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  • PT-Symmetric Absorber-Laser Enables Electromagnetic Sensors with Unprecedented Sensitivity

    Farhat, Mohamed; Yang, Minye; Ye, Zhilu; Chen, Pai-Yen (ACS Photonics, American Chemical Society (ACS), 2020-07-07) [Article]
    Achieving extraordinarily high sensitivity is a long-sought goal in the development of novel and more capable electromagnetic sensors. We present here how a coherent perfect absorber-laser (CPAL) enabled by parity-time (PT) symmetry breaking may be exploited to build ultrasensitive monochromatic electromagnetic sensors that use radio waves, microwaves, terahertz radiations, or light. We argue the possibility of using such CPAL sensors to detect extremely small-scale perturbations of admittance or refractive index caused by, for example, low-density gas molecules and microscopic properties, as they may drastically vary the system’s output intensity from very low (coherent absorption) to high (lasing). We derive the physical bounds on CPAL sensors, showing that their sensitivity and resolvability may go well beyond traditional electromagnetic sensors, such as sensors based on Fabry-Perot cavities.
  • Semiparametric estimation of cross-covariance functions for multivariate random fields

    Qadir, Ghulam A.; Sun, Ying (Biometrics, Wiley, 2020-07-06) [Article]
    The prevalence of spatially referenced multivariate data has impelled researchers to develop procedures for joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Matérn marginals and highly flexible cross-covariance functions via their spectral representations. The flexibility in our cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows us to capture non-trivial cross-spectral features. We then develop a likelihood-based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations (PM2.5) and wind speed data over the West-North-Central climatic region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Matérn model and the linear model of coregionalization for spatial prediction.
  • Early-Stage Growth Mechanism and Synthesis Conditions-Dependent Morphology of Nanocrystalline Bi Films Electrodeposited from Perchlorate Electrolyte.

    Tishkevich, Daria; Grabchikov, Sergey; Zubar, Tatiana; Vasin, Denis; Trukhanov, Sergei; Vorobjova, Alla; Yakimchuk, Dmitry; Kozlovskiy, Artem; Zdorovets, Maxim; Giniyatova, Sholpan; Shimanovich, Dmitriy; Lyakhov, Dmitry; Michels, Dominik L.; Dong, Mengge; Gudkova, Svetlana; Trukhanov, Alex (Nanomaterials (Basel, Switzerland), MDPI AG, 2020-07-02) [Article]
    Bi nanocrystalline films were formed from perchlorate electrolyte (PE) on Cu substrate via electrochemical deposition with different duration and current densities. The microstructural, morphological properties, and elemental composition were studied using scanning electron microscopy (SEM), atomic force microscopy (AFM), and energy-dispersive X-ray microanalysis (EDX). The optimal range of current densities for Bi electrodeposition in PE using polarization measurements was demonstrated. For the first time, it was shown and explained why, with a deposition duration of 1 s, co-deposition of Pb and Bi occurs. The correlation between synthesis conditions and chemical composition and microstructure for Bi films was discussed. The analysis of the microstructure evolution revealed the changing mechanism of the films' growth from pillar-like (for Pb-rich phase) to layered granular form (for Bi) with deposition duration rising. This abnormal behavior is explained by the appearance of a strong Bi growth texture and coalescence effects. The investigations of porosity showed that Bi films have a closely-packed microstructure. The main stages and the growth mechanism of Bi films in the galvanostatic regime in PE with a deposition duration of 1-30 s are proposed.
  • DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

    Thafar, Maha A.; Olayan, Rawan S.; Ashoor, Haitham; Albaradei, Somayah; Bajic, Vladimir B.; Gao, Xin; Gojobori, Takashi; Essack, Magbubah (Journal of Cheminformatics, Springer Science and Business Media LLC, 2020-07-02) [Article]
    In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
  • Prism-based tunable InGaN/GaN self-injection locked blue laser diode system: study of temperature, injection ratio, and stability

