Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Improving mmWave backhaul reliability: a machine-learning based approachPublication . Ferreira, Tânia; Figueiredo, Alexandre; Raposo, Duarte; Luís, Miguel; Rito, Pedro; Sargento, SusanaWiGig technologies, such as IEEE 802.11ad and later IEEE 802.11ay, provide multi-gigabit short-range communication at 60 GHz for bandwidth-intensive applications. However, this band suffers from high propagation losses that can only be compensated using highly directional antennas, making millimeter-wave (mmWave) links susceptible to blockage and errors. This high sensitivity to blockage leads to unstable and unreliable connections, since proprietary IEEE 802.11ad mechanisms, such as beamforming training, have high overhead, and can only be triggered when performance degradation is already detected, which compromises QoS and QoE even more.This article proposes a proactive machine learning framework that uses real-life data acquired in an outdoor setting to improve the reliability and resilience of a blockage-prone WiGig-based network. In particular, we propose a link quality classifier, which can differentiate between normal, long-term blockage and short-term operation with a test F1-score of 97%. Moreover, we introduce a novel deep learning forecasting model that can accurately capture the interactions between past multi-layer observations under different environments to produce accurate forecasts for 16 KPIs.
- On the real experimentation and simulation models for millimeter-wavePublication . Figueiredo, Alexandre; Ferreira, Tânia; Raposo, Duarte; Luís, Miguel; Rito, Pedro; Sargento, SusanaThe emergence of millimeter-wave based technologies is pushing the deployment of the 5th generation of mobile communications (5G), on the potential to achieve multi-gigabit and low-latency wireless links. Part of this breakthrough was only possible with the introduction of small antenna arrays, capable to form highly directional and electronically steerable beams. This strategy allowed the overcoming of some drawbacks, but with a higher price: the re-design of the lower layers by introducing beamforming techniques. The impact of these changes is not well studied on the higher layers, in the most recent stacks (IEEE 802.11ad, 3GPP). Thus, the study of real deployments and the use of accurate network simulators play a key role, by enabling the test of complex large-scale scenarios. This article presents a key component missing in the simulation of mmWave networks, a blockage model. To the best of our knowledge, this is first blockage model that emulates the effects of obstacles in the mmWave links. Additionally, a codebook generation of a phased antenna array, with the Quasi-Deterministic (Q-D) channel model is also presented. All models are tested and compared with an outdoor mmWave network using the IEEE 802.11ad standard. The simulated and the real-life tests show similar results, with an average error for the worst case of 2.43% (index ranges) and 4.51% (distance), and to an average standard deviation of about 1.33 dBm and 2.26 dBm.