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RECON 2023 : The 1st edition of RECON - Reinforcement Learning for Communication and Network Optimization workshop

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When Oct 30, 2023 - Nov 3, 2023
Where Boston, MA, USA
Submission Deadline Sep 4, 2023
Categories    reinforcement learning   tactical networks   machine learning
 

Call For Papers

The 1st edition of RECON - Reinforcement Learning for Communication and Network Optimization workshop, co-located with the IEEE Military Communications Conference (MILCOM 2023)

30 October – 3 November 2023 // Boston, MA, USA https://milcom2023.milcom.org.

Scope and topics

RECON focuses on the application of Reinforcement Learning (RL) to tactical and civilian communication networks. RECON aims to cover a wide range of topics within this domain, and we encourage researchers to submit their work on various subjects including, but not limited to, network planning, network optimization, adaptive protocols, decentralized network management, novel learning algorithms and frameworks, agent coordination architectures, datasets and testbeds, and practical challenges associated with deploying RL and Machine Learning (ML) in communication networks.


For more information and submission details, please visit the MILCOM 2023 website at https://milcom2023.milcom.org.

Topics

• General topics:
– (Central / Distributed) Network planning
– Network optimization (combinatorial and learned)
– RL, MARL, Deep Learning techniques
– Ad-hoc adaptive protocols
– Practical challenges for deploying ML solutions into real-networks
– Distributed Learning paradigms (Distributed learning architecture, Federated Learning)
– Deep Learning architectures and solutions suitable to process networking information/scenarios (GNNs, Transformers,
Autoencoders)
• Single and Multi-Agent Reinforcement Learning
– Communication protocol optimization
– Decentralized network management
– Data Management
– Agent(s) coordination architectures suitable to DIL networks
– Learning to Communicate
– Self-driving networks
– Custom RL training environments
– Cross-Layer routing
– Exploration techniques
• Machine Learning for Network Analysis
– Network state prediction and estimation
– ML for configuration parameters identification
– Traffic analysis
– Intrusion detection
– Anomaly detection
– ML at the network edge (Edge ML)
• Testsbeds and Datasets
– Testbeds and architectures
– Datasets
– Generation of mobility patterns/scenarios
– Data validation techniques
– Datasets for Offline RL

Organizing Committee

Raffaele Galliera, University of West Florida, Florida, USA, rgalliera@ihmc.org
Thies Mohlenhof, Fraunhofer-Gesellschaft FKIE, Germany, thies.moehlenhof@fkie.fraunhofer.de
Filippo Poltronieri, University of Ferrara, Italy, filippo.poltronieri@unife.it

Important Dates

• Submission Deadline: 4 September 2023
• Notification of Acceptance: 18 September 2023


Submission instructions

All submissions should be written in English with draft papers up to six (6) printed pages in length, two-column, single-spaced with 10-point font on US Letter paper. Final papers that exceed six (6) pages will be assessed a per-page over-length charge of $150/page, up to a maximum of eight (8) total pages. Please check the manuscript requirements for further details.
Submission link: https://edas.info/N31310

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