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10 results for “WLANs”
WLAN (WiFi) RSS database for fingerprinting positioning
<p>This data set contains two WLAN Received Signal Strengths (RSS) databases suitable for fingerprinting positioning. One database contains training data (Training_rss.csv, Training_coordinates.csv), the radio map, the second database contains test data (Test_rss.csv, Test_coordinates.csv), RSS measurements on a path and the coordinates of that path. The data was collected in a three-floor building at Tampere University of Technology.</p> <p>The files Training_rss.csv and Test_rss.csv represent a matrix, with a reference point per row and an access point per column. The radio map consists of 446 reference points and 489 access points. Empty RSS values are set to 100. The files Training_coordinates.csv and Test_coordinates.csv contain the reference positions, 3D coordinates in a metric local reference frame. The data format allows to use previously published software (https://doi.org/10.5281/zenodo.889797) to analyze the data.<br> <br> The data is postprocessed: The reference positions of each floor are mapped onto a regular grid with 5 meter grid point spacing and the RSS values at each reference position are spatial averages of the RSS values in the resulting cells. The test data is mapped as well, but to a grid of 1 meter grid point spacing, from which only every third value was selected.</p> <p> </p>
Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"
<p><a href="https://arxiv.org/pdf/1910.03510.pdf"><strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong></a></p> <p><strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.</p> <p><strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.</p> <p><strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). </p> <p>In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:</p> <ol> <li>Identifier of the AP to which the STA has been associated.</li> <li>RSSI obtained from the AP to which the STA has been associated.</li> <li>Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.</li> <li>Load in packets per second (pkt/s) that the STA generates.</li> <li>Percentage of data that the AP is able to serve before the user association is done.</li> <li>Amount of traffic load in pkt/s handled by the AP before the user association is done.</li> <li>Airtime in % that the AP enjoys before the user association is done.</li> <li>Throughput in pkt/s that the STA receives after the user association is done.</li> </ol> <p>The dataset has been generated through random simulations, based on the model provided in <a href="https://github.com/toniadame/WiFi_AP_Selection_Framework">https://github.com/toniadame/WiFi_AP_Selection_Framework</a>. More details regarding the dataset generation have been provided in <a href="https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans">https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans</a>.</p>
Self-contained 4-BSS's dataset of spectrum management in WLANs
<p>This folder contains the self-contained dataset of 4 BSS's analyzed in the thesis by <em>Sergio Barrachina-Muñoz, "Responsive Spectrum Management for Wireless Local Area Networks: from Heuristic-based Policies to Model-Free Reinforcement Learning", 2020</em>.</p> <p>-------------------------------------------------------------<br> <strong>*** General info ***</strong></p> <p>The dataset has been generated simulating all the spectrum management configurations (including primary channel and maximum bandwidth) in a 4-BSS's deploymment. Simulations have been performed with the Komondor wireless network simulator (<a href="https://github.com/wn-upf/Komondor">https://github.com/wn-upf/Komondor</a>).</p> <p><strong>*** Dataset structure ***</strong></p> <p>The dataset is composed of 1 file, dataset.csv, containing all the combinations of spectrum management configurations.</p> <p><strong>*** File format ***</strong></p> <p>The dataset.csv file is composed of 53 columns and 1679616 rows. <br> - Each column is a parameter or performance metric of the global spectrum management configuration, i.e., the configuration of all the BSS's.<br> - Each row is a realization of the global configuration.<br> - Column sim_code refers to the simulation code.</p> <p>The colums for each BSS are (only showing for BSS A):<br> - bss_A_code: code of the BSS<br> - action_ix_A: action (or BSS configuration) index<br> - status_ix_A: status index (combination of action and traffic load)<br> - primary_A: primary channel of the BSS<br> - max_bw_ix_A: index of the maximum allowed bandwidth of the BSS<br> - load_ix_A: traffic load index of the BSS<br> - load_A: traffic load [pkt/s] of the BSS<br> - thr_A: throughput [Mbps] of the BSS<br> - d_A: packet delay [ms] of the BSS<br> - rts_lost_A: number of RTS lost by BSS A<br> - rts_sent_A: number of RTS sent by BSS A<br> - frames_lost_A: number of frames lost by BSS A<br> - frames_sent_A: number of frames sent by BSS A</p>
Can a Wi-Fi WLAN Support a First Person Shooter?
