Wing-Kin Ma

The Chinese University of Hong Kong, Hong Kong, Hong Kong

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Publications (119)279.91 Total impact

  • [Show abstract] [Hide abstract]
    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.
    09/2014;
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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.
    06/2014;
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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
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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
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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. · 3.37 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). · 2.81 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
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    ABSTRACT: Hyperspectral endmember extraction is to estimate endmember signatures (or material spectra) from the hyperspectral data of an area for analyzing the materials and their composition therein. The presence of noise and outliers in the data poses a serious problem in endmember extraction. In this paper, we handle the noise- and outlier-contaminated data by a two-step approach. We first propose a robust-affine-set-fitting algorithm for joint dimension reduction and outlier removal. The idea is to find a contamination-free data-representative affine set from the corrupted data, while keeping the effects of outliers minimum, in the least squares error sense. Then, we devise two computationally efficient algorithms for extracting endmembers from the outlier-removed data. The two algorithms are established from a simplex volume max-min formulation which is recently proposed to cope with noisy scenarios. A robust algorithm, called worst case alternating volume maximization (WAVMAX), has been previously developed for the simplex volume max-min formulation but is computationally expensive to use. The two new algorithms employ a different kind of decoupled max-min partial optimizations, wherein the design emphasis is on low-complexity implementations. Some computer simulations and real data experiments demonstrate the efficacy, the computational efficiency, and the applicability of the proposed algorithms, in comparison with the WAVMAX algorithm and some benchmark endmember extraction algorithms.
    IEEE Transactions on Geoscience and Remote Sensing 07/2013; 51(7):3982-3997. · 3.47 Impact Factor
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    ABSTRACT: Consider transceiver designs in a multiuser multi-input single-output (MISO) downlink channel, where the users are to receive the same data stream simultaneously. This problem, known as physical-layer multicasting, has drawn much interest. Presently, a popularized approach is transmit beamforming, in which the beamforming optimization is handled by a rank-one approximation method called semidefinite relaxation (SDR). SDR-based beamforming has been shown to be promising for a small or moderate number of users. This paper describes two new transceiver strategies for physical-layer multicasting. The first strategy, called stochastic beamforming (SBF), randomizes the beamformer in a per-symbol time-varying manner, so that the rank-one approximation in SDR can be bypassed. We propose several efficiently realizable SBF schemes, and prove that their multicast achievable rate gaps with respect to the MISO multicast capacity must be no worse than 0.8314 bits/s/Hz, irrespective of any other factors such as the number of users. The use of channel coding and the assumption of sufficiently long code lengths play a crucial role in achieving the above result. The second strategy combines transmit beamforming and the Alamouti space-time code. The result is a rank-two generalization of SDR-based beamforming. We show by analysis that this SDR-based beamformed Alamouti scheme has a better worst-case effective signal-to-noise ratio (SNR) scaling, and hence a better multicast rate scaling, than SDR-based beamforming. We further the work by combining SBF and the beamformed Alamouti scheme, wherein an improved constant rate gap of 0.39 bits/s/Hz is proven. Simulation results show that under a channel-coded, many-user setting, the proposed multicast transceiver schemes yield significant SNR gains over SDR-based beamforming at the same bit error rate level.
    IEEE Transactions on Signal Processing 05/2013; · 2.81 Impact Factor
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    ABSTRACT: This is a companion technical report of the main manuscript "Physical-Layer Multicasting by Stochastic Transmit Beamforming and Alamouti Space-Time Coding". The report serves to give detailed derivations of the achievable rate functions encountered in the main manuscript, which are too long to be included in the latter. In addition, more simulation results are presented to verify the viability of the multicast schemes developed in the main manuscript.
    05/2013;
  • Qiang Li, Wing-Kin Ma
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    ABSTRACT: Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy, such that the achievable secrecy rate is maximized subject to the sum power constraint. AN-aided secure transmission has recently been found to be a promising approach for blocking eavesdropping attempts. In many existing studies, the confidential information transmit covariance and the AN covariance are not simultaneously optimized. In particular, for design convenience, it is common to prefix the AN covariance as a specific kind of spatially isotropic covariance. This paper considers joint optimization of the transmit and AN covariances for secrecy rate maximization (SRM), with a design flexibility that the AN can take any spatial pattern. Hence, the proposed design has potential in jamming the eavesdroppers more effectively, based upon the channel state information (CSI). We derive an optimization approach to the SRM problem through both analysis and convex conic optimization machinery. We show that the SRM problem can be recast as a single-variable optimization problem, and that resultant problem can be efficiently handled by solving a sequence of semidefinite programs. Our framework deals with a general setup of multiple multi-antenna eavesdroppers, and can cater for additional constraints arising from specific application scenarios, such as interference temperature constraints in interference networks. We also generalize the framework to an imperfect CSI case where a worst-case robust SRM formulation is considered. A suboptimal but safe solution to the outage-constrained robust SRM design is also investigated. Simulation results show that the proposed AN-aided SRM design yields significant secrecy rate gains over an optimal no-AN design and the isotropic AN design, especially when there are more eavesdroppers.
