We design and implement an algorithm for solving the static RWA problem based on an LP relaxation formulation. This formulation is capable of providing integer optimal solutions despite the absence of integrality cons...
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We design and implement an algorithm for solving the static RWA problem based on an LP relaxation formulation. This formulation is capable of providing integer optimal solutions despite the absence of integrality constraints for a large subset of RWA input instances. In static RWA there is no a-priori knowledge of the channels usage and the interference among them cannot be avoided once the solution has been found. To take into consideration adjacent channel interference, we extend our formulation and model the interference by a set of analytical formulas as additional constraints on RWA.
Recently, ontology embeddings representing entities in a low-dimensional space have been proposed for ontology completion. However, the ontology embeddings for concept subsumption prediction do not address the difficu...
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The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach...
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The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint p...
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ISBN:
(数字)9798350376715
ISBN:
(纸本)9798350376722
The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint power allocation and beamforming for 5 G and beyond mmWave-NOMA systems are essential, which can be achieved through optimization approaches. To this end, we have employed a Deep Reinforcement Learning (DRL) approach for policy generation, leading to an optimized sum-rate for users. Unlike existing methods, our approach is not susceptible to channel impulse response (CIR) estimation errors. The actor-critic framework is utilized to measure the immediate reward and provide new actions to maximize the overall Q-value of the network. The immediate reward is defined based on the summation of the rates of two users, considering the minimum guaranteed rate for each user and the total consumed power as constraints. The simulation results demonstrate the superiority of the proposed approach compared to state-of-the-art counterparts, including Time-Division Multiple Access (TDMA) and NLOS-NOMA optimization strategies, in terms of sum-rate of users, while considering both perfect and imperfect channel state information (CSI) at the receiver. This work can also be beneficial for communications in vehicular networks, particularly in applications involving Collective Perception Messages (CPMs) dissemination for autonomous driving.
The Resource Description Framework (RDF) is a framework for describing metadata, such as attributes and relationships of resources on the Web. Machine learning tasks for RDF graphs adopt three methods: (i) support vec...
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An approach for real-time analyses, estimation and prognoses of strong motion seismic waves with stochastic modeling and neural network is presented. As input information are given the parameters of recorded part of a...
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An approach for real-time analyses, estimation and prognoses of strong motion seismic waves with stochastic modeling and neural network is presented. As input information are given the parameters of recorded part of accelerogram, principle axis transform and spectral characteristics of the wave. With the help of stochastic long range dependence time series analyses is determined the beginning of destructive phase of strong motion acceleration. The suggested approach gives possibility to classify seismic waves from recorded part of the wave to certain class, according to developed seismic waves classification. For different kind of classified waves are suggested different kind prognoses models. The prognoses is realized with the help of neural network, build on the principle of vector quantization. For prognoses of destructive phase of strong motion waves is suggested scene-oriented model. The determined statistical function of density distribution of recorded data from accelerogram are generating in real time. The received destructive phase prognoses of strong motion waves can be used in devices for structural control. Examples of received prognoses are compared with real data of strong motion waves. Simulation and numerical results are shown.
This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the sample size and the nu...
This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the sample size and the number of regression coefficients both increase. This subject has two cases: one is the dependence of performance on the hyperparameters in prior distribution and the other is how the performance depends on the sample size and the number of nonzero components in regression coefficients. To address this subject, we employ methods of statistical mechanics in physics. An equivalent of thermodynamic potential is calculated, following some similarities between Bayes inference and statistical mechanics. It is known in physics that the potential provides physical quantities. In a similar way, the equivalent of potential provides us with the asymptotic evaluation of estimation performance instead of physical quantities. The estimation performance in the two cases appears to have sharply divided regions in which the estimation succeeds and fails. These results are compared with related studies.
This book gathers selected papers presented at the conference “Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology,” one of the first initiatives devoted to the problems ...
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ISBN:
(数字)9789811538674
ISBN:
(纸本)9789811538667
This book gathers selected papers presented at the conference “Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology,” one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas. The aim of the conference was to establish a platform for experts to combine their efforts and share their ideas in the related areas in order to promote and accelerate future development. This second volume discusses algorithms and applications, focusing mainly on the following topics: 3D printing technologies; naked, dynamic and auxiliary 3D displays; VR/AR/MR devices; VR camera technologies; microprocessors for 3D data processing; advanced 3D computing systems; 3D data-storage technologies; 3D data networks and technologies; 3D data intelligent processing; 3D data cryptography and security; 3D visual quality estimation and measurement; and 3D decision support and information systems.
Energy consumption and the associated costs constitute a crucial issue concerning the design and operation of data networks and data centers. Energy-awareness is required in all levels, ranging from physical layer to ...
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Energy consumption and the associated costs constitute a crucial issue concerning the design and operation of data networks and data centers. Energy-awareness is required in all levels, ranging from physical layer to algorithms, protocols and applications. Architecture-wise, a promising solution for tackling the increasing energy requirements is the deployment of optics at both long and shorter distances, including within data centers. Vertical Cavity Surface Emitting Lasers (VCSEL) constitute a popular photonic transmitter technology used in numerous short-range applications, providing also the ability to reduce energy consumption by scaling down the transmission bit rate. In this study we focus on the algorithmic aspects of energy management by proposing an OptiMal EnerGy Aware (OMEGA) routing algorithm to operate in optical networks utilizing VCSEL-based opto-electronic links. The algorithm leverages the capability of VCSELs to adapt the energy dissipation with respect to the transmission bit rate. Simulation results, under various traffic patterns, show that OMEGA balances efficiently the traffic load over the network's links, resulting in high throughput and low energy consumption.
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