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</o:shapelayout></xml><![endif]--></head><body lang=IT link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span lang=EN-US>*** MLWCOM 2019 ***<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>[Apologies if you receive multiple copies of this CfP]<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>******************************************************************************<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>International Workshop on Machine Learning for Wireless Communications (MLWCOM 2019)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>in conjunction with the 24th IEEE Symposium on Computers and Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>June 29th - July 3rd, 2019 – Barcelona, Spain<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>http://sesar.di.unimi.it/call/mlwcom-2019/<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>******************************************************************************<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>TOPICS OF INTEREST<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>The workshop solicit submissions of the unpublished works on topics including (but not limited to) the following:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Applications and emerging topics in Machine Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for channel coding<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for channel denoising<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for protocol detection and classification<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for capacity maximization<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for dynamic spectrum access<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML Aided Resource Allocation<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for physical layer issues, e.g., channel estimation, interference alignment, and coding<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML in network access and transmit control, e.g., channel allocation, power and rate control<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for network coexistence, e.g. cognitive radio, device-to-device networks<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML in emerging networks, e.g. UAVs, VANET, etc. (e.g. for localization etc…)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML in mobile edge computing, wireless caching, and mobile data offloading<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for 5G Communication Networks<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for Integrated Networking, Caching and Computing<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for user behavior and demand prediction<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- ML for user localization and trajectory prediction<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Use of Soft Computing/Computational Intelligence methods in Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Intelligent Energy-aware/Green Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Intelligent Software Defined Networks<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>ML methods and techniques for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Deep Learning/Neural Network methods and techniques for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Generative Adversarial Networks for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Use of Autoencoders in Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Transfer Learning, Adaptation methods and techniques in Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Online learning for real-time network operation<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Federated Learning over the wireless edge<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- New data sets and ML challenges in wireless systems.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Big Data analytics an scalability issues for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Dimensionality Reduction/Feature Selection in Learning for Communication<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Structured Prediction for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Supervised Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Unsupervised Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Semi-supervised Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Reinforcement Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Self-training and co-training for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Multi-view Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Active Learning for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Ensemble Methods for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Kernel Methods for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Hybrid ML and expert driven approaches and methods for Communications<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>DEADLINE<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Submission deadline: April 7,2019<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Notification of paper acceptance: April 28,2019<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Submission of camera-ready papers: May 5,2019<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>SUBMISSION<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>All papers should be submitted via EDAS using the EDAS system.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>We plan a journal special issue for which we will invite the best papers.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal>TECHNICAL PROGRAM CHAIRS<o:p></o:p></p><p class=MsoNormal>- Antonio Manzalini, TelecomItalia, IT<o:p></o:p></p><p class=MsoNormal><span lang=EN-US>- Ernesto Damiani, Center for Cyber-Physical Systems, Khalifa University, Abu Dhabi, UAE<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Nawaf Al Moosa, EBTIC, Khalifa University of Science and Technology, UAE<o:p></o:p></span></p><p class=MsoNormal>- Gabriele Gianini, Università degli Studi di Milano, IT<o:p></o:p></p><p class=MsoNormal><span lang=EN-US>- Emanuele Bellini, CINI, Italian Inter-university Consortium for Informatics, IT<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>ORGANIZATION CHAIR<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Jianyi Lin, Khalifa University of Science and Technology, UAE<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal>PUBLICITY/WEB CHAIR<o:p></o:p></p><p class=MsoNormal>- Fulvio Frati, Università degli Studi di Milano, IT<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>PROGRAM COMMITTEE<o:p></o:p></p><p class=MsoNormal>- Marco Anisetti, Università degli Studi di Milano, Italy<o:p></o:p></p><p class=MsoNormal>- Stelvio Cimato, Università degli Studi di Milano, Italy<o:p></o:p></p><p class=MsoNormal><span lang=EN-US>- Noel Crespi, Institut Mines-Telecom, Telecom SudParis, FR<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Haris Gačanin, Nokia Bell Labs/Antwerp, BE<o:p></o:p></span></p><p class=MsoNormal>- Michael Granitzer, Universitat Passau, Germany<o:p></o:p></p><p class=MsoNormal>- Pierre-Edouard Portier, INSA Lyon, France<o:p></o:p></p><p class=MsoNormal>- Didier Puzenat, CNRS Lyon, France<o:p></o:p></p><p class=MsoNormal>- Francesco Vatalaro, Università degli Studi di Roma Tor Vergata, IT<o:p></o:p></p><p class=MsoNormal>- Francesco Zavatarelli, Università degli Studi di Milano, Italy<o:p></o:p></p><p class=MsoNormal><span lang=EN-US>- Erol Gelenbe, Imperial College, UK<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Gwanggil Jeon, Incheon National University, South Korea<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>- Jiankun Hu, University of New South Wales, Australia<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p></div>
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