources. This new approach is similar to Novey’s nc-FastICA algorithm, but differs in that it is much more efficient in terms of the computational speed, which is significantly notable with a large number of samples. In this study, the local stability of our reference-based negentropy is analyzed and the derivation of our new algorithm is described in detail. Simulations conducted to demonstrate the performance and effectiveness of our method are also described.

An improved method for block BSS in time domain by overlapping adjacent blocks

This paper proposes to solve the indeterminacy problem of blind source separation (BSS) in the case that long duration mixture signals are separated and processed block by block in time domain. For general BSS problems, there exist inherent permutation and scaling ambiguities between separations and sources. However, when continuously mixed signals are separated independently in each adjacent blocks, the indeterminacies in adjacent blocks differ from each other. When tying the separations in each block together, the whole recovered signals are not correct estimations of sources. To solve this problem, an effective approach has been proposed by overlapping adjacent blocks partially in time domain and using the associativity between separated components of overlapping signals to clean out these indeterminacies of block BSS. Inspired from this approach, an improved method is put forward in this paper. This new method shows better performance than similar and previous ones in terms of computational speed, especially when the number of sources and blocks is large. Computer simulations are performed to verify the performance of this improved method.

An extended and efficient approach for block BSS in time domain

This paper considers eliminating ambiguities of blind source separation (BSS) when continuous mixtures of source signals are split in time and processed block by block. Due to the inherent permutation and scaling ambiguities of BSS, the original sources can not be recovered successfully when the separated components in each block are concatenated together. For this block BSS case, a new approach is proposed by overlapping adjacent mixture block partially and utilizing the correlation between overlapping signals in time domain. On the one hand, this approach is the extension of previous separation and reconstruction method, in which case the length of overlapping signals is generalized from half the length of signal block to any ratio. On the other hand, this new method is much more efficient than the previously original one in terms of computational speed by simplifying the correlation computation of overlapping signals, which is significantly striking when the sample size of overlapping signals and the number of sources and blcoks are large. Simulations are presented to validate the effectiveness and performance of this new method.

Variable Step-Size Method Based on a Reference Separation System for Source Separation

Traditional variable step-size methods are effective to solve the problem of choosing step-size in adaptive blind source separation process. But the initial setting of learning rate is vital, and the convergence speed is still low. This paper proposes a novel variable step-size method based on reference separation system for online blind source separation. The correlation between the estimated source signals and original source signals increases along with iteration. Therefore, we introduce a reference separation system to approximately estimate the correlation in terms of mean square error (MSE), which is utilized to update the step-size. The use of “minibatches” for the computation of MSE can reduce the complexity of the algorithm to some extent. Moreover, simulations demonstrate that the proposed method exhibits superior convergence and better steady-state performance over the fixed step-size method in the noise-free case, while converging faster than classical variable step-size methods in both stationary and nonstationary environments.

Maximization of Nonlinear Autocorrelation for Blind Source Separation of Non-stationary Complex Signals

Blind source separation of complex-valued signals has been a vital issue especially in the field of digital communication signal processing. This paper proposes a novel method based on nonlinear autocorrelation to solve the problem. Relying on the temporal structure with nonlinear autocorrelation of the signals, the method has a potential capability of extracting non-stationary complex sources with Gaussian or non-Gaussian distribution. Most traditional methods would fail in separating this kind of sources. We also analyze the stability conditions of the method in theory. Numerical simulations on artificial complex Gaussian data and orthogonal frequency division multiplexing sources corroborate the validity and efficiency of the proposed method. Moreover, with respect to classical methods, including cumulant-based approach using the non-stationarity of variance and complexity pursuit, our method offers equally good results with lower computational cost and better robustness. Finally, experiments for the separation of real communication signals illustrate that our method has good prospects in real-world applications.

Blind source separation with variable step-size method based on a reference separation system

Variable step-size methods are effective methods to solve the problem of choosing step size in adaptive blind source separation process. This paper proposes a novel variable step-size method based on a reference separation system for blind source separation. In view of the correlation between the estimated source signals and original source signals increases along with iteration, the method introduces a reference separation system to approximately estimate the correlation which is utilized to update the step-size. The performance in terms of cross-talking error of the proposed algorithm is analyzed. Simulation results show that the proposed method exhibits superior convergence and better steady-state performance compared with the fixed step-size method in the noise free case, and converges faster than classical variable step-size methods in both stationary and non-stationary environments.

Blind source separation algorithm based on modified bacterial colony chemotaxis

Most blind source separation (BSS) algorithm use single-point optimization method which always have the disadvantage of slow convergence speed, bad separate precision and easily getting into the local optimization. In view of these disadvantages, recently, Chen proposed a multiple-point optimization algorithm for BSS named DPBCC, which overcome these disadvantages at a certain extent. But DPBCC uses the superior bacterial random perturbation strategy to solve the problem of local convergence, which cannot ensure that after random perturbation it will be an ergodic search of the domain. So the ability of global convergence still has to improve. This paper proposes a modified bacterial colony chemotaxis algorithm (CBCC) for BSS, combined the strategy of chaos search with the strategy of neighborhood random search, reaching to an ergodic search of the entire domain, solving the local convergence better, improving the convergence speed and separate precision further. Take the BSS algorithm based on kurtosis under the instantaneous linear model for example to do computer simulation. The results validate the superiority of CBCC by comparing with the existing ones.