The 5G mobile communication era has just started, and we are already experiencing the dominance of various new applications with enhanced broadband connectivity requirements. These requirements will become even more critical with the integration of cellular networks in different sectors of society. Conventional sub-6 GHz-based cellular networks represent a short-term solution, where available spectral opportunities are limited and will unquestionably dry up soon. To this end, RISE-MM aims to set the ground for the THz spectrum-based cellular networks. Combining the researcher’s experience on reconfigurable intelligent surfaces (RIS)- enabled networks and the expertise on mmWave communication and its practical implementation of IMDEA Networks, in RISE-MM, we will develop channel models for RISE-MM communication in indoor and outdoor deployment settings. Moreover, the project aims to develop an algorithm for joint communication and sensing (JCAS) through RISE-MM using machine learning techniques.
RISE-MM aims to validate the proposed channel models and the algorithm using system-level simulations (SLS) and software-defined radios (SDR)-based mmWave experimentation platforms. It will also implement the proposed channel models using a large testbed with tens of 60 GHz off-the-shelf devices, which will provide a more realistic performance analysis for large-scale deployments to complement the SLS and SDR-based results. The practical deployment of RISE-MM will also help formulate the optimal RIS placement policy, which is a critical factor for RIS-enabled network planning.
RISE-MM is a unique scientific advance because it capitalises on communication theory, machine learning, and practical experimentation to propose new networking models to design and characterise RISE-MM communication for beyond 5G/6G cellular networks. In addition, the specifically developed JCAS algorithm can be the basis of novel developments for passive object detection and identification.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101061011.