Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: Overview, design, and challenges

L Kong, MK Khan, F Wu, G Chen… - IEEE Communications …, 2017 - ieeexplore.ieee.org
Autonomous vehicles are a rising technology in the near future to provide a safe and
efficient transportation experience. Vehicular communication systems are indispensable …

A flexible machine-learning-aware architecture for future WLANs

F Wilhelmi, S Barrachina-Muñoz… - IEEE …, 2020 - ieeexplore.ieee.org
Lots of hopes have been placed on machine learning (ML) as a key enabler of future
wireless networks. By taking advantage of large volumes of data, ML is expected to deal with …

Federated mmWave beam selection utilizing LIDAR data

MB Mashhadi, M Jankowski, TY Tung… - IEEE Wireless …, 2021 - ieeexplore.ieee.org
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial
yet challenging task due to the overhead imposed by beam selection. For vehicle-to …

5G MIMO data for machine learning: Application to beam-selection using deep learning

A Klautau, P Batista, N González-Prelcic… - 2018 Information …, 2018 - ieeexplore.ieee.org
The increasing complexity of configuring cellular networks suggests that machine learning
(ML) can effectively improve 5G technologies. Deep learning has proven successful in ML …

Lumos5G: Mapping and predicting commercial mmWave 5G throughput

A Narayanan, E Ramadan, R Mehta, X Hu… - Proceedings of the …, 2020 - dl.acm.org
The emerging 5G services offer numerous new opportunities for networked applications. In
this study, we seek to answer two key questions: i) is the throughput of mmWave 5G …

Usage of network simulators in machine-learning-assisted 5G/6G networks

F Wilhelmi, M Carrascosa, C Cano… - IEEE Wireless …, 2021 - ieeexplore.ieee.org
Without any doubt, Machine Learning (ML) will be an important driver of future
communications due to its foreseen performance when applied to complex problems …

Beam alignment for millimetre wave links with motion prediction of autonomous vehicles

I Mavromatis, A Tassi, RJ Piechocki, A Nix - 2017 - IET
Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-
gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can …

[图书][B] White Paper on Machine Learning in 6G Wireless Communication Networks: 6G Research Visions, No. 7, 2020

A Samad, W Saad, R Nandana, C Kapseok… - 2020 - diva-portal.org
This white paper discusses various topics, advances, and projections regarding machine
learning (ML) in wireless communications. Sixth generation (6G) wireless communications …

Deep learning coordinated beamforming for highly-mobile millimeter wave systems

A Alkhateeb, S Alex, P Varkey, Y Li, Q Qu… - IEEE …, 2018 - ieeexplore.ieee.org
Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of
important applications, such as vehicular communications and wireless virtual/augmented …

Design and implementation for deep learning based adjustable beamforming training for millimeter wave communication systems

LH Shen, TW Chang, KT Feng… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Millimeter wave (mmWave) provides extremely high throughput owing to their high
bandwidth utilization over higher frequencies. To compensate for the severe loss and …