IEEE Journal of Selected Topics in Signal Processing (IEEE J-STSP )

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers


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    IEEE journal of selected topics in signal processing, Selected topics in signal processing
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    Internet Resource, Computer File, Journal / Magazine / Newspaper

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Institute of Electrical and Electronics Engineers

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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The recent increased interest in large-scale multiple- input multiple-output systems, combined with the cost of analog radio-frequency (RF) chains, necessitates the use of efficient antenna selection (AS) schemes. Capacity or signal-to-noise ratio (SNR) optimal AS has been considered to require an exhaustive search among all possible antenna subsets. In this work, we prove that, under a total power constraint on the beamformer, the maximum-SNR joint beamforming transmit AS problem with two receive antennas and an arbitrary number of transmit antennas $N$ is polynomially solvable and develop an algorithm that solves it with quartic complexity, independently of the number of selected antennas. The algorithm identifies with complexity ${cal O}(N^{4})$ a cubic-size collection of antenna subsets that contains the one that maximizes the post-processing receiver SNR. From a different perspective, for any given two-row complex matrix, our algorithm computes with quartic complexity its two-row submatrix with the maximum principal singular value, for any number of selected columns. In addition, our method also applies to receive AS with two transmit antennas. Finally, if we enforce a per-antenna-element power constraint on the beamformer (i.e., constant-envelope transmission), then the set of transmit AS subsets that contains the optimal one is the same as in the total power constraint case. Therefore, our algorithm offers a practical solution to the maximum-SNR antenna selection problem when either the transmitter or the receiver consists of a large number of antennas.
    IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):891-901.
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    ABSTRACT: Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.
    IEEE Journal of Selected Topics in Signal Processing 10/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Constant envelope (CE) precoding is a very recently developed transmission approach for large antenna array systems, where each antenna is restricted to transmit CE signals and only phases are used to shape desired information signals at the receiver. CE precoding is proposed as a solution for circumventing the high peak-to-average power ratio (PAPR) problem arising in non-CE transmission approaches, which becomes a difficult hardware implementation issue in large antenna array systems. While CE precoding is a nonlinear precoding approach and introduces challenges not seen in the widely-used non-CE linear precoding approach, the former has been shown to hold great potential in large-scale single-user MISO channels from an information rate analysis viewpoint. The present paper considers single-user MISO CE precoding from a transceiver realization viewpoint. We first solve the noise-free receive signal region characterization problem. From the characterization proof, we derive a simple and efficient CE precoder algorithm whose complexity is linear in the number of antennas. Then, we consider optimal CE precoding designs when the system can perform either antenna-subset selection or unequal per-antenna power allocation—both aiming at maximizing the system performance under transmission power constraints. Polynomial-time exact algorithms for the proposed design, via simple search or convex optimization, are developed. Simulation results demonstrate that for large antenna array systems, the proposed CE precoding schemes can yield symbol error probability performance comparable to that of the non-CE maximum ratio transmission scheme.
    IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):982-995.
  • IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):739-741.
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    ABSTRACT: Signal feature extraction and classification are two common tasks in the signal processing literature. This paper investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals. We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. Many classification tasks can be defined on such outputs. For example, one may want to identify the dialect of a microblog contributed by an author, or classify information referred to in a user's tweet as true or false. While the design of such classifiers is application-specific, social signals share in common one key property: they are augmented by the explicit identity of the source. This motivates investigating whether or not knowing the source of each signal (in addition to exploiting signal features) allows the classification accuracy to be improved. We call it provenance-assisted classification. This paper answers the above question affirmatively, demonstrating how source identities can improve classification accuracy, and derives confidence bounds to quantify the accuracy of results. Evaluation is performed in two real-world contexts: (i) fact-finding that classifies microblog entries into true and false, and (ii) language classification of tweets issued by a set of possibly multi-lingual speakers. We also carry out extensive simulation experiments to further evaluate the performance of the proposed classification scheme over different problem dimensions. The results show that provenance features significantly improve classification accuracy of social signals, even when no information is known about the sources (besides their ID). This observation offers a general mechanism for enhancing classification results in social networks.
    IEEE Journal of Selected Topics in Signal Processing 08/2014; 8(4):624-637.