    Khan, Mohammed Zahed Mustafa; Mukhtar, Sani; Holguin Lerma, Jorge Alberto; Alkhazragi, Omar; Ashry, Islam; Ng, Tien Khee; Ooi, Boon S. (Journal of Nanophotonics, SPIE-Intl Soc Optical Eng, 2020-07-02) [Article]
    A quasicontinuously wavelength tuned self-injection locked blue laser diode system employing a prism is presented. A rigorous analysis of the injection ratio (IR) in the form of three systems, namely high (HRS, ∼ − 0.7 dB IR), medium (MRS, ∼ − 1.5 dB IR), and low (LRS, ∼ − 3.0 dB IR) reflection systems, showed a direct relationship with the wavelength tunability whereas the usable system power exhibited an inverse correlation. In particular, MRS configuration demonstrated a concurrent optimization of tuning window and system power, thus emerging as a highly attractive candidate for practical realization. Moreover, a comprehensive investigation on two distinct MRS configurations employing different commercially available InGaN/GaN blue lasers, i.e., MRS-1 and MRS-2, displayed a wavelength tunability (system power) of ∼8.2 nm (∼7.6 mW) and ∼6.3 nm (∼11.6 mW), respectively, at a low injection current of 130 mA. In addition, both MRS configurations maintained high-performance characteristic with corresponding average optical linewidths of ∼80 and ∼58 pm and a side-mode-suppression-ratio of ≥12 dB. Lastly, a thorough stability analysis of HRS and MRS configurations, which are more prone to system instabilities due to elevated IRs, is performed at critical operation conditions of a high injection current of ≥260 mA and a temperature of 40°C, showing an extended stable performance of over 120 min, thus further substantiating the promising features of the prism-based systems for practical applications.
  • Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos

    Siripatana, Adil; Le Maitre, Olivier; Knio, Omar; Dawson, Clint; Hoteit, Ibrahim (Ocean Dynamics, Springer Science and Business Media LLC, 2020-07-01) [Article]
    Bayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a) to enable and infer uncertainties in a parameterized prior field. The feasibility of the method was successfully demonstrated on a simple transient diffusion equation. In this work, we adopt a similar approach to infer a spatially varying Manning’s n field in a coastal ocean model. The idea is to view the prior on the Manning’s n field as a stochastic Gaussian field, expressed through a covariance function with uncertain hyper-parameters. A generalized Karhunen-Loève (KL) expansion, which incorporates the construction of a reference basis of spatial modes and a coordinate transformation, is then applied to the prior field. To improve the computational efficiency of the method proposed in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a), we propose to use two polynomial chaos expansions to (i) approximate the coordinate transformation and (ii) build a cheap surrogate of the large-scale advanced circulation (ADCIRC) numerical model. These two surrogates are used to accelerate the Bayesian inference process using a Markov chain Monte Carlo algorithm. Water elevation data are inverted within an observing system simulation experiment framework, based on a realistic ADCIRC model, to infer the KL coordinates and hyper-parameters of a reference 2D Manning’s field. Our results demonstrate the efficiency of the proposed approach and suggest that including the hyper-parameter uncertainties greatly enhances the inferred Manning’s n field, compared with using a covariance with fixed hyper-parameters.
  • An in-field integrated capacitive sensor for rapid detection and quantification of soil moisture

    Surya, Sandeep Goud; Yuvaraja, Saravanan; Varrla, Eswaraiah; Baghini, Maryam Shojaei; Palaparthy, Vinay S.; Salama, Khaled N. (Sensors and Actuators B: Chemical, Elsevier BV, 2020-06-29) [Article]
    The development of in-situ soil moisture sensors (SMS) with advanced materials is the requirement of the future autonomous agriculture industry. However, an open challenge for these sensors is to control changes in the capacitance rather than resistance while attaining reliability, high performance, scalability and stability. In this work, a series of materials such as Graphite oxide (GO), Molybdenum disulfide (MoS2), Vanadium oxide (V2O5), and Molybdenum oxide (MoO3) are tested in realizing a receptor layer that can efficiently sense soil moisture. Here, we found that MoS2 offers the sensitivity, which is nearly three times higher (1200 pF) than in the case of V2O5 for any given range of soil-moisture content outperforming both GO and MoO3 materials. The corresponding increase in the sensitivities for MoO3, GO, MoS2, and V2O5 are ∼13%, ∼11%, ∼30%, and ∼9% respectively, for a variety of temperature up to 45 °C. A temperature variation of 25 °C to 50 °C showed a minimal increase in the sensitivity response for all the devices. We further demonstrated a record sensitivity of 540% with MoS2 in black soil and the corresponding response time was 65 sec. Finally, the recovery time for the MoS2 sensor is 27 s, which is quite fast.
  • Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

