The dataset includes parameters of RSN selection for inter-constellation satellite collaboration. It consists of four files. One includes the satellite topologies, from which we choose three topologies for our evaluations. The other three are parameters for each topology, including each satellite's request frequency, latency to other satellites, storage cost, processing capability, storage space, and reliability.

 

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This repository includes the DDPG, MADDPG, HHCDA, and MAHHCDA based on the paper "AI-Based and Mobility-aware Energy Efficient Resource Allocation and Trajectory Design for NFV enabled Aerial Networks".

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Supplementary data for "Investigation on Homogenization of Flat and Conformal Stacked Dielectric Resonator Antennas"

Instructions: 

Supplementary data for "Investigation on Homogenization of Flat and Conformal Stacked Dielectric Resonator Antennas"

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The simulation code for the paper:

"AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning"

 

The overall architecture of the proposed MARL framework is shown in the figure.

 

Modified MADDPG: This algorithm trains two critics (different from legacy MADDPG) with the following functionalities:

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2D geometrically shaped constellations that are simultaneously robust to both residual phase noise (RPN) and AWGN (named as LCM-RPN, where LC is the low-complexity receiver metric from [1]) for 8 to 64-ary PCAWGN reception using a mismatched PCAWGN model. We added AWGN-only shaped constellations (LCM-AWGN) to serve as a reference; the term M is the modulation cardinality.

Instructions: 

Each modulation order is placed in a separate folder, in which, every text file has the coordinates for the in-phase and quadrature components of each symbol in the first and the second column, respectively. The bit mapping for each symbol is natural mapping for the line number, i.e., 000 001 010 011 100 etc.

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This Matlab model and the included results are submitted as reference for the paper ''. 

Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Least-squares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets. 

The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration. 

 

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This Data contains measurements on reading and writing data to OPC UA servers directly and via REST and GraphQL interfaces. Each measurement is conducted 50 times. Measurements include reading a single value and reading a value 50 times. Measurements are conducted with Wireshark and the csv files are exported from it.

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Kinaesthetic data collected by Chai3D

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This file contains the MAB algorithms for millimeter wave (mmWave) beamforming training (BT) in Indoor environment for both single beam and concurrent beams scenarios. The algorithms were developed using MATLAB software and they are making use of the following data set

 

 

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This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

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