Machine Learning for Wireless Communication and Networks

As wireless communication systems evolve to meet the demands of a hyper-connected world, artificial intelligence models are emerging as the driving force behind a new wave of technological innovation. This workshop will explore how state-of-the-art artificial intelligence and machine learning (ML) methods are poised to redefine the core of wireless networks providing solutions to old and new communication challenges. One of the central themes is semantic communication, where ML enables wireless networks to understand and transmit the meaning behind data, rather than the whole bitstream, drastically improving efficiency in bandwidth-constrained environments and presenting novel scenarios and possible applications that were not even conceivable a couple of years ago. Additionally, the rise of generative and language models for wireless communication is bringing new ways to compress and enhance signal transmissions, impacting several downstream applications such as autonomous driving, video streaming, and virtual reality. Concurrently with widening the range of applications, these models also bring novel challenges related to large models' computational demands or to the regenerated content's controllability and reliability.

Central to bridging ML and wireless communication is the study of inverse problems, where generative models play a pivotal role in reconstructing lost or incomplete signals, and solving ill-posed tasks inherent in communication systems constrained by noisy and interference channels with limited bandwidth. The workshop aims also to explore key areas such as multimodal content compression, post-training quantization, efficient semantic feature extraction, and designing trustworthy models tailored for resource-constrained and noisy environments, in which foundational ML research finds crucial applications in communication scenarios.

• Distributed AI/ML over wireless networks

• Split, hybrid, and collaborative inference in resource-constrained environments

• Neural compression and transmission of multimedia and multimodal data

• Generative and agentic AI for autonomous and coordinated decision-makings

• Agentic AI communication protocols over wireless networks

•  AI/ML for and on Radio Access Network (RAN)

• Lightweight on-device neural network architectures for wireless communications

• Low-latency adaptation, quantization, and test-time compute for wireless communications

• Privacy, security, and trustworthy AI-enabled wireless services

• Explainability, reliability, and efficiency in semantic and token-based communications

• Theoretical foundations and performance bounds of AI-native wireless systems

• High-fidelity synthetic data and simulation frameworks for network intelligence

• Practical validations and testbeds for AI-native communication and networks

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Paper submission deadline Oct 26th, 2025

Author notification Nov 9th, 2025

ML4Wireless Workshop Jan 26th, 2026

The submission for ML4Wireless will be hosted on OpenReview.  

More detailed guidance can be found in the Call for Papers subpage of this site.