Search results for "distributed processing"

showing 5 items of 5 documents

Energy Efficiency Optimization for Multi-cell Massive MIMO : Centralized and Distributed Power Allocation Algorithms

2021

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices desi…

Signal Processing (eess.SP)FOS: Computer and information sciencesmallintaminenComputational complexity theoryComputer scienceenergiatehokkuusComputer Science - Information TheoryMIMO02 engineering and technologyPrecoding0203 mechanical engineeringoptimointistatistical CSIalgoritmit0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringOverhead (computing)Electrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal Processingenergy efficiencymax-min fairnessInformation Theory (cs.IT)020206 networking & telecommunications020302 automobile design & engineeringmulti-cell MIMOCovarianceDistributed algorithmChannel state informationConvex optimizationdistributed processingAlgorithm
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FDAE: A f̲ailure d̲etector for a̲synchronous e̲vents

2010

distributed processing fault tolerant computing system monitoring
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The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects

2013

The stability-plasticity dilemma is a well-know constraint for artificial and biological neural systems. The basic idea is that learning in a parallel and distributed system requires plasticity for the integration of new knowledge, but also stability in order to prevent the forgetting of previous knowledge. Too much plasticity will result in previously encoded data being constantly forgotten, whereas too much stability will impede the efficient coding of this data at the level of the synapses. However, for the most part, neural computation has addressed the problems related to excessive plasticity or excessive stability as two different fields in the literature.

Computer sciencelcsh:BF1-990Catastrophic Forgetting02 engineering and technologyPlasticity050105 experimental psychologyPsycholinguisticsLearning effectModels of neural computationConnectionismneural computation0202 electrical engineering electronic engineering information engineeringPsychology0501 psychology and cognitive sciencesGeneral PsychologyComputingMilieux_MISCELLANEOUSCognitive scienceForgettingPsycholinguisticsParallel Distributed Processingbusiness.industryAge of Acquisition05 social sciencesOpinion ArticleDilemmalcsh:Psychology[ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]020201 artificial intelligence & image processing[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Artificial intelligencebusinessCoding (social sciences)Frontiers in Psychology
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Modeling and Verification of Symbolic Distributed Applications Through an Intelligent Monitoring Agent

2022

Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniGeneral Computer ScienceGeneral EngineeringGeneral Materials ScienceElectrical and Electronic EngineeringDistributed applications Distributed processing Embedded Systems Fault detection Fault diagnosis Internet of Things Knowledge based systems Software maintenance Software monitoring Wireless sensor networksIEEE Access
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A Lightweight Network Discovery Algorithm for Resource-constrained IoT Devices

2019

Although quite simple, existing protocols for the IoT suffer from the inflexibility of centralized infrastructures and require several configuration stages. The implementation of these protocols is often prohibitive on resource-constrained devices. In this work, we propose a distributed lightweight implementation of network discovery for simple IoT devices. Our approach is based on the exchange of symbolic executable code among nodes. Based on this abstraction, we propose an algorithm that makes even IoT resource-constrained nodes able to construct the network topology graph incrementally and without any a priori information about device positioning and presence. The minimal set of executab…

Topology constructionSIMPLE (military communications protocol)Computer scienceExecutable code exchangeResource-constrained devicecomputer.file_formatConstruct (python library)Network topologyDistributed processingSet (abstract data type)Computer Networks and CommunicationHardware and ArchitectureA priori and a posterioriGraph (abstract data type)Symbolic processingExecutableInternet of ThingAlgorithmcomputerSoftwareAbstraction (linguistics)2019 International Conference on Computing, Networking and Communications (ICNC)
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