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Multimodal communication (MC) plays a crucial role in the Underwater Sensor Networks (UWSNs). The appropriate resource allocation of MC can significantly optimize the UWSNs performance. This letter investigates the optimum data transmission allocation in the underwater MC. Considering the fundamental MC tradeoff, the MC energy efficiency model is established and formulated as a mixed integer nonlinear programming problem with the provable convex optimization properties. Then this letter designs two schemes for the various data amounts to optimize the optical-acoustic transmission ratio and an optimized data collection algorithm to save the MC energy. Simulation results validate the effectiveness of this letter. [ABSTRACT FROM AUTHOR]

Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. However, it remains challenging in a realistic scenario, i.e., limited number of labeled samples and sensors. This article proposes a novel deep learning method to achieve accurate and robust HAR with only a single inertial measurement unit (IMU) sensor. Our contributions are twofold. First, based on the skinned multiperson linear (SMPL) model, we build a large synthetic HAR dataset containing multimodal measurements: acceleration and angular velocity, which were generated according to the forward kinematics. Second, We propose a multiple-level domain adaptive learning model with information-theoretically stimulated constraints to simultaneously align the distributions of low- and high-level representations of virtual and real HAR data. The proposed mutual information constraints encourage the neural network to learn a disentangled representation for the multimodal sensing data. Comprehensive experimental results on three publicly available datasets demonstrate that the proposed method compares favorably with competing ones and has robust performance with variable labeled samples. [ABSTRACT FROM AUTHOR]

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios. [ABSTRACT FROM AUTHOR]

Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does significantly affect the way in which a distribution grid is planned and operated. This paper, based on random matrix theory (RMT), proposes a statistical, data-driven framework to handle the massive grid data, in contrast to its deterministic, model-based counterpart. Combining the RMT-based data-mining framework with conventional techniques, some heuristics are derived as the solution to the invisible units detection and estimation task: linear eigenvalue statistic indicators (LESs) are suggested as the main ingredients of the solution; according to the statistical properties of LESs, the hypothesis testing is formulated to conduct change point detection in the high-dimensional space. The proposed method is promising for anomaly detection and pertinent to current distribution networks—it is capable of detecting invisible power usage and fraudulent behavior while even being able to locate the suspect's location. Case studies, using both simulated data and actual data, validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers the precoding problem in massive multiuser multiple-input multiple-output (MU-MIMO) systems equipped with low-resolution digital-to-analog converters. In previous literature on this topic, it is commonly assumed that the channel state information (CSI) is perfectly known. However, in practical applications the CSI is inevitably contaminated by noise. In this paper, we propose, for the first time, an eigen-inference (EI) precoding scheme to improve the error performance of the coarsely quantized massive MU-MIMO systems under imperfect CSI, which is mathematically modeled by a sum of two rectangular random matrices (RRMs): $\sqrt{1 - \eta } {{\bf H}} + \sqrt\eta {{\bf E}}$. Instead of performing analysis based on the RRM, using Girkoąŕs Hermitization trick, the proposed method leverages the block random matrix theory by augmenting the RRM into a block symmetric channel matrix (BSCA). Specially, we derive the empirical distribution of the eigenvalues of the BSCA and establish the limiting spectra distribution connection between the true BSCA and its noisy observation. Then, based on these theoretical results, we propose an EI-based moments matching method for CSI-related noise level ($\eta$) estimation and a rotation invariant estimation method for CSI reconstruction. Based on the cleaned CSI, the quantized precoding problem is tackled via the Bussgang theorem and the Lagrangian multiplier method. The prosed methods are finally verified by numerical simulations and the results demonstrate the effectiveness of the proposed precoder. [ABSTRACT FROM AUTHOR]

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS). Using 1-bit DACs can significantly reduce the power consumption. This paper addresses the precoding problem for the massive narrow-band MU-MIMO downlink system equipped with 1-bit DACs at each BS. In such a system, the precoding problem plays a central role as the precoded symbols are affected by extra distortion introduced by 1-bit DACs. In this paper, we develop a highly efficient nonlinear precoding algorithm based on the alternative direction method framework. Unlike the classic algorithms, such as the semidefinite relaxation (SDR) and squared-infinity norm Douglas–Rachford splitting (SQUID) algorithms, which solve convex relaxed versions of the original precoding problem, the new algorithm solves the original nonconvex problem directly. The new algorithm is guaranteed to globally converge under some mild conditions. A sufficient condition for its convergence has been derived. The experimental results in various conditions demonstrated that the new algorithm can achieve the state-of-the-art performance comparable with the SDR algorithm while being much more efficient (e.g., more than 300 times faster than the SDR algorithm). [ABSTRACT FROM AUTHOR]

We address the problem of locating multiple sources from the Euclidean distance matrix (EDM), which can be obtained from the received signal strength or time of arrival measurements. In EDM-based localization, EDM is usually corrupted by some inevitable factors, such as non-line-of-sight propagation, hardware failures, and strong interference. Note that EDM is a low-rank matrix but not a positive semidefinitematrix, classical semidefinite programming (SDP)-based algorithms cannot be implemented directly to handle the case. We derive an SDP-based low-rank solution to reconstruct EDM based on the semidefinite embedding lemma. Based on the recovered EDM, unlike some existing conventional non-convex estimators, a semidefinite relaxation method is developed to fix the locations of sources. In particular, we relax the non-convex localization problem into convex one by using square range information. Numerical simulation results demonstrate that the proposed algorithm performs higher accuracy while increasing slightly computational complexity as compared with the other existing approaches. [ABSTRACT FROM PUBLISHER]