    IEEE Transactions on Signal Processing 03/2013; 61(10). · 2.81 Impact Factor
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    ABSTRACT: This paper considers transmit covariance optimization for a multi-input multi-output (MIMO) Gaussian wiretap channel. Specifically, we aim to maximize the MIMO secrecy capacity by judiciously designing the transmit covariance under the sum power and per-antenna power constraints. The MIMO secrecy capacity maximization (SCM) problem is nonconvex, and so far there is no tractable solution available. We propose an alternating optimization (AO) approach to handle the SCM problem. In particular, our development consists of two steps: First, we show that the SCM problem can be reexpressed to a form that can be conveniently processed by AO. Second, we develop a custom-designed fast algorithm for each AO iteration. Interestingly, with this fast implementation, the overall AO algorithm can be viewed as performing iterative reweighting and water-filling. Finally, the convergence of the proposed algorithm to a stationary solution of SCM is shown, and numerical results are provided to demonstrate its efficacy.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
  • Senshan Ji, S.X. Wu, A.M.-C. So, Wing-Kin Ma
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    ABSTRACT: In this paper, we consider transmit design in multiple-input single-output (MISO) multi-group multicast (MM) cognitive radio (CR) systems. Previously, semidefinite relaxation (SDR)-based transmit beamforming has been very successful in transmit design. However, recent research shows that further performance gain is possible by suitably modifying the transmit structure. Here, we propose a transmit beamformed Alamouti space-time code scheme for MM-CR systems, whose corresponding transmit design problem can be reformulated as a rank-2 constrained fractional semidefinite program. We then develop an SDR framework for this scheme and study its signal-to-interference-and-noise ratio (SINR) performance via both theoretical analysis and simulations. Specifically, we show that the worst-case approximation accuracy of the proposed scheme scales on the order of √MS log MP, where MP (resp. MS) is the number of primary (resp. secondary) users in the CR network. This unifies and generalizes a number of results in the literature and is, to the best of our knowledge, the first provable bound on the performance of a beamforming scheme in a general MM-CR system. Finally, simulation results show that our proposed scheme indeed has a better performance in both MM and MM-CR scenarios than the traditional beamforming scheme.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: Promoting the spatial resolution of off-the-shelf hyperspectral sensors is expected to improve typical computer vision tasks, such as target tracking and image classification. In this paper, we investigate the scenario in which two cameras, one with a conventional RGB sensor and the other with a hyperspectral sensor, capture the same scene, attempting to extract redundant and complementary information. We propose a non-negative sparse promoting framework to integrate the hyperspectral and RGB data into a high resolution hyperspectral set of data. The formulated problem is in the form of a sparse non-negative matrix factorization with prior knowledge on the spectral and spatial transform responses, and it can be handled by alternating optimization where each subproblem is solved by efficient convex optimization solvers; e.g., the alternating direction method of multipliers. Experiments on a public database show that our method achieves much lower average reconstruction errors than other state-of-the-art methods.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
  • Xiao Fu, Wing-Kin Ma
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    ABSTRACT: This paper presents an efficient method for blind source separation of convolutively mixed speech signals. The method follows the popular frequency-domain approach, wherein researchers are faced with two main problems, namely, per-frequency mixing system estimation, and permutation alignment of source components at all frequencies. We adopt a novel concept, where we utilize local sparsity of speech sources in transformed domain, together with non-stationarity, to address the two problems. Such exploitation leads to a closed-form solution for per-frequency mixing system estimation and a numerically simple method for permutation alignment, both of which are efficient to implement. Simulations show that the proposed method yields comparable source recovery performance to that of a state-of-the-art method, while requires much less computation time.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: The main goal of this special issue is to gather state-of-the art-contributions that address such challenges as they pertain to the design, analysis, and optimization of physical layer security in next-generation networks.
    IEEE Journal on Selected Areas in Communications 01/2013; 31(9):1657-1659. · 3.12 Impact Factor
  • Jiaxian Pan, Wing-Kin Ma
