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 a 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.


Conference Program now available!


Topics of interest

The workshop solicit submissions of the unpublished works on topics including (but not limited to) the following: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 an 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


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 paper submissions must be done through EDAS using the following link.

Papers must adopt the IEEE double column proceedings (US Letter page) format.

Manuscript templates for IEEE conference proceedings can be found at the following link

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 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