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Gaussian data-aided sensing with multichannel random access and model selection
In this article, we study data-aided sensing (DAS) for a system consisting of a base station (BS) and a number of nodes, where the BS becomes a receiver that collects measurements or data sets from the nodes that are distributed over a cell. DAS is an iterative data collection scheme that allows the BS to efficiently estimate a target signal (i.e., all nodes' measurements) with a small number of measurements (compared to random polling). In DAS, a set of nodes are selected in each round based on the data sets that are already available at the BS from previous rounds for efficient data collection. We consider DAS for measurements that are correlated with Gaussian in this article. The resulting DAS is referred to as Gaussian DAS. Using the mean-squared error (MSE) criterion, in each round, the BS is able to choose a node that has a data set to minimize the MSE of the next round. Furthermore, we generalize Gaussian DAS in two different ways: 1) with multiple parallel channels to upload measurements from nodes using random access and 2) with a model selection, where a multiarmed bandit problem formulation is used to combine the model selection with DAS.
History
Journal
IEEE internet of thingsVolume
7Issue
3Pagination
2412 - 2420Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
2327-4662eISSN
2327-4662Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
Science & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringSensorsCovariance matricesData modelsInternet of ThingsData collectionBase stationsIndexesIntelligent data collectionInternet of Things (IoT)model selectionmultiarmed banditDistributed Computing
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