This data set will be used by participants of the ITU-T AI Challenge. The data set contains: 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. Output files: contain the output of the simulations - throughput per STA, RSSI that each STA receives from its AP, and interference map from APs' point of view. More details can be found on the official website of the challenge: https://www.upf.edu/web/wnrg/ai_challeng
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
International audienceTo satisfy the increasing demand for wireless systems capacity, the industry i...
This data set will be used by participants of the ITU-T AI Challenge. The data set contains: In...
With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and st...
A Flexible Machine Learning-Aware Architecture for Future WLANs Authors: Francesc Wilhelmi, Sergio ...
This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (...
As wireless standards evolve, more complex functionalities are introduced to address the increasing ...
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in pr...
The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is pr...
As wireless standards evolve, more complex functionalities are introduced to address the increasing ...
Software-Defined Networking promises to deliver a more manageable network whose behaviour could be e...
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
International audienceTo satisfy the increasing demand for wireless systems capacity, the industry i...
This data set will be used by participants of the ITU-T AI Challenge. The data set contains: In...
With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and st...
A Flexible Machine Learning-Aware Architecture for Future WLANs Authors: Francesc Wilhelmi, Sergio ...
This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (...
As wireless standards evolve, more complex functionalities are introduced to address the increasing ...
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in pr...
The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is pr...
As wireless standards evolve, more complex functionalities are introduced to address the increasing ...
Software-Defined Networking promises to deliver a more manageable network whose behaviour could be e...
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
Driven by the emerging trend for transparent, open and programmable communications, Open Radio Acces...
International audienceTo satisfy the increasing demand for wireless systems capacity, the industry i...