Efficient Data-Enabled Learning Based NextG Wireless System Optimization with Experimental Validation

Aim:  

Next generation (NextG) wireless systems, such as 6G and beyond, demand intelligent, agile, and data-efficient optimization methods to satisfy the increasing performance, energy efficiency, and adaptability requirements. This workshop aims to bring together researchers and practitioners from academia, industry, and government to explore the intersection of Artificial Intelligence (AI), Machine Learning (ML), optimization, and experimental testbeds for wireless systems. Special focus will be given to data-efficient learning techniques, online and adaptive algorithms, and experimental validation using real-world testbeds.

We will discuss topics including but not limited to reinforcement learning for dynamic resource allocation, distributed federated learning for edge computing optimization, data-driven channel modeling, real-time spectrum management, RIS  hardware testbed development, and physical layer optimization. The workshop will emphasize hands-on experimental insights and practical deployment considerations, fostering collaboration toward robust, efficient, and reproducible NextG wireless communication system design.

Scope and Topics:

This special session will provide a forum to deliver and discuss original research results and new techniques in efficient data-enabled learning based NextG wireless system optimization with experimental validation. We are particularly interested in the following topics:

• Data-efficient learning algorithms for wireless system optimization

•  Federated and distributed learning in wireless networks

• Reinforcement learning for dynamic resource allocation

• Meta-learning and transfer learning for wireless system optimization

• End-to-end optimization of wireless networks using ML

• RIS hardware development and physical measurements

• Real-world wireless testbeds and experimental frameworks (e.g., POWDER, COSMOS, Colosseum, etc.)

• Reconfigurable Intelligent Surface assisted NextG wireless system design

• Cross-layer optimization for NextG wireless systems with experimental validation

Pape Submission:

• Please follow IEEE CAMAD 2025 Submission Website:https://camad2025.ieee-camad.org/call-papers 

• Deadline: August 5th, 2025

• Acceptance Notification: August 15th, 2025

Name of Organizers

• Lijun Qian, Prairie View A&M University, TX, USA

• Abdullah Eroglu, SUNY Polytechnic Institute, NY, USA

• Binbin Yang, North Carolina A&T University, NC, USA

• Hao Xu, University of Nevada, NV, USA