Contemporary communications are characterized by higher network density and heterogeneity.
Developing a communication system to address these challenges and the requirement to serve
users with varying demands in terms of data rates, reliability, latency, and mobility patterns
results in high design complexity. However, recent advances in artificial intelligence (AI) and
machine learning (ML) techniques facilitate improved network design, operation, and
maintenance by leveraging the high volume of network data generated by dense networks. On
the other hand, the requirement to embed wireless networks with the AI necessitates their
optimization for enabling efficient learning over wireless networks. Thereby, potentially requiring
a joint design of AI and wireless.
This workshop focuses on theoretical aspects of AI/ML in wireless networks, data-driven
optimization algorithms, ML-based intelligent network automation, and optimization of
autonomous and self-organizing mobile wireless networks. As part of the main conference, the
symposium titled Machine Learning for Communications focuses particularly on the design of
wireless networks using machine learning. In contrast, this proposal also focuses on the design
of wireless networks for AI, and the design of AI-based techniques for operating and maintaining
wireless networks. The aim is to bring together academic researchers and industry practitioners
to brainstorm on relevant challenges, recent advances in AI/ML, and the capabilities of emerging
solutions.Topics of interest for this workshop include but are not limited to the following:
- Deep and Reinforcement Learning for Wireless Communications
- Machine learning for automated anomaly detection systems
- Network architecture and protocol design for AI-enabled 6G
- Communication and energy efficiency in distributed intelligent systems
- Cross-layer design for distributed machine learning (ML)
- Communication and signal processing for wireless edge intelligence
- Data analytics for large-scale network performance analysis
- Data-driven intelligent radio resource management algorithms
- Artificial intelligence-enabled zero-touch network and service management
- Multi-agent reinforcement learning in wireless networks
- Privacy and security aspects of decentralized ML over wireless networks
- Robustness in distributed sensing, learning and inference
- Fundamental limits of decentralized ML
- Data-driven green communications and computing
- Wireless designs for federated learning
- Graph machine learning for large-scale wireless networks
- Other ML frameworks including deep unfolding, transfer learning, meta-learning, etc.
IMPORTANT DATES
Deadline for workshop paper submission: July 15, 2023
July 29, 2023 (Extended)
Aug. 12, 2023 (Final)
Acceptance/rejection announcement: September 15, 2023
Final workshop papers due: October 1, 2023
PAPER SUBMISSION: EDAS LINK
