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  • To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

    Yang, Zhuo (2022-06) [Dissertation]
    Advisor: Zhang, Xiangliang
    Committee members: Wang, Di; Moshkov, Mikhail; Feng, Zhuo
    Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. Understanding and exploiting the Label Dependency (LD) is well accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack. From the perspective of generalization on clean data, previous works have proved the advantage of exploiting LD in multi-label classification. To further verify the positive role of LD in multi-label classification and address previous limitations, we originally propose an approach named Prototypical Networks for Multi- Label Learning (PNML). Specially, PNML addresses multi-label classification from the angle of estimating the positive and negative class distribution of each label in a shared nonlinear embedding space. PNML achieves the State-Of-The-Art (SOTA) classification performance on clean data. From the perspective of robustness under evasion attack, as a pioneer, we firstly define the attackability of an multi-label classifier as the expected maximum number of flipped decision outputs by injecting budgeted perturbations to the feature distribution of data. Denote the attackability of a multi-label classifier as C∗, and the empirical evaluation of C∗ is an NP-hard problem. We thus develop a method named Greedy Attack Space Exploration (GASE) to estimate C∗ efficiently. More interestingly, we derive an information-theoretic upper bound for the adversarial risk faced by multi-label classifiers. The bound unveils the key factors determining the attackability of multi-label classifiers and points out the negative role of LD in multi-label classifiers’ adversarial robustness, i.e. LD helps the transfer of attack across labels, which makes multi-label classifiers more attackable. One step forward, inspired by the derived bound, we propose a Soft Attackability Estimator (SAE) and further develop Adversarial Robust Multi-label learning with regularized SAE (ARM-SAE) to improve the adversarial robustness of multi-label classifiers. This work gives a more comprehensive understanding of LD in multi-label learning. The exploiting of LD should be encouraged since its positive role in models’ generalization on clean data, but be restricted because of its negative role in models’ adversarial robustness.
  • On the Performance Optimization of Two-way Hybrid VLC/RF based IoT System over Cellular Spectrum

    Ghosh, Sutanu; Alouini, Mohamed-Slim (IEEE Internet of Things Journal, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-24) [Article]
    This paper investigates the system outage performance of a useful architecture of two-way hybrid visible light communication/radio frequency (VLC/RF) communication using overlay mode of cooperative cognitive radio network (CCRN). The demand of high data rate application can be fulfilled using VLC link and communication over a wide area of coverage with high reliability can be achieved through RF link. In the proposed architecture, cooperative communication between two licensed user (LU) nodes is accomplished via an aggregation agent (AA). AA can perform like a relay node and in return, it can access the LU spectrum for two-way communications with Internet-of-Things (IoT) device. First, closed form expressions of outage probability of both LU and IoT communication are established. On the basis of these expressions, optimization problems are formulated to achieve minimum outage probability of both LU and IoT network. The impacts of both VLC and RF system parameters on these systems outage probability and throughput are finally shown in simulation results.
  • Distributed Energy Resources Cybersecurity Outlook: Vulnerabilities, Attacks, Impacts, and Mitigations

    Zografopoulos, Ioannis; Konstantinou, Charalambos; Hatziargyriou, Nikos D. (arXiv, 2022-05-23) [Preprint]
    The digitalization and decentralization of the electric power grid are key thrusts towards an economically and environmentally sustainable future. Towards this goal, distributed energy resources (DER), including rooftop solar panels, battery storage, electric vehicles, etc., are becoming ubiquitous in power systems, effectively replacing fossil-fuel based generation. Power utilities benefit from DERs as they minimize transmission costs, provide voltage support through ancillary services, and reduce operational risks via their autonomous operation. Similarly, DERs grant users and aggregators control over the power they produce and consume. Apart from their sustainability and operational objectives, the cybersecurity of DER-supported power systems is of cardinal importance. DERs are interconnected, interoperable, and support remotely controllable features, thus, their cybersecurity should be thoroughly considered. DER communication dependencies and the diversity of DER architectures (e.g., hardware/software components of embedded devices, inverters, controllable loads, etc.) widen the threat surface and aggravate the cybersecurity posture of power systems. In this work, we focus on security oversights that reside in the cyber and physical layers of DERs and can jeopardize grid operations. We analyze adversarial capabilities and objectives when manipulating DER assets, and then present how protocol and device -level vulnerabilities can materialize into cyberattacks impacting power system operations. Finally, we provide mitigation strategies to thwart adversaries and directions for future DER cybersecurity.
  • Offline Training-based Mitigation of IR Drop for ReRAM-based Deep Neural Network Accelerators