<p>Jose Saldana, Juan Luis de la Cruz, Luis Sequeira, Julian Fernandez-Navajas, Jose Ruiz-Mas, "Can a Wi-Fi WLAN Support a First Person Shooter?," NetGames 2015, The 14th International Workshop on Network and Systems Support for Games Zagreb, Croatia, December 3-4, 2015.<br /> ISBN: 978-1-5090-0067-8</p> <p>This work has been partially ?nanced by the EU H2020 Wi-5 project (Grant Agreement no: 644262), and European Social Fund in collaboration with the Government of Aragon.</p> <p><br /> - The file "netgames_2015_in_proc.pdf" contains the paper published in the proceedings of the conference.</p> <p><br /> - Support files and results:</p> <p><br /> "captures" directory contains the ".pcap" captures made for both tests using Wireshark.</p> <p><br /> "files" directory contains the filtered data from the captures or from the D-ITG raw output. The ".log" files are binary. They have been obtained with D-ITG (Distributed Internet Traffic Generator, http://traffic.comics.unina.it/software/ITG/)</p> <p>A. Botta, A. Dainotti, A. Pescapè, "A tool for the generation of realistic network workload for emerging networking scenarios", Computer Networks (Elsevier), 2012, Volume 56, Issue 15, pp 3531-3547. </p> <p> </p> <p>"src" directory contains the ".m" matlab script and functions used for processing the raw data obtained. MATLAB R2013 compatible. It also includes the ".fig" files, to be opened with the same version of MATLAB.</p> <p>NOTE: It is MANDATORY adding the directories to the MATLAB path.</p> <p>Juan Luis de la Cruz, September 2015</p> <p> </p> <p><em>Abstract</em>—In corporate and commercial environments, the deployment of a set of coordinated Wi-Fi APs is becoming a common solution to provide Internet coverage to moving users. In these scenarios, real-time services as online games can also be present. This paper presents a set of experiments developed in a test scenario where an end device moves between different APs while generating game traffic. A WLAN solution based on virtual APs is used, in order to make the handoffs transparent for Layer 3. The results show that it is possible to maintain an acceptable level of subjective quality during the handoff. At the same time, it is set clear that the fact of having a gamer in an AP could be taken into account by radio resource management algorithms, in order to provide a better quality.</p>
A Broadband Fabry-Perot Cavity Antenna for WLAN and V2V Applications
<p><span>In this paper, a Fabry-Perot cavity (FPC) antenna with a partially reflective surface (PRS) consisting of two dielectric slabs with identical thickness and permittivity to increase the gain with wide bandwidth is presented. The PRS is placed in front of a broadband U-shaped microstrip patch antenna to create an air-filled cavity between the PRS and the ground plane of the antenna structure. The configuration of the two dielectric slabs aims to create a positive phase gradient of the reflection coefficient, which strongly controls the gain bandwidth performance. The proposed PRS was first designed and analyzed using a transmission line model and then verified by a full wave simulation. The measurement results show that the proposed FPC antenna achieves a gain improvement of up to 4 dB </span><span>compared to</span><span> the antenna without the PRS, with a 3-dB gain bandwidth of 15.25% and broadside peak gain of 10.43 dBi. In addition, the measured impedance bandwidth is approximately </span><span>20.25% and </span><span>ranges from </span><span>5.14 to 6.298 GHz, which covers the </span><span>required </span><span>f</span><span>requency band </span><span>of wireless local area network (WLAN) and vehicle-to-vehicle (</span><span>V2V) applications.</span></p>
Wireshark captures of a residential WLAN testbed
<p>This dataset contains the Wireshark captures obtained from the WLAN testbed used in the magazine paper entitled "Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks". More details regarding the experimental setup can be found at <a href="https://github.com/fwilhelmi/usage_of_simulators_in_future_networks">https://github.com/fwilhelmi/usage_of_simulators_in_future_networks</a>. </p>
giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag
<p>This repository holds the data that belongs to the publication:</p> <p>M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization", [arXiv:2207.02668]</p> <p>which is to appear at the 2022 IPIN conference. </p> <p>It is split into the following sub-parts:<br> - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)<br> - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)<br> - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))<br> - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc)</p> <p> </p> <p>For a detailed description, please refer to the given paper and the additional github repositories that host the implementations:</p> <p>- https://github.com/laskama/VI-SLAM2tag_post</p> <p>- https://github.com/laskama/VI-SLAM2tag_app</p> <p>- https://github.com/laskama/mCELindoorLoc</p>
