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WMLC 2019 : Workshop on Machine Learning for Communications

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Link: http://www.wi-opt.org/wmlc.php
 
When Jun 7, 2019 - Jun 7, 2019
Where Avignon, France
Abstract Registration Due Feb 1, 2019
Submission Deadline Feb 8, 2019
Notification Due Mar 22, 2019
Final Version Due Apr 5, 2019
Categories    machine learning   computer networks   deep learning   wireless
 

Call For Papers

Call For Papers
Artificial Intelligence and particularly machine learning has lately been the focus of extensive research efforts. Recent breakthroughs have illustrated that AI can perform well in challenging tasks like image recognition and game playing, often surpassing human performance. The interplay between AI and wireless communications is twofold: On the one hand, encouraged by the good performance of AI in other domains, it is envisioned that AI can be used to optimize wireless systems without the need of an accurate model, just by relying on past data and observations. On the other hand, the computation power of interconnected devices can be leveraged to distribute the heavy computation load needed to apply AI (particularly the training of deep neural networks).
Although there has been a surge of interest in AI for wireless communication systems with encouraging preliminary results, its full potential and limitations are not yet well understood. Towards addressing this challenge, we solicit novel contributions on topics including, but not limited to, the following:
Deep learning for channel coding and transceiver design.
Neural networks for wireless communication (autoencoders, generative adversarialnetworks, etc.).
Deep learning for radio resource management.
Machine learning techniques and deep learning for congestion control and routing.
Reinforcement Learning and online learning for real-time network operation and selforganized networks.
Deep Learning for user behavior and demand prediction.
Deep Learning for user localization and trajectory prediction
Federated Learning over the wireless edge.
on-device machine learning.
Co-design of hardware and machine learning algorithms.
Experimental results on applications of AI in wireless systems.
New data sets and ML challenges in wireless systems.

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