Hi all,
Please consider contributing your work or demonstration proposals to the workshop at IEEE ICC if you are working at the intersection of communications systems and machine learning. Please see the CFP below for details.
-Nathan
Call for Papers:
IEEE ICC 2020 Open Workshop on Machine Learning in Communications
7-11 June 2020, Dublin, Ireland
** Submission deadline: January 27, 2020 **
https://icc2020.ieee-icc.org/workshop/ws-19-open-workshop-machine-learning-communications/===================
Call For Papers
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Machine Learning in Communications is a rapidly growing field within networking and communications with the potential to substantially transform wireless, optical, and other modes of networking and communications engineering in a wide range of future systems by leveraging measurement, data, feedback, domain knowledge, and learning to achieve optimality for a wide range of performance metrics. By directly exploiting real data, representation learning, end-to-end learning, reinforcement learning, concurrent neuromorphic processing, and a wide range of concepts which have advanced rapidly in the machine learning community in recent years, ML-Comms holds the promise to discover alternative and superior ways for information processing in practical application scenarios where deficient or inaccurate models limit present development. `
Openness and Reproducibility are two essential components in conducting rigorous machine learning driven research, and this workshop seeks to highlight and encourage both of these to help further increase the maturity of communications as a data science.
· Authors are strongly encouraged to utilize open tools such as pre-publication (e.g. ArXiv), providing reference code, data, simulations, GNU Radio modules, etc. openly (e.g. Github), and fully describe their methodologies in an open and re-producible way such that others can easily validate, leverage and build upon their work.
· For accepted workshop papers meeting such criterion, IEEE ComSoc is willing to provide as Open-Access publications at no additional fee to the authors!
MLC Dataset Challenge: In this year’s ICC 2020 Open Workshop, we are also excited to announce an open-dataset challenge focused on the unique task of Vision-Aided Beam Tracking for mmWave Systems. This challenge will adopt the recently developed ViWi dataset. Additional details of the competition and submission will be provided in early February 2020 via the workshop webpage:
https://icc2020.ieee-icc.org/workshop/ws-19-open-workshop-machine-learning-communications. The workshop expects to announce results at the ICC’2020 on a hold-out test set and invite competitors to share their approaches and experiences.
We invite submissions of unpublished works on the application and theory of machine learning to communications. The workshop shall not restrict the type of machine learning techniques and applications but does provide the following list is a non-exhaustive list of suggested topics.
· Machine learning driven design and optimization of modulation and coding schemes
· Machine learning techniques for channel estimation, channel modeling, and channel prediction.
· Machine learning based enhancements for difficult to model communications channels such as molecular, biological, multi-scale, and other non-traditional communications mediums
· Transceiver design and channel decoding using deep learning
· Machine learning driven techniques for radio environment awareness and decision making
· Machine learning for Internet of things (IoT) and massive connectivity.
· Machine learning for ultra-reliable and low latency communications (URLLC).
· Machine learning for Massive MIMO, active and passive Large Intelligent Surfaces (LIS).
· Distributed learning approaches for distributed communications problems
· (Deep) Reinforcement Learning and Policy learning for resource management & optimization
· Reinforcement Learning for self-organized networks and AP/BTS optimization
· Machine learning techniques for non-linear signal processing
· Low-complexity and approximate learning techniques and power reduction applications
· Machine learning for edge Intelligence, sensing platforms, and sense making
· Algorithmic advances in machine learning for communication systems
· Advancing the joint understanding of information theory, capacity, complexity and machine learning communications systems
· Machine learning methods for exploiting complex spatial, traffic, channel, traffic, power and other distributions more effectively using measurement vs idealized distributions.
· Compression of neural networks for low-complexity hardware implementation
· Unsupervised, semi-supervised and self-supervised learning approaches to communications
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Important Dates
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Paper submission deadline: January 27, 2020
Notification of acceptance: February 20, 202
Camera-ready papers: March 1, 2020
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Paper Submission
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The workshop accepts only novel, previously unpublished papers. The page length limit for all initial submissions for review is SIX (6) printed pages (10-point font) and must be written in English. Initial submissions longer than SIX (6) pages will be rejected without review. All final submissions of accepted papers must be written in English with a maximum paper length of six (6) printed pages (10-point font) including figures. No more than one (1) additional printed page (10-point font) may be included in final submissions and the extra page (the 7th page) will incur an over length page charge of USD100. For more information, please see IEEE ICC 2020 official website:
https://icc2020.ieee-icc.org/authors/call-workshop-papersEDAS submission link:
https://edas.info/newPaper.php?c=26827Please note we are also accepting submissions for live demonstrations and prototype systems in the ML-Comms area via abstract as well! Please see the full CFP pdf on the workshop webpage for additional details!
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Workshop Organizers
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· Tim O'Shea, DeepSig & Virginia Tech, US
· Elisabeth de Carvalho, Aalborg University, DK
· Jakob Hoydis, Nokia Bell Labs, FR
· Marios Kountouris, EURECOM, FR
· Zhi Ding, UC Davis, US
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MLC ViWi Dataset/Competition Organizers
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· Ahmed Alkhateeb, Arizona State University, US
· Muhammad Alrabeiah, Arizona State University, US