    Guo, Wenzhe; Yantir, Hasan Erdem; Fouda, Mohamed E.; Eltawil, Ahmed; Salama, Khaled N. (Electronics, MDPI AG, 2020-06-29) [Article]
    <jats:p>To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the fact that neurons that fire more spikes contribute more to network performance. Based on these criteria, we demonstrate that pruning with an adaptive spike count threshold provides a simple and effective approach that can reduce network size significantly and maintain high classification accuracy. The online adaptive pruning shows potential for developing energy-efficient training techniques due to less memory access and less weight-update computation. Furthermore, a parallel digital implementation scheme is proposed to implement spiking neural networks (SNNs) on field programmable gate array (FPGA). Notably, our proposed pruning strategies preserve the dense format of weight matrices, so the implementation architecture remains the same after network compression. The adaptive pruning strategy enables 2.3× reduction in memory size and 2.8× improvement on energy efficiency when 400 neurons are pruned from an 800-neuron network, while the loss of classification accuracy is 1.69%. And the best choice of pruning percentage depends on the trade-off among accuracy, memory, and energy. Therefore, this work offers a promising solution for effective network compression and energy-efficient hardware implementation of neuromorphic systems in real-time applications.</jats:p>
  • Efficient Acceleration of Stencil Applications through In-Memory Computing

    Yantir, Hasan Erdem; Eltawil, Ahmed; Salama, Khaled N. (Micromachines, MDPI AG, 2020-06-29) [Article]
    <jats:p>The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is increasing rapidly, especially after the era of big data and artificial intelligence. This fact forces new constraints in computer architecture design towards more data-centric principles. Therefore, new paradigms such as in-memory and near-memory processors have begun to emerge to counteract the memory bottleneck by bringing memory closer to computation or integrating them. Associative processors are a promising candidate for in-memory computation, which combines the processor and memory in the same location to alleviate the memory bottleneck. One of the applications that need iterative processing of a huge amount of data is stencil codes. Considering this feature, associative processors can provide a paramount advantage for stencil codes. For demonstration, two in-memory associative processor architectures for 2D stencil codes are proposed, implemented by both emerging memristor and traditional SRAM technologies. The proposed architecture achieves a promising efficiency for a variety of stencil applications and thus proves its applicability for scientific stencil computing.</jats:p>
  • Photonics based perfect secrecy cryptography: Toward fully classical implementations

    Mazzone, Valerio; Falco, Andrea Di; Cruz, Al; Fratalocchi, Andrea (Applied Physics Letters, AIP Publishing, 2020-06-29) [Article]
    Developing an unbreakable cryptography is a long-standing question and a global challenge in the internet era. Photonics technologies are at the frontline of research, aiming at providing the ultimate system with capability to end the cybercrime industry by changing the way information is treated and protected now and in the long run. Such a perspective discusses some of the current challenges as well as opportunities that classical and quantum systems open in the field of cryptography as both a field of science and engineering.
  • A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems

    Bergou, El Houcine; Diouane, Y.; Kungurtsev, V.; Royer, C. W. (arXiv, 2020-06-29) [Preprint]
    Least squares form one of the most prominent classes of optimization problems, with numerous applications in scientific computing and data fitting. When such formulations aim at modeling complex systems, the optimization process must account for nonlinear dynamics by incorporating constraints. In addition, these systems often incorporate a large number of variables, which increases the difficulty of the problem, and motivates the need for efficient algorithms amenable to large-scale implementations. In this paper, we propose and analyze a Levenberg-Marquardt algorithm for nonlinear least squares subject to nonlinear equality constraints. Our algorithm is based on inexact solves of linear least-squares problems, that only require Jacobian-vector products. Global convergence is guaranteed by the combination of a composite step approach and a nonmonotone step acceptance rule. We illustrate the performance of our method on several test cases from data assimilation and inverse problems: our algorithm is able to reach the vicinity of a solution from an arbitrary starting point, and can outperform the most natural alternatives for these classes of problems.
  • Metasurface Supporting Broadband Circular Dichroism for Reflected and Transmitted Fields Simultaneously

