• Date: 26 June 2024
• Time: at 14:00 CEST Geneva | 08:00-09:00 EDT, New York | 20:00-21:00 CST, Beijing
• Duration: 1 hour (including 20 minutes networking)
• Programme stream: Discovery – AI/ML in 5G
• Topics: 6G

Radio resource management (RRM) involves selecting key transmission parameters—such as transmit power, frequency resources, precoder, and modulation. The complexity of this process is expected to increase significantly with the tenfold densification anticipated in 6G networks compared to 5G. The concept of in-X subnetworks has been introduced as a further leap of heterogeneous network, with the aim of providing highly localized wireless coverage for use cases such as in-robot, in-production module, in-vehicle, in-room communication. These subnetworks are expected to support diverse services, possibly extreme requirements in terms of ultra-short control cycle time, reliability, and service availability, surpassing the capabilities of 5G and its evolution. 

While the specific characteristics of in-X subnetworks can offer opportunities for efficient radio design, interference can be a major limiting factor in dense deployments. Subnetworks may be then characterized by such high density (e.g., vehicles in a congested road, robots in matrix production), and they can also be mobile, leading to rapid interference fluctuations. These aspects may result in wide and rapidly fluctuating interference patterns, which make the RRM problem more challenging than in traditional wireless setups, characterized by static base stations/access points and lower cell densities. 

The radio resource management problem is usually non-convex with NP-hardness, while traditional optimization methods and heuristics have shown efficacy in certain scenarios, these algorithms have exponential computational complexity and typically require many iterations. The dynamic and complex environment of In-X subnetworks, coupled with stringent low latency requirements, calls for more adaptive solutions with lower computational complexity. This necessity is driving the adoption of advanced artificial intelligence (AI) solutions. This challenge, supported by two EU SNS JU projects, 6G-SHINE and CENTRIC, focuses on developing machine learning-based solutions for optimizing sub-band allocation and power control within dense in-factory subnetworks. 

This live event includes a 20-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.