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WACA WiFi 5-GHz dataset (Camp Nou only)
No full textThis folder contains ONLY the Camp Nou campaign included in the dataset presented in "Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)".
Please, refer to the GitHub repository for more details:
https://github.com/sergiobarra/WACA_WiFiAnalyzer
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*** Dataset structure ***
The dataset is composed of 11 scenarios, each with a corresponding folder (campaign duration within brackets):
- 1_RVA: Rice Village Apartments, Houston; Apartment; 15th February 2019; 9600 iterations (~1 day)
- 2_RNG: Rice Networks Group, Houston; Campus office; 19th February 2019; 8750 iterations (~1 day)
- 3_TFA: Technology for All, Houston; Community center; 20th February 2019; 8609 iterations (~1 day)
- 4_FLO: Flo Paris (at Rice Village), Houston; Cafe; 23rd February 2019; 413 iterations (~1 hour)
- 5_VIL: Rice Village parking lot, Houston; Shopping mall; 23rd February 2019; 125 iterations (~20 min)
- 6_FEL: La Sagrera neighborhood, Barcelona; Apartment; 25th March 2019; 59500 iterations (~1 week)
- 7_WNO: Wireless Networking, Barcelona; Campus office; 3rd April 2019; 9600 iterations (~1 day)
- 8_22A: 22@ neighborhood, Barcelona; Office area; 2nd July 2019; 9600 iterations (~1 day)
- 9_GAL: Hotel Gallery, Barcelona; Downtown hotel; 10th July 2019; 9706 iterations (~1 day)
- 10_SAG: Sagrada Familia, Barcelona; Apartment; 11th February 2019; 38192 iterations (~4 days)
- 11_FCB: Camp Nou stadium, Barcelona; Futbol (soccer) Stadium; 4th August; 2001 iterations (~ 5 hours)
*** File format ***
- RSSI measurements are stored in .mat files inside each scenario folder.
- .mat files are named "it__.mat". For instance file "it0251_02-19-19_08-03-09.mat" refers to iteration number 251, initiated on 19th February 2019 at 08:03:09.
- Every .mat file contains 24 arrays 10000x1, one per basic channel, where the ith element represents the RSSI value at sample i (of duration 10 microseconds) inside the iteration (of duration 1 second).
- The RSSI value is given in the units of the MAX2829 transceiver (10-bit values). Please, refer to the transceiver datasheet or to our GitHub repository to see how to convert from 10-bit values to dBm.
- Finally, every scenario folder also contains the "experiment_general.mat". This file is auxiliary and contains different constants of interest related to the configuration of the campaigns.
*** How do I process the dataset? ***
We provide the RSSI measurements for every channel in every scenario. You may generate a Matlab snippet to process the data. Nevertheless, the Matlab code we used in the paper "Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)" can be found in the GitHub repository
WACA WiFi 5-GHz dataset
No full textThis folder contains the dataset presented in "Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)".
Please, refer to the GitHub repository for more details:
https://github.com/sergiobarra/WACA_WiFiAnalyzer
-------------------------------------------------------------
*** Dataset structure ***
The dataset is composed of 11 scenarios, each with a corresponding folder (campaign duration within brackets):
1_RVA: Rice Village Apartments, Houston; Apartment; 15th February 2019; 9600 iterations (~1 day)
2_RNG: Rice Networks Group, Houston; Campus office; 19th February 2019; 8750 iterations (~1 day)
3_TFA: Technology for All, Houston; Community center; 20th February 2019; 8609 iterations (~1 day)
4_FLO: Flo Paris (at Rice Village), Houston; Cafe; 23rd February 2019; 413 iterations (~1 hour)
5_VIL: Rice Village parking lot, Houston; Shopping mall; 23rd February 2019; 125 iterations (~20 min)
6_FEL: La Sagrera neighborhood, Barcelona; Apartment; 25th March 2019; 59500 iterations (~1 week)
7_WNO: Wireless Networking, Barcelona; Campus office; 3rd April 2019; 9600 iterations (~1 day)
8_22A: 22@ neighborhood, Barcelona; Office area; 2nd July 2019; 9600 iterations (~1 day)
9_GAL: Hotel Gallery, Barcelona; Downtown hotel; 10th July 2019; 9706 iterations (~1 day)
10_SAG: Sagrada Familia, Barcelona; Apartment; 11th February 2019; 38192 iterations (~4 days)
11_FCB: Camp Nou stadium, Barcelona; Futbol (soccer) Stadium; 4th August; 2001 iterations (~ 5 hours)
*** File format ***
RSSI measurements are stored in .mat files inside each scenario folder.
