We invite original and unpublished papers, which must be formatted using the standard IEEE two-column format adopted by the IEEE INFOCOM 2027 main conference and must not exceed six pages in length, including references. Unlike the double-blind review policy adopted by the main conference, the workshop follows a single-blind review policy. All submissions will undergo a rigorous peer-review process. Accepted papers that are presented by at least one author at the workshop will be published in the IEEE INFOCOM 2027 workshop proceedings and IEEE Xplore.
Paper submission site: to be provided soon
Submission Deadline: December 29, 2026
Notification of Acceptance: February 2, 2027
Camera Ready: February 16, 2027
Workshop: May 24, 2027
Deep learning has reshaped the design of wireless systems, moving data-driven methods from research prototypes toward the core of next-generation wireless networks including both 6G and Wi-Fi 9. In this context, pressing questions arise about how learning-based solutions can be made reliable, efficient, explainable, and secure in real-world deployments. Now in its fifth edition, the DeepWireless Workshop brings together researchers and practitioners from academia and industry to present and discuss fundamental advances and practically relevant solutions at the intersection of deep learning and wireless communications, sensing, and security. The workshop also aims to bridge research and practice by offering industry fresh insights into the development and deployment of deep learning in wireless networks, and by highlighting the open challenges, datasets, and testbeds that can accelerate progress in the field.
In line with such objectives, original contributions, for both technical and demo sessions, are solicited on topics of interest to include, but not limited to, the following:
Deep Learning for signal detection, channel coding, and end-to-end transceiver design
Deep Learning for channel modelling, estimation, and prediction
Deep Learning for resource allocation and network optimization
Deep Learning for network traffic analysis, classification, and prediction
Deep learning for semantic communications
Deep learning for digital twins
Deep learning for UAV and satellite networks
Deep learning for intelligent reflecting surfaces
Deep learning for new Internet of Things applications
AI-native air interfaces, AI-RAN and Open RAN intelligence
Deep learning for wireless sensing using Wi-Fi, mmWave radar, UWB, LoRa, RFID, and other modalities
Deep learning for localization, positioning, and tracking
Deep learning for integrated sensing and communications (ISAC)
Signal and spectrum intelligence, including modulation recognition, technology classification, and spectrum sensing
Deep learning for physical layer security and authentication
Deep learning for radio frequency fingerprint identification
Adversarial attacks and defenses for learning-based wireless communications, sensing and security
Explainable and trustworthy AI for wireless communications, sensing, and security
Large language models and foundation models for wireless communications, sensing and security
Generative AI for wireless
World models and physical AI for wireless
Quantum machine learning for wireless
Agentic AI for autonomous network operation, sensing, and security
Cross-domain, cross-modal and transfer learning for domain adaptation
Edge AI, model compression, and energy-efficient learning on resource-constrained devices for wireless communications, sensing, and security
Datasets, benchmarks, experimental testbeds, and real-world deployment studies
Professor Shiwen Mao, Auburn University, USA, smao@auburn.edu
Professor Yingying Chen, Rutgers University, USA, yingche@scarletmail.rutgers.edu
Professor Carlo Fischione, KTH Royal Institute of Technology, Sweden, carlofi@kth.se
Professor Jie Xu, The Chinese University of Hong Kong, Shenzhen, China. xujie@cuhk.edu.cn
Dr. Junqing Zhang, University of Liverpool, UK, junqing.zhang@liverpool.ac.uk
Dr. Xuyu Wang, Florida International University, USA, xuywang@fiu.edu
Dr. Francesca Meneghello, , Northeastern University, USA. fr.meneghello@northeastern.edu
To be updated
To be updated