Wing-Kin Ma

University of Minnesota Duluth, Duluth, Minnesota, United States

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Publications (136)324.55 Total impact

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    ABSTRACT: This letter considers multi-input single-output (MISO) downlink multicasting with finite-alphabet inputs when perfect channel state information is known at the transmitter. Two advanced transmit schemes, namely the beamformed (BF) Alamouti scheme and the stochastic beamforming (SBF) scheme, for maximizing the finite-alphabet-constrained multicast rate are studied. We show that the transmit optimization for these two schemes can be formulated as an SNR-based max-min-fair (MMF) problem with Gaussian inputs, which can be handled via the semidefinite relaxation (SDR) technique. Apart from transmit optimization, we analyzed the rate performance of the two schemes. Our analytical results show that for BF Alamouti, the multicast rate degrades with the number of users $M$ at a rate of $sqrt M $, which is better than the traditional transmit beamforming scheme. For SBF, the multicast rate degradation is less sensitive to the increase in the number of users and outperforms BF Alamouti for large $M$. All the results were verified by numerical simulations.
    IEEE Signal Processing Letters 09/2015; 22(10):1614-1618. DOI:10.1109/LSP.2015.2416258 · 1.64 Impact Factor
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    Xiao Fu · N.D. Sidiropoulos · John Tranter · Wing-Kin Ma
    IEEE Transactions on Signal Processing 09/2015; DOI:10.1109/TSP.2015.2464194 · 3.20 Impact Factor
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    ABSTRACT: We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as speech, fluorescence, and some social network data, fit this paradigm. Prior work tackles this problem by iteratively selecting a fixed number of slabs and fitting, a procedure which may not converge. We formulate this problem from a group-sparsity promoting point of view, and propose an alternating optimization framework to handle the corresponding $\ell_p$ ($0<p\leq 1$) minimization-based low-rank tensor factorization problem. The proposed algorithm features a similar per-iteration complexity as the plain trilinear alternating least squares (TALS) algorithm. Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants. In addition, regularization and constraints can be easily incorporated to make use of \emph{a priori} information on the latent loading factors. Simulations and real data experiments on blind speech separation, fluorescence data analysis, and social network mining are used to showcase the effectiveness of the proposed algorithm.
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    ABSTRACT: The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.
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    ABSTRACT: This paper revisits blind source separation of instantaneously mixed quasi-stationary sources (BSS-QSS), motivated by the observation that in certain applications (e.g., speech) there exist time frames during which only one source is active, or locally dominant. Combined with nonnegativity of source powers, this endows the problem with a nice convex geometry that enables elegant and efficient BSS solutions. Local dominance is tantamount to the so-called pure pixel/separability assumption in hyperspectral unmixing/nonnegative matrix factorization, respectively. Building on this link, a very simple algorithm called successive projection algorithm (SPA) is considered for estimating the mixing system in closed form. To complement SPA in the specific BSS-QSS context, an algebraic preprocessing procedure is proposed to suppress short-term source cross-correlation interference. The proposed procedure is simple, effective, and supported by theoretical analysis. Solutions based on volume minimization (VolMin) are also considered. By theoretical analysis, it is shown that VolMin guarantees perfect mixing system identifiability under an assumption more relaxed than (exact) local dominance—which means wider applicability in practice. Exploiting the specific structure of BSS-QSS, a fast VolMin algorithm is proposed for the overdetermined case. Careful simulations using real speech sources showcase the simplicity, efficiency, and accuracy of the proposed algorithms.
    IEEE Transactions on Signal Processing 05/2015; 63(9):1-1. DOI:10.1109/TSP.2015.2404577 · 3.20 Impact Factor
  • Qiang Li · Ye Yang · Wing-Kin Ma · Meilu Lin · Jianhua Ge · Jingran Lin
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    ABSTRACT: This paper is concerned with an optimization problem in a two-hop relay wiretap channel, wherein multiple multi-antenna relays collaboratively amplify and forward (AF) information from a single-antenna source to a single-antenna destination, and at the same time emit artificial noise (AN) to improve physical-layer information security in the presence of multiple multi-antenna eavesdroppers (or Eves). More specifically, the problem is to simultaneously optimize the AF matrices and AN covariances for secrecy rate maximization, with robustness against imperfect channel state information of Eves via a worst-case robust formulation. Such a problem is nonconvex, and we propose a polynomial-time optimization solution based on a two-level optimization approach and semidefinite relaxation (SDR). In particular, while SDR is generally an approximation technique, we prove that SDR is optimal in the specific context here. This desirable result is obtained by careful reformulation and Karush-Kuhn-Tucker optimality analysis, where, rather interestingly, AN is found to be instrumental in providing guarantee of SDR optimality. Simulation results are provided, and the results show that the proposed joint AF-AN solution can attain considerably higher achievable secrecy rates than some existing suboptimal designs.
