Communications

The Internet Graphs (IGraphs) dataset is a substantial collection of real intra-AS (Autonomous System) graphs sourced from the Internet Topology Data Kit (ITDK) project. Comprising a total of 90,326 graphs, each ranging from 12 to 250 nodes, this dataset provides a diverse and extensive resource for the exploration and analysis of network structures within autonomous systems.

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The data set was used to produce three figures (Figs. 4, 5, and 6) in the experimental section of a manuscript submitted for review to the IEEE Communications Magazine. The submission, titled Lightweight Determinism in Large-Scale Networks, describes a novel approach to the realization of network determinism in packet-switched networks of generic size and topology.

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This dataset corresponds to the near field (NF) measurements of a 70 cm diameter offset reflector antenna, fed with a circularly polarized helix antenna, working at 4650 MHz [1,2].

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Location awareness is vital in next generation (xG) wireless networks to enable different use cases, including location-based services (LBSs) and efficient network management. However, achieving the service level requirements specified by the 3rd Generation Partnership Project (3GPP) is challenging.

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The data repository includes simulation parameters and results for the Quantum Approximate Optimization Algorithm (QAOA) channel decoding applied to short block-lengths in Additive White Gaussian Noise (AWGN) channels. This research utilizes a Random Linear Code (RLC) in a Coded Modulation (CM) design at the encoder. Extensive simulations were conducted using IBM Quantum Lab and the ibmq_qasm_simulator back-end provided by IBM Quantum, simulating quantum circuits on classical hardware.

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The Transport-level pAcket RouTing ANalysis Tool for Cloud-native Applications (TARTAN) Dataset contains TARTAN/HiPerConTracer Traceroute runs between an endpoint in Oslo, Norway and the public Comprehensive TeX Archive Network (CTAN, https://www.ctan.org) and Comprehensive R Archive Network (CRAN, https://cran.r-project.org) mirror we

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In this dataset, 4 radio channel measurements at 28 GHz are presented. These have been captured in two different spots of a highway with a fixed Tx and a mobile Rx onboard a car, emulating a V2I link. The Tx was placed both on the roadside (3 measurement runs) and on an overpass (1 measurement). Each run consists of around 4.29 s of continuously captured channel impulse responses (CIRs), with a channel sampling period of 65 μs and a delay resolution of 2 ns.

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The rapid evolution of communication networks and the ever-increasing demand for efficient data transfer have led to the development of cognitive networking, which aims to enhance network performance through intelligent and adaptive protocols. To facilitate research and development in this domain, we present a comprehensive dataset detailing the parameters of a Network Protocol Stack which can be used to develop a Cognitive Network Protocol Stack designed for efficient networking.

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The dataset contains 5G positioning measurements simulated using a MATLAB raytracer tool in realistic environments (outdoors and indoors).
Outdoor scenarios include static and dynamic users in the urban area of Città Studi, Milan, Italy, near the Politecnico di Milano - Campus Leonardo.
The indoor context is reconstructed using a LiDAR acquisition in the MADE Competence Center I4.0 located in Politecnico di Milano - Campus Durandò, Bovisa, Milan, Italy.
The datasets include:

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The datasets consists of the traffic-delay pairs collected in a real network.  For each link connecting two switches, we measured the link delay associated with 1000 evenly-divided traffic test points. To obtain the traffic-delay pairs at a traffic test point of a link, the traffic generation machine, connected to the starting switch of the link, firstly sent data packets to the traffic generation machine, connected to the ending switch of the link, at the corresponding rate (i.e., the traffic value of the  traffic test point).

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