.mat files are named "it__.mat". For instance file "it0251_02-19-19_08-03-09.mat" refers to iteration number 251, initiated on 19th February 2019 at 08:03:09.
Every .mat file contains 24 arrays 10000x1, one per basic channel, where the ith element represents the RSSI value at sample i (of duration 10 microseconds) inside the iteration (of duration 1 second).
The RSSI value is given in the units of the MAX2829 transceiver (10-bit values). Please, refer to the transceiver datasheet or to our GitHub repository to see how to convert from 10-bit values to dBm.
Finally, every scenario folder also contains the "experiment_general.mat". This file is auxiliary and contains different constants of interest related to the configuration of the campaigns.
*** How do I process the dataset? ***
We provide the RSSI measurements for every channel in every scenario. You may generate a Matlab snippet to process the data.
Nevertheless, the Matlab code we used in the paper "Sergio Barrachina-Muñoz, Boris Bellalta, and Edward Knightly. 2020. Wi-Fi All-Channel Analyzer. WinTech (2020)" can be found in the GitHub repository
VoIP Call Admission Control in WLANs in Presence of Elastic Traffic
Full text linkVoIP service over WLAN networks is a promising alternative to provide mobile voice communications. However, several performance problems appear due to i) heavy protocol overheads, ii) unfairness and asymmetry between the uplink and downlink flows, and iii) the coexistence with other traffic flows. This paper addresses the performance of VoIP communications with simultaneous presence of bidirectional TCP traffic, and shows how the presence of elastic flows drastically reduces the capacity of the system. To solve this limitation a simple solution is proposed using an adaptive Admission and Rate Control algorithm which tunes the BEB (Binary Exponential Backoff) parameters. Analytical results are obtained by using an IEEE 802.11e user centric queuing model based on a bulk service M=G[1;B]=1=K queue, which is able to capture the main dynamics of the EDCA-based traffic differentiation parameters (AIFS, BEB and TXOP). The results show that the improvement achieved by our scheme on the overall VoIP performance is significant
wn-upf/Komondor: 11axHDWLANsSim v1.0.1
No full text<p>Full code for reproducing the results gathered for the paper Barrachina-Munoz, Sergio, Francesc Wilhelmi, and Boris Bellalta. "Performance Analysis of Dynamic Channel Bonding in Spatially Distributed High Density WLANs."</p>
Stateless Reinforcement Learning for Multi-Agent Systems: the Case of Spectrum Allocation in Dynamic Channel Bonding WLANs
Full text linkSpectrum allocation in the form of primary channel and bandwidth selection is a key factor for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with varying environments, where networks change their configurations on their own, the wireless community is looking towards solutions aided by machine learning (ML), and especially reinforcement learning (RL) given its trial-and-error approach. However, strong assumptions are normally made to let complex RL models converge to near-optimal solutions. Our goal with this paper is two-fold: justify in a comprehensible way why RL should be the approach for wireless networks problems like decentralized spectrum allocation, and call into question whether the use of complex RL algorithms helps the quest of rapid learning in realistic scenarios. We derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of extensive or meaningless RL states.The work of Sergio Barrachina-Munoz and Boris-Bellalta was supported in part by Cisco, WINDMAL under Grant PGC2018099959-B-I00 (MCIU/AEI/FEDER,UE) and Grant SGR-2017-1188. Alessandro Chiumento is partially funded by the InSecTT project (https://www.insectt.eu/) which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 876038
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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