    Amin, M.; Siddiqui, Omar; Farhat, Mohamed (Journal of Physics D: Applied Physics, IOP Publishing, 2020-06-26) [Article]
    We demonstrate significant optical activity in the near-infrared spectrum of a chiral metasurface which is designed using an array of L-shape silver \textcolor{black}{nanostructure}. The far-field radiation from the plasmon-polariton surface wave currents produces combination of strong and highly dispersive orthogonal electric field components leading to the observation of broadband circular and elliptical polarization state (dichroism) for reflected and transmitted fields. Full-wave electromagnetic simulations show a linear to left hand- and right hand- circular polarization conversion between 200 -- 261 THz frequency (1.15 $\mu$m -- 1.5 $\mu$m wavelength) range for reflected and transmitted fields. \textcolor{red}{The structural chirality can be further enhanced by engraving another smaller L-dipole in nested configuration reaching near perfect polarization conversion efficiency.} The nested L-dipole configuration supports circular polarization conversion between 262 -- 306 THz frequency (980 nm -- 1.14 $\mu$m wavelength) range. \textcolor{red}{Full-wave simulations suggest clear enhancement of the surface currents with helical orientation leading to increased optical activity.} The proposed optical waveplate may be utilized in polarization control applications such as optical imaging, sensing, and display components.
  • Attributed heterogeneous network fusion via collaborative matrix tri-factorization

    Yu, Guoxian; Wang, Yuehui; Wang, Jun; Domeniconi, Carlotta; Guo, Maozu; Zhang, Xiangliang (Information Fusion, Elsevier BV, 2020-06-26) [Article]
    Heterogeneous network based data fusion can encode diverse inter- and intra-relations between objects, and has been sparking increasing attention in recent years. Matrix factorization based data fusion models have been invented to fuse multiple data sources. However, these models generally suffer from the widely-witnessed insufficient relations between nodes and from information loss when heterogeneous attributes of diverse network nodes are transformed into ad-hoc homologous networks for fusion. In this paper, we introduce a general data fusion model called Attributed Heterogeneous Network Fusion (AHNF). AHNF firstly constructs an attributed heterogeneous network composed with different types of nodes and the diverse attribute vectors of these nodes. It uses indicator matrices to differentiate the observed inter-relations from the latent ones, and thus reduces the impact of insufficient relations between nodes. Next, it collaboratively factorizes multiple adjacency matrices and attribute data matrices of the heterogeneous network into low-rank matrices to explore the latent relations between these nodes. In this way, both the network topology and diverse attributes of nodes are fused in a coordinated fashion. Finally, it uses the optimized low-rank matrices to approximate the target relational data matrix of objects and to effectively accomplish the relation prediction. We apply AHNF to predict the lncRNA-disease associations using diverse relational and attribute data sources. AHNF achieves a larger area under the receiver operating curve 0.9367 (by at least 2.14%), and a larger area under the precision-recall curve 0.5937 (by at least 28.53%) than competitive data fusion approaches. AHNF also outperforms competing methods on predicting de novo lncRNA-disease associations, and precisely identifies lncRNAs associated with breast, stomach, prostate, and pancreatic cancers. AHNF is a comprehensive data fusion framework for universal attributed multi-type relational data. The code and datasets are available at
  • Automating Analogue AI Chip Design with Genetic Search

    Krestinskaya, Olga; Salama, Khaled N.; James, Alex P. (Advanced Intelligent Systems, Wiley, 2020-06-25) [Article]
    Optimization of analogue neural circuit designs is one of the most challenging, complicated, time-consuming, and expensive tasks. Design automation of analogue neuromemristive chips is made difficult by the need to design chips at low cost, ease of scaling, high-energy efficiency, and small on-chip area. The rapid progress in edge AI computing applications generates high demand for developing smart sensors. The integration of high-density analogue computing AI chips as coprocessing units to sensors is gaining popularity. This article proposes a hardware–software codesign framework to speed up and automate the design of analogue neuromemristive chips. This work uses genetic algorithms with objective functions that take into account hardware nonidealities such as limited precision of devices, the device-to-device variability, and device failures. The optimized neural architectures and hyperparameters successfully map with the library of relevant neuromemristive analogue hardware blocks. The results demonstrate the advantage of proposed automation to speed up the analogue circuit design of large-scale neuromemristive networks and reduce overall design costs for AI chips.
  • On Integrated Access and Backhaul Networks: Current Status and Potentials