    Lee, Sugil; Fouda, Mohammed E.; Lee, Jongeun; Eltawil, Ahmed; Kurdahi, Fadi (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-23) [Article]
    Recently, ReRAM-based hardware accelerators showed unprecedented performance compared the digital accelerators. Technology scaling causes an inevitable increase in interconnect wire resistance, which leads to IR drops that could limit the performance of ReRAM-based accelerators. These IR drops deteriorate the signal integrity and quality especially in the Crossbar structures which are used to build high density ReRAMs. Hence, finding a software solution that can predict the effect of IR drop without involving expensive hardware or SPICE simulations, is very desirable. In this paper, we propose two neural networks models to predict the impact of the IR drop problem. These models are uded to evaluate the performance of the different deep neural networks (DNNs) models including binary and quantized neural networks showing similar performance (i.e., recognition accuracy) to the golden validation (i.e., SPICE-based DNN validation). In addition, these predication models are incorporated into DNNs training framework to efficiently retrain the DNN models and bridge the accuracy drop. To further enhance the validation accuracy, we propose incremental training methods. The DNN validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method even with challenging datasets such as CIFAR10 and SVHN.
  • An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors

    Zhou, Longxi; Meng, Xianglin; Huang, Yuxin; Kang, Kai; Zhou, Juexiao; Chu, Yuetan; Li, Haoyang; Xie, Dexuan; Zhang, Jiannan; Yang, Weizhen; Bai, Na; Zhao, Yi; Zhao, Mingyan; Wang, Guohua; Carin, Lawrence; Xiao, Xigang; Yu, Kaijiang; Qiu, Zhaowen; Gao, Xin (Nature Machine Intelligence, Springer Science and Business Media LLC, 2022-05-23) [Article]
    Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight.
  • Discovering trends and hotspots of biosafety and biosecurity research via machine learning

    Guan, Renchu; Pang, Haoyu; Liang, Yanchun; Shao, Zhongjun; Gao, Xin; Xu, Dong; Feng, Xiaoyue (Briefings in bioinformatics, Oxford University Press (OUP), 2022-05-22) [Article]
    Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms.
  • Optimized explicit Runge–Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics

    Al Jahdali, Rasha; Dalcin, Lisandro; Boukharfane, Radouan; Nolasco, I.R.; Keyes, David E.; Parsani, Matteo (Computers & Mathematics with Applications, Elsevier BV, 2022-05-20) [Article]
    In compressible computational fluid dynamics, the step size of explicit time integration schemes is often constrained by stability when high-order accurate spatial discretizations are used. We report a set of new optimized explicit Runge–Kutta schemes for the integration of systems of ordinary differential equations arising from the spatial discretization of wave propagation problems with high-order entropy stable collocated discontinuous Galerkin methods. The eigenvalues of the discrete spatial operator for the advection equation and the propagation of an isentropic vortex with the compressible Euler equations for various values of the problems' parameters are used to optimize the stability region of the proposed time integration schemes. To demonstrate the efficiency and the robustness of the methods, we solve the compressible turbulent flow past the Valeo controlled-diffusion airfoil and a delta wing at a Reynolds number of 8.3*10 5 and 106, respectively. A thorough analysis of the performance of the two families of optimized schemes revealed that methods generated using the spectra of the vortex problem are 6-to-20% faster than methods constructed using the spectra of the advection equation. Compared to widely used explicit Runge–Kutta schemes, the methods designed using the spectra of the vortex problem yield a time-to-solution saving of approximately 6-to-38%. For large-scale time-dependent partial differential equations computations, these gains mean saving hundreds of thousands if not millions of core hours. In addition, the new methods can be effectively and efficiently applied to integrate systems of ordinary differential equations arising from a wide range of spatial discretization, including discontinuous Galerkin spectral element methods, spectral difference methods, and flux reconstruction methods.
  • Road Users Classification Based on Bi-Frame Micro-Doppler With 24-GHz FMCW Radar