    IEEE Transactions on Signal Processing 01/2015; 63(1):206-220. DOI:10.1109/TSP.2014.2369001 · 3.20 Impact Factor
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    ABSTRACT: This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in enabling simultaneous identification of the endmember spectral signatures and the number of endmembers. Previous SD-MMV studies mainly focus on convex relaxations. In this study, we explore the alternative of greedy pursuit, which generally provides efficient and simple algorithms. In particular, we design a greedy SD-MMV algorithm using simultaneous orthogonal matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be closely related to some existing pure pixel search algorithms, especially, the successive projection algorithm (SPA). Thus, a link between SD-MMV and pure pixel search is revealed. We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise---including its identification of the (unknown) number of endmembers---under a sufficiently low noise level. The identification performance of the proposed greedy algorithm is demonstrated through both synthetic and real-data experiments.
    IEEE Journal of Selected Topics in Signal Processing 09/2014; DOI:10.1109/JSTSP.2015.2410763 · 3.63 Impact Factor
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    Nicolas Gillis · Wing-Kin Ma
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    ABSTRACT: In this paper, we analyze different preconditionings designed to enhance robustness of pure-pixel search algorithms, which are used for blind hyperspectral unmixing and which are equivalent to near-separable nonnegative matrix factorization algorithms. Our analysis focuses on the successive projection algorithm (SPA), a simple, efficient and provably robust algorithm in the pure-pixel algorithm class. Recently, a provably robust preconditioning was proposed by Gillis and Vavasis (arXiv:1310.2273) which requires the resolution of a semidefinite program (SDP) to find a data points-enclosing minimum volume ellipsoid. Since solving the SDP in high precisions can be time consuming, we generalize the robustness analysis to approximate solutions of the SDP, that is, solutions whose objective function values are some multiplicative factors away from the optimal value. It is shown that a high accuracy solution is not crucial for robustness, which paves the way for faster preconditionings (e.g., based on first-order optimization methods). This first contribution also allows us to provide a robustness analysis for two other preconditionings. The first one is pre-whitening, which can be interpreted as an optimal solution of the same SDP with additional constraints. We analyze robustness of pre-whitening which allows us to characterize situations in which it performs competitively with the SDP-based preconditioning. The second one is based on SPA itself and can be interpreted as an optimal solution of a relaxation of the SDP. It is extremely fast while competing with the SDP-based preconditioning on several synthetic data sets.
    SIAM Journal on Imaging Sciences 06/2014; 8(2). DOI:10.1137/140994915 · 2.87 Impact Factor
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    ABSTRACT: In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are all intimately mixed. In the no pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. Also, our analysis reveals a surprising and counter-intuitive result, namely, that MVES becomes more robust against lack of pure pixels as the number of endmembers increases. The theoretical results are verified by numerical simulations.
    IEEE Transactions on Geoscience and Remote Sensing 06/2014; 53(10). DOI:10.1109/TGRS.2015.2424719 · 3.51 Impact Factor
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    Xiao Fu · Nicholas D. Sidiropoulos · Wing-Kin Ma
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    ABSTRACT: This paper considers the problem of separating the power spectra and mapping the locations of co-channel transmitters using compound measurements from multiple sensors. This kind of situational awareness is important in cognitive radio practice, for spatial spectrum interpolation, transmission opportunity mining, and interference avoidance. Using temporal auto- and cross-correlations of the sensor outputs, it is shown that the power spectra separation task can be cast as a tensor decomposition problem in the Fourier domain. In particular, a joint diagonalization or (symmetric) parallel factor analysis (PARAFAC) model emerges, with one loading matrix containing the sought power spectra - hence being nonnegative, and locally sparse. Exploiting the latter two properties, it is shown that a very simple algebraic algorithm can be used to speed up the factorization. Assuming a path loss model, it is then possible to identify the transmitter locations by focusing on exclusively used (e.g., carrier) frequencies. The proposed approaches offer identifiability guarantees, and simplicity of implementation. Simulations show that the proposed approaches are effective in separating the spectra and localizing the transmitters.
    2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM); 06/2014
  • Jiaxian Pan · Wing-Kin Ma
