SNOB-5G Data

This repository contains data acquired in a real mmWave Wigig-compliant network which supported our experimental work entitled "Improving mmWave backhaul reliability: A machine-learning based approach". The network is composed of three Metnet 60G nodes deployed in an outdoor environment following the architecture shown in the following figure.

The multi-layer data includes information for all mmWave links for different modulation and coding schemes (MCS) modes: (i) constant mode (1, 3,242 5, 7 and 9); (ii) dynamic adjustment according to the channel variations. Moreover, it includes information regarding the behavior of the multi-layer metrics under three distinct scenarios, according to the type and duration of the blockage event crossing the LOS path. These scenarios can be described as follows:

• Normal scenario: metrics were collected during 15 minutes in a non-obstructed propagation environment
• Long-term blocked scenario: metrics were collected during 15 minutes under continuous LOS blockage caused by the static metallic obstacle shown in Figure above
• Short-term blocked scenario: metrics were collected during 5 minutes in the presence of human blockages that cause temporary LOS blockage during short periods of time

Authors

• Tânia Ferreira - Instituto de Telecomunicações (IT)
• Alexandre Figueiredo - Instituto de Telecomunicações (IT)
• Duarte Raposo - Instituto de Telecomunicações (IT)
• Pedro Rito - Instituto de Telecomunicações (IT)
• Miguel Luís - Instituto de Telecomunicações (IT) and Instituto Superior de Engenharia Lisboa (ISEL)
• Susana Sargento - Instituto de Telecomunicações (IT) and Universidade de Aveiro (UA)

Funding

This work is supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework and FCT under the MIT Portugal Program [Project SNOB-5G with Nr. 045929 (CENTRO-01-0247-FEDER-045929)].

Cite this dataset

[1] Tânia Ferreira, Alexandre Figueiredo, Duarte Raposo, Miguel Luís, Pedro Rito, Susana Sargento, Improving mmWave backhaul reliability: A machine-learning based approach, Ad Hoc Networks, 2022, 103050, ISSN 1570-8705, https://doi.org/10.1016/j.adhoc.2022.103050. (https://www.sciencedirect.com/science/article/pii/S1570870522002220)

Contact

Any questions are welcome.

Tânia Ferreira [email protected]