    Coppola, Rudi; Ahmed, Sajid; Alouini, Mohamed-Slim (Frontiers in Signal Processing, Frontiers Media SA, 2022-05-20) [Article]
    This study shows an approach for classifying road users using a 24-GHz millimeter-wave radar. The sensor transmits multiple linear frequency–modulated waves, which enable range estimation and Doppler-shift estimation of targets in the scene. We aimed to develop a solution for localization and classification, which yielded the same performance when the sensor was fixed on ground or mounted on a moving platform such as a car or quadcopter. In this proposed approach, classification was achieved using supervised learning and a set of hand crafted features independent of relative speed between the target and sensor. The proposed model is based on obtaining micro-Doppler information; only one receiver is used. Therefore, in addition to the target reflectivity, no geometrical information is used. For our study, we selected three classes: pedestrians, cyclists, and cars. We then illustrated distinctive micro-Doppler features for each class based on simulations, which we compared with real-world data. Our results confirm that a limited set of low-complexity features yields high accuracy scores when the target’s trajectory does not excessively deviate from the radar’s radial direction.
  • The Convex Uncertain Voronoi Diagram for Safe Multi-Robot Multi-Target Tracking Under Localization Uncertainty

    Chen, Jun; Dames, Philip (Research Square Platform LLC, 2022-05-19) [Preprint]
    Accurately detecting, localizing, and tracking an unknown and time-varying number of dynamic targets using a team of mobile robots is a challenging problem that requires robots to reason about the uncertainties in their collected measurements. The problem is made more challenging when robots are uncertain about their own states, as this makes it difficult to both collectively localize targets and avoid collisions with one another. In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot. Our algorithms are capable of safely driving mobile robots towards areas of high information distribution while having them maintain coverage of the whole area of interest. We demonstrate the efficacy of these algorithms via a series of simulated and hardware tests, and compare the results to our previous work which assumes perfect localization.
  • The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization

    Kovalev, Dmitry; Gasnikov, Alexander (arXiv, 2022-05-19) [Preprint]
    In this paper, we study the fundamental open question of finding the optimal high-order algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound Ω(ϵ−2/(3p+1)) on the number of the p-th order oracle calls required by an algorithm to find an ϵ-accurate solution to the problem, where the p-th order oracle stands for the computation of the objective function value and the derivatives up to the order p. However, the existing state-of-the-art high-order methods of Gasnikov et al. (2019b); Bubeck et al. (2019); Jiang et al. (2019) achieve the oracle complexity O(ϵ−2/(3p+1)log(1/ϵ)), which does not match the lower bound. The reason for this is that these algorithms require performing a complex binary search procedure, which makes them neither optimal nor practical. We fix this fundamental issue by providing the first algorithm with O(ϵ−2/(3p+1)) p-th order oracle complexity.
  • Variance partitioning in spatio-temporal disease mapping models

    Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Haavard (Statistical methods in medical research, SAGE Publications, 2022-05-18) [Article]
    Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.
  • Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections

    Salvaña, Mary Lai O.; Lenzi, Amanda; Genton, Marc G. (Journal of the American Statistical Association, Informa UK Limited, 2022-05-17) [Article]
    When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special property is observed: the covariances and cross-covariances are stronger in certain directions. This property is attributed to the presence of natural forces, such as wind, which cause the transport and dispersion of these variables. This spatio-temporal dynamics prompted the use of the Lagrangian reference frame alongside any Gaussian spatio-temporal geostatistical model. Under this modeling framework, a whole new class was birthed and was known as the class of spatio-temporal covariance functions under the Lagrangian framework, with several developments already established in the univariate setting, in both stationary and nonstationary formulations, but less so in the multivariate case. Despite the many advances in this modeling approach, efforts have yet to be directed to probing the case for the use of multiple advections, especially when several variables are involved. Accounting for multiple advections would make the Lagrangian framework a more viable approach in modeling realistic multivariate transport scenarios. In this work, we establish a class of Lagrangian spatio-temporal cross-covariance functions with multiple advections, study its properties, and demonstrate its use on a bivariate pollutant dataset of particulate matter in Saudi Arabia.
  • Role of C-Reactive Protein in Diabetic Inflammation