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    ABSTRACT: Peer influence and interactions between agents in a population give rise to complex, nonlinear behaviors. This paper adopts the SIS (susceptible-infected-susceptible) framework from epidemiology to analytically study how network topology affects the diffusion of ideas/opinions/beliefs/innovations in social networks. We introduce the scaled SIS process, which models peer influence as neighbor-to-neighbor infections. We model the scaled SIS process as a continuous-time Markov process and derive for this process its closed form equilibrium distribution. The adjacency matrix that describes the underlying social network is explicitly reflected in this distribution. The paper shows that interesting population asymptotic behaviors occur for scenarios where the individual tendencies of each agent oppose peer influences. Specifically, we determine how the most probable configuration of agent states (i.e., the population configuration with maximum equilibrium distribution) depends on both model parameters and network topology. We show that, for certain regions of the parameter space, this and related issues reduce to standard graph questions like the maximum independent set problem.
    IEEE Journal of Selected Topics in Signal Processing 08/2014; 8(4):537-551.
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    ABSTRACT: In contrast to static imagery, detection of events of interest in video involves evidence accumulation across space and time; the observer is required to integrate features from both motion and form to decide whether a behavior constituents a target event. Do such events that extend in time elicit evoked responses of similar strength as evoked responses associated with instantaneous events such as the presentation of a static target image? Using a set of simulated scenarios, with avatars/actors having different behaviors, we identified evoked neural activity discriminative of target vs. distractor events (behaviors) at discrimination levels that are comparable to static imagery. EEG discriminative activity was largely in the time-locked evoked response and not in oscillatory activity, with the exception of very low EEG frequency bands such as delta and theta, which simply represent bands dominating the event related potential (ERP). The discriminative evoked response activity we see is observed in all target/distractor conditions and is robust across different recordings from the same subjects. The results suggest that we have identified a robust neural correlate of target detection in video, at least in terms of the stimulus set we used—i.e., dynamic behavior of an individual in a low clutter environment. Additional work is needed to test a larger variety of behaviors and more diverse environments.
    IEEE Journal of Selected Topics in Signal Processing 06/2014; 8(3):358-365.
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    ABSTRACT: In this paper, we propose a region-of-interest (ROI) based HEVC coding approach for conversational videos, with a novel hierarchical perception model of face (HP model), to improve the perceived visual quality of state-of-the-art HEVC standard. In contrast to the previous ROI-based video coding approaches, this novel HP model allows the unequal importance of facial features (e.g., the eyes and mouth) within the facial region, by generating a pixel-wise weight map. Benefitting from such a perception model, the adaptive coding tree unit (CTU) partition structure is developed to alleviate the encoding complexity of HEVC, without any degradation of the visual quality in facial regions, especially in the regions of facial features. Subsequently, for the rate control in HEVC a weight-based unified rate-quantization (URQ) scheme, instead of the conventional pixel-based URQ scheme, is proposed to adaptively adjust the value of quantization parameter (QP). Such an adaptive adjustment of QPs is capable of allocating more bits to the face/facial features with respect to our HP model, and as a result, the visual quality of face, in particular facial features, can be enhanced for conversational HEVC coding. Finally, the experimental results show that the perceived visual quality of our approach is greatly improved, with even less encoding time, for conversational video coding on the HEVC platform.
    IEEE Journal of Selected Topics in Signal Processing 06/2014; 8(3):475-489.
  • [Show abstract] [Hide abstract]
    ABSTRACT: To achieve clear binocular vision, neural processes that accomplish accommodation and vergence are performed via two collaborative, cross-coupled processes: accommodation-vergence (AV) and vergence-accommodation (VA). However, when people watch stereo images on stereoscopic displays, normal neural functioning may be disturbed owing to anomalies of the cross-link gains. These anomalies are likely the main cause of visual discomfort experienced when viewing stereo images, and are called Accommodation-Vergence Mismatches (AVM). Moreover, the absence of any useful accommodation depth cues when viewing 3D content on a flat panel (planar) display induces anomalous demands on binocular fusion, resulting in possible additional visual discomfort. Most prior efforts in this direction have focused on predicting anomalies in the AV cross-link using measurements on a computed disparity map. We further these contributions by developing a model that accounts for both accommodation and vergence, resulting in a new visual discomfort prediction algorithm dubbed the 3D-AVM Predictor. The 3D-AVM model and algorithm make use of a new concept we call local 3D bandwidth (BW) which is defined in terms of the physiological optics of binocular vision and foveation. The 3D-AVM Predictor accounts for anomalous motor responses of both accommodation and vergence, yielding predictive power that is statistically superior to prior models that rely on a computed disparity distribution only.
    IEEE Journal of Selected Topics in Signal Processing 06/2014; 8(3):415-427.
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    ABSTRACT: Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval---a combination of these two traditional problems---has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.
    IEEE Journal of Selected Topics in Signal Processing 04/2014; 8(4).