SOURCE-LINKED INTELLIGENCE

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

CORDIS · observation · Publication date unknown

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 d

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recordType

award

status

CLOSED

region

EU

value

177412.96

unit

EUR