Inputs and outputs of conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina and Boris Bellalta. The article has been sent to CSCN 2019.</p> <p>Regarding the input, we provide both the "input_node" and "input_system" files used by the Komondor simulator. In particular, up to 50,400 different scenarios are provided, which stand for 3 maps sizes 50 different random deployments (i.e., nodes allocation), 21 OBSS/PD values, and 16 traffic loads. More details are provided in the article. </p> <p>The output files collect the results gathered for all the scenarios. In addition, we include the code files used to "post-process" all the results.</p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Dataset of the journal article "Spatial Reuse in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "Spatial Reuse in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina, Cristina Cano, Ioannis Selinis and Boris Bellalta. The article has been sent to IEEE Surveys & Tutorials.</p> <p>Regarding the Komondor's input, we provide the files used in the Komondor simulator, as well as the execution scripts, for generating the results presented in the paper. We find "toy" and "random" scenarios, which cover different parts of the paper. In the random case, we have 39,800 different scenarios, which correspond to 4 network densities, 3 strategies on applying SR, 3 traffic loads, 50 deployments, and 21 different OBSS/PD values. More details are provided in the article. </p> <p>Apart from the Komondor's input, we also provide other Matlab files used in the context of the SFCTMN analytical model (refer to <a href="https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR">https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR</a>). </p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
[ITU-T AI Challenge] Input/Output of project "Improving the capacity of IEEE 802.11 WLANs through Machine Learning"
<p>This data set will be used by participants of the ITU-T AI Challenge. </p> <p>The data set contains:</p> <ul> <li>Input files: contain information such as nodes labels, nodes position, or channels used. These files have been used to simulate the behavior of random WLAN deployments under different channel bonding conditions. </li> <li>Output files: contain the output of the simulations - throughput per STA, RSSI that each STA receives from its AP, interference map from APs' point of view, average SINR experienced by each device during packet receptions.</li> </ul> <p>More details can be found on the official website of the challenge: <a href="https://www.upf.edu/web/wnrg/ai_challenge">https://www.upf.edu/web/wnrg/ai_challenge</a></p> <p><strong>[Update - 28 July 2020] </strong>A script (<a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/script_process_dataset.sh?versionId=2817eabd-ab9e-496a-a7d6-f67d227c51bf">script_process_dataset.sh</a>) has been added to process the output files. In particular, the results of each deployment are separated into different files. Besides, different files are created according to the type of label/feature (throughput, airtime, RSSI map, and interference list).</p> <p><strong>[Update - 22 September 2020] </strong>A new feature has been added to all the files in the data set. In particular, we have added the average Signal-to-Interference-plus-Noise Ration (SINR) experienced by each STA during packet receptions (including data and control packets). The SINR values in APs are marked as Inf because we focus on downlink transmissions only.</p> <p><strong>[Update - 30 September 2020] </strong>The test data set has been released, which corresponds to the simulations of a set of deployments with different characteristics. Input node files are contained in <a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/input_node_files_test.zip">input_node_files_test.zip</a>, while <a href="https://zenodo.org/api/files/88053224-d3a9-417e-b034-f08c763069ac/output_simulator_test.zip">output_simulator_test.zip</a> includes the output generated by the simulator. The label (i.e., the throughput) of the test data set will not be included in this repository until the next update (estimated date: 15 October 2020).</p> <p><strong>[Update - 19 October 2020] </strong>After participants have submitted their solutions, we provide the entire test data set, including the actual throughput obtained by each AP and STA in the test deployments.</p>
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