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    ABSTRACT: This paper considers constant envelope (CE) precoding in single-user MISO downlink systems. CE precoding is a transmission scheme recently proposed for very large antenna arrays, in which the use of highly power-efficient RF amplifiers is a requirement. There are two important issues in CE precoding, namely the characterization of the region of all possible noise-free receive signals, and the recovery of the phases of the transmitting signal. An existing result by Mohammed and Larsson showed that the noise-free receive signal region can be geometrically interpreted as a region between two circles centered at the origin of the complex plane. However, this result did not prove the expression of the radius of the inner circle. We provide a new analysis approach to characterize the noise-free receive signal region. Our result shows that the radius of the inner circle has a simple closed-form expression, there by completing the result by Mohammed and Larsson. In addition, we propose an algorithm that can recover the phases of the transmitting signal exactly with a complexity linear in the number of antennas. Simulation results show that the proposed method can be significantly faster than an existing phase recovery algorithm.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
  • Hoi-To Wai, Qiang Li, Wing-Kin Ma
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    ABSTRACT: This paper considers a discrete sum rate maximization (DSRM) problem for transmit optimization in multiuser MISO downlink. Unlike many existing sum rate maximization designs, DSRM focuses on a scenario where each user's achievable rate can only be chosen from a given discrete rate set. This discrete rate-based design is motivated by the fact that practical communication systems can support only a finite number of combinations of modulation and coding schemes. We tackle the DSRM problem first by deriving a novel reformulation of DSRM, in which the discrete rate variables are absorbed by the objective function. Then, from this reformulation, an approximation algorithm based on convex optimization and iterative solution refinement is developed. Simulations results are provided to demonstrate the performance of the proposed algorithm compared with some state-of-the-art algorithms.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: This paper considers transmit optimization in multi-input multi-output (MIMO) wiretap channels, wherein we aim at maximizing the secrecy capacity or rate of an MIMO channel overheard by one or multiple eavesdroppers. Such optimization problems are nonconvex, and appear to be difficult especially in the multi-eavesdropper scenario. In this paper, we propose an alternating optimization (AO) approach to tackle these secrecy optimization problems. We first consider the secrecy capacity maximization (SCM) problem in the single eavesdropper scenario. An AO algorithm is derived through a judicious SCM reformulation. The algorithm conducts some kind of reweighting and water-filling in an alternating fashion, and thus is computationally efficient to implement. We also prove that the AO algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point of the SCM problem. Then, we turn our attention to the multiple eavesdropper scenario, where the artificial noise (AN)-aided secrecy rate maximization (SRM) problem is considered. Although the AN-aided SRM problem has a more complex problem structure than the previous SCM, we show that AO can be extended to deal with the former, wherein the problem is handled by solving convex problems in an alternating fashion. Again, the resulting AO method is proven to have KKT point convergence guarantee. For fast implementation, a custom-designed AO algorithm based on smoothing and projected gradient is also derived. The secrecy rate performance and computational efficiency of the proposed algorithms are demonstrated by simulations.
    IEEE Journal on Selected Areas in Communications 01/2013; 31(9):1714-1727. · 3.12 Impact Factor
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    ABSTRACT: This letter studies cooperative secure beamforming for amplify-and-forward (AF) relay networks in the presence of multiple eavesdroppers. Under both total and individual relay power constraints, we propose two schemes, namely secrecy rate maximization (SRM) beamforming and null-space beamforming. In the first scheme, our design problem is based on SRM. Using a suboptimal, but convex, technique-semidefinite relaxation (SDR), we show that this problem can be handled by performing a one-dimensional search which involves solving a sequence of semidefinite programs (SDPs). To reduce the complexity, in the second scheme, we instead maximize the information rate at the destination while completely eliminating the information leakage to all eavesdroppers. We prove that this problem can be exactly solved by SDR with one SDP only. Simulation results demonstrate the performance gains of the two proposed designs.
    IEEE Signal Processing Letters 01/2013; 20(1):35-38. · 1.67 Impact Factor

Publication Stats

2k Citations
279.91 Total Impact Points

Institutions

  • 1999–2013
    • The Chinese University of Hong Kong
      • • Department of Electronic Engineering
      • • Department of Systems Engineering and Engineering Management
      Hong Kong, Hong Kong
  • 2006–2012
    • National Tsing Hua University
      • Department of Electrical Engineering
      Hsin-chu-hsien, Taiwan, Taiwan
  • 2010
    • Peking University
      Peping, Beijing, China
  • 2009
    • University of New South Wales
      • School of Electrical Engineering and Telecommunications
      Kensington, New South Wales, Australia
    • Tsinghua University
      • Institute of Systems Engineering
      Peping, Beijing, China
  • 2008
    • National Hsinchu University of Education
      Hsin-chu-hsien, Taiwan, Taiwan
    • Rutgers, The State University of New Jersey
      • Department of Electrical and Computer Engineering
      New Brunswick, NJ, United States
  • 2004–2006
    • 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