RISE-MM · Reconfigurable Intelligent Surface-Enabled millimetre Wave Communication for Beyond 5G Cellular Networks

Reconfigurable Intelligent Surface-Enabled millimetre Wave Communication for Beyond 5G Cellular Networks

The 5G era has just begun, but demand for high-capacity, low-latency mobile communications is skyrocketing. Currently, 5G accounts for 10% of mobile data traffic (7 exabytes/month), and this is expected to exceed 50% by 2026 (126 exabytes/month). As applications like vehicle-to-vehicle and drone communication, augmented reality, and others emerge, data requirements will grow exponentially. Furthermore, the demand for ultra-reliable (99.999%) and low-latency (1 ms) communications poses significant challenges for traditional cellular networks. The sub-6 GHz spectrum used for 5G provides only a short-term solution, as available bandwidth will soon be exhausted. To meet future needs, the telecom industry is exploring the extremely-high-frequency (EHF) band, particularly millimeter wave (mmWave) communication, which offers vast spectrum resources. However, mmWave signals face significant path loss, susceptibility to physical blockages, and limited transmission range, making them unsuitable for broad cellular use in their current form. Reconfigurable intelligent surfaces (RIS) present a promising solution for enhancing mmWave communication. RISs are 2D surfaces with multiple scattering elements that reflect incoming signals by adjusting phase and amplitude, allowing for controllable, reconfigurable wireless environments. RIS-enabled mmWave (RISE-MM) systems enhance transmission range and capacity, offering a smoother integration with future 6G networks. Key challenges include accurate channel modeling and path loss estimation for different RISE-MM settings (far/near-field, indoor/outdoor). Equally important is optimizing RIS configurations—such as size, placement, and reflecting angles—to maximize data rates, coverage, and minimize interference. Moreover, RISs can enhance wireless sensing (localization and object detection), though mmWave's sensitivity to blockages poses challenges. Initial studies on RIS-aided localization exist, but algorithms for multipath localization remain undeveloped. Moreover, real experimentation and testbed-based evaluations are needed to validate RISE-MM models and assess their practicality in 5G/6G scenarios. This project focuses on three core objectives: 1- Channel Modeling and Estimation for RISE-MM: Revisiting path loss models for RISE-MM, accounting for RIS size, element states, and link geometry. 2- Joint Communication and Sensing (JCAS) through RISE-MM: Using machine learning (ML) and federated learning (FL) to optimize RISE-MM for joint communication and sensing. 3- System-Level Simulations and Real-Time Experiments: Leveraging simulators and real-world testbeds to validate RISE-MM models, ensuring feasibility for beyond 5G/6G deployments. This project aims to bridge theoretical, simulation and testbed-based, and experimental gaps to advance RISE-MM as a key enabler for future mobile networks.

Data: CORDIS, © European Union