9 papers
Beam Scheduling for Cross-Layer ISAC: A Deep Reinforcement Learning Approach
Xiyu Wang, Gilberto Berardinelli, Hei Victor Cheng +2
Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering co…
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
Hamid Reza Hashempour, Gilberto Berardinelli, Shashi Raj Pandey +1
This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constra…
Distributed Learning for Reliable and Timely Communication in 6G Industrial Subnetworks
Samira Abdelrahman, Hossam Farag, Gilberto Berardinelli
Emerging 6G industrial networks envision autonomous in-X subnetworks to support efficient and cost-effective short range, localized connectivity for autonomous control operations.…
AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart Factories
Taofeek A. O. Yusuf, Sigurd S. Petersen, Puchu Li +6
In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key ena…
Learning Power Control Protocol for In-Factory 6G Subnetworks
Uyoata E. Uyoata, Gilberto Berardinelli, Ramoni Adeogun
In-X Subnetworks are envisioned to meet the stringent demands of short-range communication in diverse 6G use cases. In the context of In-Factory scenarios, effective power control…
Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication
Xiyu Wang, Gilberto Berardinelli, Hei Victor Cheng +2
Mobile users are prone to experience beam failure due to beam drifting in millimeter wave (mmWave) communications. Sensing can help alleviate beam drifting with timely beam changes…