Machine Learning for Wireless Communication and Networks (ML4Wireless)

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.

Workshop Goals: This workshop aims to foster collaboration between ML researchers and wireless communication experts, encouraging cross-disciplinary innovation that will help shape the future of intelligent communication systems as well as more efficient and reliable AI models and techniques. Through a series of presentations, discussions, and interactive sessions, participants will explore both the theoretical foundations and practical applications of ML in wireless networks, with an eye toward addressing the most pressing challenges in this rapidly evolving field. On top of fostering collaborations and networking, we aim to boost research in machine learning and wireless communication topics by i) Hosting discussions about current wireless communication method limitations and how diverse ML models can empower communication systems by solving those challenges. ii) Encourage cross-collaboration between ML researchers and communication ones. iii) Giving space to younger researchers and PhD students to present their works and to get in contact with experts in this area, which is usually arduous in main conference tracks.

Why This Workshop at ICML? We know that artificial intelligence and machine learning models are driving technological transformations across numerous applications, with a particularly significant impact on wireless communication, given our daily reliance on smartphones and the emergence of connected intelligent devices, ranging from autonomous cars to mobile humanoids. Nonetheless, few ML researchers are actively contributing to wireless communication communities and venues, leaving researchers from this field alone in developing AI-powered methods and systems. On the other hand, ML researchers working on communication- potentially interesting topics like compression, quantization, inverse problems, or reliability sometimes lack real-world scenarios, datasets, or embedding systems to test their foundational research. We believe there is an unmet need to bridge the gap between the two research worlds. With this workshop, we aim to close this gap by fostering an active exchange and discussion between ML and communication researchers that can benefit both the research communities and establish a starting point for future collaborations and connections between the two worlds.