    Stanimirovic, Julijana; Radovanovic, Jelena; Banjac, Katarina; Obradovic, Milan; Essack, Magbubah; Zafirovic, Sonja; Gluvic, Zoran; Gojobori, Takashi; Isenovic, Esma (Mediators of Inflammation, Hindawi Limited, 2022-05-17) [Article]
    Even though type 2 diabetes mellitus (T2DM) represents a worldwide chronic health issue that affects about 462 million people, specific underlying determinants of insulin resistance (IR) and impaired insulin secretion are still unknown. There is growing evidence that chronic subclinical inflammation is a triggering factor in the origin of T2DM. Increased C-reactive protein (CRP) levels have been linked to excess body weight since adipocytes produce tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which are pivotal factors for CRP stimulation. Furthermore, it is known that hepatocytes produce relatively low rates of CRP in physiological conditions compared to T2DM patients, in which elevated levels of inflammatory markers are reported, including CRP. CRP also participates in endothelial dysfunction, the production of vasodilators, and vascular remodeling, and increased CRP level is closely associated with vascular system pathology and metabolic syndrome. In addition, insulin-based therapies may alter CRP levels in T2DM. Therefore, determining and clarifying the underlying CRP mechanism of T2DM is imperative for novel preventive and diagnostic procedures. Overall, CRP is one of the possible targets for T2DM progression and understanding the connection between insulin and inflammation may be helpful in clinical treatment and prevention approaches.
  • Charging Techniques for UAV-Assisted Data Collection: Is Laser Power Beaming the Answer?

    Lahmeri, Mohamed-Amine; Kishk, Mustafa A.; Alouini, Mohamed-Slim (IEEE Communications Magazine, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-17) [Article]
    As COVID-19 has increased the need for connectivity around the world, researchers are targeting new technologies that could improve coverage and connect the unconnected in order to make progress toward the United Nations Sustainable Development Goals. In this context, drones are seen as one of the key features of 6G wireless networks that could extend the coverage of previous wireless network generations. That said, limited onboard energy seems to be the main drawback that hinders the use of drones for wireless coverage. Therefore, different wireless and wired charging techniques, such as laser beaming, charging stations, and tether stations, are proposed. In this article, we analyze and compare these different charging techniques by performing extensive simulations for the scenario of drone-assisted data collection from ground-based Internet of Things devices. We analyze the strengths and weaknesses of each charging technique, and finally show that laser-powered drones strongly compete with, and outperform in some scenarios, other charging techniques.
  • Reconfigurable Intelligent Surface Enabled Interference Nulling and Signal Power Maximization in mmWave bands

    Ye, Jia; Kammoun, Abla; Alouini, Mohamed-Slim (IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-17) [Article]
    Reconfigurable intelligent surface (RIS) has emerged as a promising mean to enhance wireless transmission. The effective reflected paths provided by RIS are able to alleviate the susceptibility to blockage effects, especially in high-frequency band communications, where signals experience severe path loss and high directivity. This paper is concerned with an RIS-assisted system over the millimeter wave (mmWave) channel characterized by sparse propagation paths. A base station tries to connect with the desired user through an RIS, while the undesired user can also receive the signal transmitted from BS unavoidably, which is treated as the interference signal. All terminals are assumed to be equipped with a single antenna for the sake of simplicity. The paper aims to propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power transmitted from the base station (BS) at the desired user, while nulling the received interference signal power at the undesired user. The proposed reflecting design relies on the decomposition of the reflecting beamforming vectors and all channel path vectors into Kronecker product of factors being uni-modulus vectors. By exploiting characteristics of Kronecker mixed products, different factors of the reflecting are designed for either nulling the interference signal at the undesired user, or coherently combining data paths at the desired user. Furthermore, a channel estimation strategy is proposed to enable the proposed reflecting beamforming design. The magnitude, azimuth, and elevation arrival and departure angles of desired and undesired paths are estimated by an efficient 2-dimension (2-D) line spectrum optimization technique based on the atomic norm minimization (ANM) framework. The performance of the reflecting designs and channel estimation scheme is analyzed and demonstrated by simulation results.
  • Ergodic Capacity Analysis of UAV-based FSO Links over Foggy Channels