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    ABSTRACT: This paper considers robust constant envelope (CE) precoding with antenna-subset selection (AS) in a large-scale MISO downlink scenario where only imperfect channel state information at the transmitter (CSIT) is available. CE precoding is a recently proposed transmission scheme that enables the use of cheap but highly power-efficient power amplifiers, while AS is a well-known approach for reducing the number of power amplifiers. The combination of these two techniques can significantly cut down costs in hardware implementations. We formulate a power minimization problem for AS CE precoding where the worst-case symbol error rate is constrained to be less than a given threshold. The formulation utilizes our recent results on signal characterization of CE precoding. The formulated power minimization optimization problem turns out to be a zero-one linear program. We show that this problem is NP-hard in general. Then, we propose an efficient approximation by Lagrangian dual relaxation and greedy knapsack approximation. Simulation results show that the proposed algorithm can achieve near-optimal performance, and the average number of active antennas accounts for only 19-53% of the total transmit antennas.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Qiang Li · Wing-Kin Ma · Anthony Man-Cho So
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    ABSTRACT: Consider a wireless scenario in which a multi-antenna transmitter wants to send a confidential message to a single-antenna information receiver (IR) while transferring wireless energy to a number of multi-antenna energy receivers (ERs). In order to keep the ERs from retrieving the confidential message, an artificial noise (AN)-aided physical-layer secrecy approach is employed at the transmitter. The AN has dual purpose: First, it can interfere with the ERs' information receptions and thus help improve security. Secondly, it provides wireless energy for the ERs to harvest. Assuming imperfect channel state information at the transmitter, we jointly optimize the co-variances of confidential information and AN such that the secrecy rate at the IR is maximized, while each ER receives a prescribed amount of wireless energy. Although this secrecy-rate maximization problem is non-convex, we show that it can be handled by solving a sequence of convex optimization problems. Numerical results are provided to demonstrate the efficacy of the proposed design.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Qiang Li · Anthony Man-Cho So · Wing-Kin Ma
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    ABSTRACT: This paper considers robust transmit beamforming for multiuser multi-input single-output (MISO) downlink transmission, where imperfect channel state information (CSI) is assumed at the base station (BS). The imperfect CSI is captured by a moment-based random error model, in which the BS knows only the mean and covariance of each CSI error, but not the exact distribution. Under this error model, we formulate a distributionally robust beamforming (DRB) problem, in which the total transmit power at the BS is to be minimized, while each user's SINR outage probability, evaluated w.r.t. any distribution with the given mean and covariance, is kept below a given threshold. The DRB problem is a semi-infinite chance-constrained problem. By employing recent results in distributionally robust optimization, we show that the DRB problem admits an explicit conic reformulation, which can be conveniently turned into a convex optimization problem after semidefinite relaxation (SDR). We also consider the case where the mean and covariance are not perfectly known. We show that the resulting DRB problem still admits a conic reformulation and can be approximately solved using SDR. The robustness of the proposed designs are demonstrated by numerical simulations.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: Unraveling power spectra mixtures and finding the directions of the constituent sources can enable effective spatial occupancy prediction by location-dependent combining of the recovered source power spectra; and it is also useful for primary interference avoidance. Such unmixing and direction-finding is a challenging leap beyond ordinary 'aggregate' spectrum sensing. This paper presents a promising new method for blind (power) spectra separation and emitter direction finding using a network of cognitive radios. Each radio has a pair of antennas, and the baselines of different radios are aligned (e.g., using a compass), in a configuration reminiscent of classical spatial correlation-based ESPRIT. Unlike classical ESPRIT, array geometry is exploited here in the temporal correlation domain to come up with a simple and effective blind spectra separation and direction finding solution with guaranteed identifiability and robustness to noise. A notable feature is that the different radios need not be synchronized, as they do in spatial ESPRIT.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: Recently, simultaneous wireless information and power transfer (SWIPT) has received considerable attention. In this paper, we consider a multicast SWIPT system, where a multi-antenna transmitter broadcasts common information to a group of single-antenna information receivers (IRs) and at the same time provides certain amount of energy transfer to a group of single-antenna energy receivers (ERs). Assuming imperfect channel state information (CSI) at the transmitter, two transmit schemes are proposed to maximize the IRs' outage-constrained multicast rate subject to a minimum provision of average energy transfer to ERs. In the first transmit scheme, we consider transmit beamforming and develop a safe approximation approach to obtain a conservative beamforming solution for maximizing the outage-constrained multicast rate. To further improve the performance of transmit beamforming, in the second transmit scheme, we consider a stochastic beamforming (SBF) approach, which allows the beamformer to randomly change over time according to some prescribed distribution. By doing so, the SBF scheme is able to fully exploit the temporal degree of freedom to achieve more balanced outage-constrained achievable rates among IRs. Simulation results demonstrated that the SBF scheme is generally better than the transmit beamforming scheme.