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International Workshop on Machine Learning for Wireless Communications
IN CONJUNCTION WITH THE TWENTY-FOURTH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS
The application of Machine Learning techniques and more specifically of Deep Learning techniques to Wireless Communication research problems holds great potential. Recent developments show that some problems in wireless systems can be dealt with effectively by using data-driven approaches.
Machine learning methods, and, more specifically, a set of recently developed techniques, known as Deep Learning bear the potential of advancing the intelligence of radio devices, providing data-driven flexible solutions, without relying heavily on expert knowledge. Among the problems that the Machine Learning can target are signal denoising, protocol detection, and classification; further applications might include device or user profiling and classification, source counting. In general, spectrum management can greatly benefit from the adoption of intelligent techniques that support the coexistence of heterogeneous radio access technologies.
This workshop aims at gathering experts of Machine Learning and of Wireless Communication to foster innovation and cross-seeding in this promising interdisciplinary area of research.
TOPICS OF INTEREST
The workshop solicits submissions of the unpublished works on topics including (but not limited to) the following:
Applications and emerging topics in Machine Learning for Communications:
ML for channel coding
ML for channel denoising
ML for protocol detection and classification
ML for capacity maximization
ML for dynamic spectrum access
ML Aided Resource Allocation
ML for physical layer issues, e.g., channel estimation, interference alignment, and coding
ML in network access and transmit control, e.g., channel allocation, power and rate control
ML for network coexistence, e.g. cognitive radio, device-to-device networks
ML in emerging networks, e.g. UAVs, VANET, etc. (e.g. for localization etc…)
ML in mobile edge computing, wireless caching, and mobile data offloading
ML for 5G Communication Networks
ML for Integrated Networking, Caching and Computing
ML for user behavior and demand prediction
ML for user localization and trajectory prediction
Use of Soft Computing/Computational Intelligence methods in Communications
Intelligent Energy-aware/Green Communications
Intelligent Software Defined Networks
ML methods and techniques for Communications:
Deep Learning/Neural Network methods and techniques for Communications
Generative Adversarial Networks for Communications
Use of Autoencoders in Communications
Transfer Learning, Adaptation methods and techniques in Communications
Online learning for real-time network operation
Federated Learning over the wireless edge
New data sets and ML challenges in wireless systems.
Big Data analytics and scalability issues for Communications
Dimensionality Reduction/Feature Selection in Learning for Communication
Structured Prediction for Communications
Supervised Learning for Communications
Unsupervised Learning for Communications
Semi-supervised Learning for Communications
Reinforcement Learning for Communications
Self-training and co-training for Communications
Multi-view Learning for Communications
Active Learning for Communications
Ensemble Methods for Communications
Kernel Methods for Communications
Hybrid ML and expert-driven approaches and methods for Communications
SUBMISSION
Authors are invited to submit full papers written in English, with a paper length up to six (6) printed pages for regular papers, or up to four (4) printed pages for short papers, including figures, tables & references in IEEE double-column format (IEEE standard conference templates). Submissions should contain original material and not be previously published, nor currently submitted for consideration elsewhere. All papers will be reviewed for scientific quality by the Technical Program Committee.
All papers should be submitted via EDAS using the EDAS system.
Paper submission implies the willingness of at least one author to register, at the regular rate (non-student), and present the paper. The Workshop Proceedings will be part of the ISCC 2019 Proceedings, they will be indexed SI, dblp and Scopus and will be submitted for inclusion in IEEE Xplore Digital Library.
We plan a journal special issue for which we will invite the best papers.
TECHNICAL PROGRAM CHAIRS
Antonio Manzalini, TelecomItalia, IT
Ernesto Damiani, Center for Cyber-Physical Systems, Khalifa University, Abu Dhabi, UAE
Nawaf Al Moosa, EBTIC, Khalifa University of Science and Technology, UAE
Gabriele Gianini, Università degli Studi di Milano, IT
Emanuele Bellini, CINI, Italian inter-university consortium for informatics, IT
ORGANIZATION CHAIR
Jianyi Lin, Khalifa University of Science and Technology, UAE
PUBLICITY/WEB CHAIR
Fulvio Frati, Università degli Studi di Milano, IT
PROGRAM COMMITTEE
Marco Anisetti, Università degli Studi di Milano, Italy
Stelvio Cimato, Università degli Studi di Milano, Italy
Noel Crespi, Institut Mines-Telecom, Telecom SudParis, FR
Haris Gačanin, Nokia Bell Labs/Antwerp, BE
Michael Granitzer, Universitat Passau, Germany
Pierre-Edouard Portier, INSA Lyon, France
Didier Puzenat, CNRS Lyon, France
Francesco Vatalaro, Università degli Studi di Roma Tor Vergata, IT
Francesco Zavatarelli, Università degli Studi di Milano, Italy
Erol Gelenbe, Imperial College, UK
Gwanggil Jeon, Incheon National University, South Korea
Jiankun Hu, University of New South Wales, Australia
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