    Jung, Kug-Jin; Nam, Sung Sik; Alouini, Mohamed-Slim; Ko, Young-Chai (IEEE Wireless Communications Letters, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-17) [Article]
    In this paper, we investigate the ergodic capacity of unmanned aerial vehicle (UAV)-based free space optics (FSO) links over random foggy channel. More specifically, we derive composite probability density function (PDF) and close approximation for the moments of the composite PDF using the statistical model of a UAV-based 3D pointing error and a random foggy channel. With it, we obtain upper bound and asymptotic approximation of the ergodic capacity for the two possible detection techniques of intensity modulation/direct detection (IM/DD) and heterodyne detection at high and low signal-to-noise ratio (SNR) regimes. The numerical results confirm all the presented analytic results via computer-based Monte-Carlo simulations.
  • Microstructural analysis of N-polar InGaN directly grown on a ScAlMgO4(0001) substrate

    Velazquez-Rizo, Martin; Najmi, Mohammed A.; Iida, Daisuke; Kirilenko, Pavel; Ohkawa, Kazuhiro (Applied Physics Express, IOP Publishing, 2022-05-17) [Article]
    We report the characterization of a N-polar InGaN layer deposited by metalorganic vapor-phase epitaxy on a ScAlMgO4(0001) (SAM) substrate without a low-temperature buffer layer. The InGaN layer was tensile-strained, and its stoichiometry corresponded to In0.13Ga0.87N. We also present the microstructural observation of the InGaN/SAM interface via integrated differential phase contrast-scanning transmission electron microscopy. The results show that the interface between N-polar InGaN and SAM occurs between the O atoms of the O–Sc SAM surface and the (Ga,In) atoms of InGaN.
  • Acoustic Beam Splitting and Cloaking Based on a Compressibility-Near-Zero Medium

    Xu, Changqing; Huang, Sibo; Guo, Zhiwei; Jiang, Haitao; Li, Yong; Wu, Ying; Chen, Hong (Physical Review Applied, American Physical Society (APS), 2022-05-16) [Article]
    We report an artificial acoustic compressibility-near-zero medium made of a phononic crystal composed of epoxy blocks arranged in a square lattice. Its anisotropic effective density leads to a linear cross in its isofrequency contour in the vicinity of the Brillouin zone center, as its effective compressibility approaches zero. When a Gaussian beam is normally incident on the phononic crystal, a splitting effect is achieved at the frequency of the crossing point. Based on such a beam-splitting effect, an acoustic cloaking of an irregular-shaped object embedded in the phononic crystal is demonstrated both theoretically and experimentally. Such an anisotropic zero-index material offers a potential method to control acoustic waves.
  • Joint Beamforming and Clustering for Energy Efficient Multi-Cloud Radio Access Networks

    Reifert, Robert-Jeron; Ahmad, Alaa Alameer; Dahrouj, Hayssam; Chaaban, Anas; Sezgin, Aydin; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim (IEEE, 2022-05-16) [Conference Paper]
    The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices’ numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l 0 -norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.
  • Enhancing QoS Through Fluid Antenna Systems over Correlated Nakagami-m Fading Channels

    Tlebaldiyeva, Leila; Nauryzbayev, Galymzhan; Arzykulov, Sultangali; Eltawil, Ahmed; Tsiftsis, Theodoros (IEEE, 2022-05-16) [Conference Paper]
    Fluid antenna systems (FAS) enable mechanically flexible antennas that offer adaptability and flexibility for modern communication devices. In this work, we present a conceptual model for a single-antenna N-port (SANP) FAS over spatially correlated Nakagami-m fading channels and compare it with the traditional diversity schemes in terms of outage probability. The proposed FAS model switches to the best antenna port and resembles the operation of a selection combining (SC) diversity. FAS improves the quality of service (QoS) of the network through antenna port selection. The advantage of FAS is the ability to fit hundreds of antenna ports into a half-wavelength antenna size at the cost of spatial channel correlation. Simulation results demonstrate the superior outage probability performance of FAS at several tens of antenna ports compared to the traditional diversity schemes such as maximum ratio combining, equal gain combining, and SC. Moreover, the novel probability and cumulative density functions for the land mobile correlated Nakagami-m random variates are evaluated in this paper.

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