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: In recent years, it has become clear that hyperspectral imaging has formed a core area within the geoscience and remote sensing community. Armed with advanced optical sensing technology, hyperspectral imaging offers high spectral resolution-a hyperspectral image can contain more than 200 spectral channels (rather than a few channels as in multispectral images), covering visible and near-infrared wavelengths at a resolution of about 10 nm. The result, on one hand, is significant expansion in data sizes. A captured scene can easily take 100 MB, or more. On the other hand, the vastly increased spectral information content available in hyperspectral images (or large spectral degrees of freedom in signal processing languages) creates a unique opportunity that may have previously been seen as impossible in multispectral remote sensing. We can detect difficult targets, for example, those appearing at a subpixel level. We can perform image classification with greatly improved accuracy. We can also identify underlying materials in a captured scene without prior information of the materials to be encountered, by carrying out blind unmixing.
    IEEE Signal Processing Magazine 01/2014; 31(1):22-23. DOI:10.1109/MSP.2013.2282417 · 4.48 Impact Factor
  • Jiaxian Pan · Wing-Kin Ma · Joakim Jalden
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    ABSTRACT: This paper considers lattice decoding for multi-input multi-output (MIMO) detection under PAM constellations. A key aspect of lattice decoding is that it relaxes the symbol bound constraints in the optimal maximum-likelihood (ML) detector for faster implementations. It is known that such a symbol bound relaxation may lead to a damaging effect on the system performance. For this reason, regularization was proposed to mitigate the out-of-bound symbol effects in lattice decoding. However, minimum mean square error (MMSE) regularization is the only method of choice for regularization in the present literature. We propose a systematic regularization optimization approach considering a Lagrangian dual relaxation (LDR) of the ML detection problem. As it turns out, the proposed LDR formulation is to find the best diagonally regularized lattice decoder to approximate the ML detector, and all diagonal regularizations, including the MMSE regularization, can be subsumed under the LDR formalism. We show that for the 2-PAM case, strong duality holds between the LDR and ML problems. Also, for general PAM, we prove that the LDR problem yields a duality gap no worse than that of the well-known semidefinite relaxation method. To physically realize the proposed LDR, the projected subgradient method is employed to handle the LDR problem so that the best regularization can be found. The resultant method can physically be viewed as an adaptive symbol bound control wherein regularized lattice decoding is recursively performed to correct the decision. Simulation results show that the proposed LDR approach can outperform the conventional MMSE-based lattice decoding approach.
    IEEE Transactions on Signal Processing 12/2013; 62(2). DOI:10.1109/TSP.2013.2292040 · 3.20 Impact Factor
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    ABSTRACT: This paper studies a multiple-measurement vector (MMV)-based sparse regression approach to blind hyperspectral un-mixing. In general, sparse regression requires a dictionary. The considered approach uses the measured hyperspectral data as the dictionary, thereby intending to represent the whole measured data using the fewest number of measured hyperspectral vectors. We tackle this self-dictionary MMV (SD-MMV) approach using greedy pursuit. It is shown that the resulting greedy algorithms are identical or very similar to some representative pure pixels identification algorithms, such as vertex component analysis. Hence, our study pro-vides a new dimension on understanding and interpreting pure pixels identification methods. We also prove that in the noiseless case, the greedy SD-MMV algorithms guaran-tee perfect identification of pure pixels when the pure pixel assumption holds.
    21st European Signal Processing Conference; 09/2013

Publication Stats

3k Citations
324.55 Total Impact Points

Institutions

  • 2015
    • University of Minnesota Duluth
      • Department of Electrical Engineering
      Duluth, Minnesota, United States
  • 1999–2015
    • The Chinese University of Hong Kong
      • Department of Electronic Engineering
      Hong Kong, Hong Kong
  • 2006–2012
    • National Tsing Hua University
      • • Department of Electronic Engineering
      • • Department of Electrical Engineering
      Hsin-chu-hsien, Taiwan, Taiwan
  • 2004–2005
    • University of Melbourne
      • Department of Electrical and Electronic Engineering
      Melbourne, Victoria, Australia
    • The University of Hong Kong
      • Department of Electrical and Electronic Engineering
      Hong Kong, Hong Kong