Communications
In situations when the precise position of a machine is unknown, localization becomes crucial. It is crucial to identify and ascertain the machine's position. This research focuses on improving the position prediction accuracy over long-range networks using a unique machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting approach using LoRa technology, this study suggested an ML-based algorithm.
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This paper investigates resource management in device-to-device (D2D) networks coexisting with mobile cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network’s energy needs, we formulate an online optimization problem.
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The dataset explores the linguistic characteristics of Ukrainian online community members on "Lviv. Forum Ridne City" (https://misto.ridne.net/) based on gender (female/male). It includes vectors of male and female profiles, along with 36 control vectors for 18 women's profiles and 18 men's profiles. The dataset includes 48 linguistic characteristics of gender in online communication. The linguistic features analyzed encompass a wide range, including apology, modal designs, emotions, profanity, sports and politics references, and more.
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This is the dataset used in the paper "Cross-phone calibration for smartphone-based crowdsourced measurement of E-field strength of mobile downlink signals using transfer learning". The dataset is mainly composed of RSRP and E-field strength data collected using smart phones and the Spectrum Analyzer with isotropic antenna. The file contains two subdirectories, one for the raw data after removing the outliers and the other for the preprocessed feature dataset. See the Readme file in the folder for details.
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The dataset is the experimental output of a 5G New Radio (NR) coverage expansion use case in the context of the NANCY project (https://nancy-project.eu/). Two experimental scenarios were carried out, namely a) a scenario where a user equipment (UE) is directly connected to a Base Station (BS) through a 5G NR link, and b) a scenario where an intermediate node is employed, which acts as a relay between the base station and the UE. To this end, two 5G BSs were deployed, using Ettus Research USRP B210 devices.
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This dataset serves loosely coupled hybrid scheduling of processing and communication for TSN-based IMA systems.
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It proposes a lossless and stateless compression method for IEC 61850 Sampled Values flows. A set of bitmaps is introduced to indicate the size in bytes of the sampled currents and voltages, and to flag the presence or absence of the Quality field. The method has been evaluated for different profiles, and it can provide bandwidth savings between 30 and 52%, with a very low computational cost as a counterpart. It has been implemented and tested in two different hardware platforms, with traffic generated by a Merging Unit.
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The dataset Provides S-parameter measurements of an AI enhanced wireless power transfer system using two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under three configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt. The structure and components of the system is shown in the Image attached to the Dataset This measured data validates the implementation of the system at the three coil configurations discussed in the publication.
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Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in network slicing (NetS) technology.This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS.
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