Yimin Wei

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.

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.

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.

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.

A reduced complexity interference cancellation technique based on matrix decomposition for FTN signaling

FTN signaling is an alternative transmission technology, in this system, information carrying symbols can be sent faster than traditional Nyquist rate, thus interference (ISI) is inevitable in the receiver. Therefore, people proposed proposed Partial Decision Feedback Equalization (PDFE) to cancel the interference in the receiver. However, its computational complexity is very high because this method bases on QR decomposition. For this reason, this paper puts forward a novel interference cancellation technique to further improve the system performance with a lower complexity. From the simulation results, we can see that the novel method can give better performance than PDFE, and its complexity is very low because of the absence of QR decomposition.

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.