    Madapatha, Charitha; Makki, Behrooz; Fang, Chao; Teyeb, Oumer; Dahlman, Erik; Alouini, Mohamed-Slim; Svensson, Tommy (arXiv, 2020-06-25) [Preprint]
    In this paper, we introduce and study the potentials and challenges of integrated access and backhaul (IAB) as one of the promising techniques for evolving 5G networks. We study IAB networks from different perspectives. We summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on IAB, and highlight the main IAB-specific agreements on different protocol layers. Also, concentrating on millimeter wave-based communications, we evaluate the performance of IAB networks in both dense and suburban areas. Using a finite stochastic geometry model, with random distributions of IAB nodes as well as user equipments (UEs) in a finite region, we study the service coverage rate defined as the probability of the event that the UEs' minimum rate requirements are satisfied. We present comparisons between IAB and hybrid IAB/fiber-backhauled networks where a part or all of the small base stations are fiber-connected. Finally, we study the robustness of IAB networks to weather and various deployment conditions and verify their effects, such as blockage, tree foliage, rain as well as antenna height/gain on the coverage rate of IAB setups, as the key differences between the fiber-connected and IAB networks. As we show, IAB is an attractive approach to enable the network densification required by 5G and beyond.
  • High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model

    Sifaou, Houssem; Kammoun, Abla; Alouini, Mohamed-Slim (arXiv, 2020-06-25) [Preprint]
    Quadratic discriminant analysis (QDA) is a widely used classification technique that generalizes the linear discriminant analysis (LDA) classifier to the case of distinct covariance matrices among classes. For the QDA classifier to yield high classification performance, an accurate estimation of the covariance matrices is required. Such a task becomes all the more challenging in high dimensional settings, wherein the number of observations is comparable with the feature dimension. A popular way to enhance the performance of QDA classifier under these circumstances is to regularize the covariance matrix, giving the name regularized QDA (R-QDA) to the corresponding classifier. In this work, we consider the case in which the population covariance matrix has a spiked covariance structure, a model that is often assumed in several applications. Building on the classical QDA, we propose a novel quadratic classification technique, the parameters of which are chosen such that the fisher-discriminant ratio is maximized. Numerical simulations show that the proposed classifier not only outperforms the classical R-QDA for both synthetic and real data but also requires lower computational complexity, making it suitable to high dimensional settings.
  • Break Point Detection for Functional Covariance

    Jiao, Shuhao; Frostig, Ron D.; Ombao, Hernando (arXiv, 2020-06-24) [Preprint]
    Many experiments record sequential trajectories that oscillate around zero. Such trajectories can be viewed as zero-mean functional data. When there are structural breaks (on the sequence of curves) in higher order moments, it is often difficult to spot these by mere visual inspection. Thus, we propose a detection and testing procedure to find the change-points in functional covariance. The method is fully functional in the sense that no dimension reduction is needed. We establish the asymptotic properties of the estimated change-point. The effectiveness of the proposed method is numerically validated in the simulation studies and an application to study structural changes in rat brain signals in a stroke experiment.
  • Modern Deep Learning in Bioinformatics.

    Li, Haoyang; Tian, Shuye; Li, Yu; Fang, Qiming; Tan, Renbo; Pan, Yijie; Huang, Chao; Xu, Ying; Gao, Xin (Journal of molecular cell biology, Oxford University Press (OUP), 2020-06-24) [Article]
    Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.
  • Modern Deep Learning in Bioinformatics.

    Li, Haoyang; Tian, Shuye; Li, Yu; Fang, Qiming; Tan, Renbo; Pan, Yijie; Huang, Chao; Xu, Ying; Gao, Xin (Journal of molecular cell biology, Oxford University Press (OUP), 2020-06-24) [Article]
    Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.
  • Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications

    Guo, Shuaishuai; Zhang, Haixia; Zhang, Peng; Zhang, Shuping; Xu, Chengcheng; Alouini, Mohamed-Slim (arXiv, 2020-06-23) [Preprint]
    This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

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