Reconfigurable Intelligent Surfaces for 5G and Beyond Applications

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While 5G technologies are in commercialization stages, research activities to shape the Beyond 5G (B5G) have been initiated in both academia and industry. The target of B5G wireless networks, such as the 6G, is providing more stringent requirements than 5G, such as ultra-high data rate and energy efficiency, extremely high reliability and low latency, and global coverage. Transition to mm-waves and using multi antennas at the base stations (BSs) and mobile users would be able to improve the energy and spectral efficiency in 5G. However, higher propagation losses at mm-wave restrict the development and applications of communication at these frequencies. Fortunately, the recently proposed Reconfigurable Intelligent Surfaces (RIS) can improve the capacity, coverage, and efficiency of wireless networks by intelligently engineering the wave propagation environment. Although the RISs showed promising results in simulations, there is no experimental confirmation of an RIS system due to implementation issues in their design, reconfigurability, and control of cells. In fact, achieving reconfigurability and intelligence in the large-scale surfaces is extremely difficult, as with the current state-of-the-art technology the RISs will involve bulky, complex, power-hungry, and expensive systems. Our proposed solution to address these challenges is to introduce new concept of integrated sensing, intelligence, and adaptability into the RIS structure to achieve the optimal performance in controlling the wireless medium and making a smart radio environment (SRE). In this program, three main functions of (a) Reconfigurable Metasurfaces as reflector or radiator; (b) Sensing and Energy Harvesting; and (c) Intelligent Beam Shaping will be created on the RISs to control the wireless mediums. These functions will be implemented through novel techniques of reconfiguring the properties of metasurfaces as a Reflector or Radiator RIS, electromagnetic fields measurement by novel concepts of self-powered near-field sensing, and non-linear optimization by using Deep Learning (DL) algorithms. The achievements on RIS with the above functionality will be utilized to develop a wireless Healthcare monitoring system. The Healthcare scenario is selected as the pilot system due to the importance and needs of a comprehensive remote health monitoring in the current pandemic and the subsequent ones. To get the best performance for all patient with various body factors, the DL algorithm in this system will be protected from the age, gender, and ethnicity biases by a widespread DL training with representors from all available groups. Potential impacts of the proposed program includes (a) Reshaping the concept of wireless network design; (b) Introducing huge industrial applications and commercialization capacities; and (c) Training at least seven (7) HQPs on theoretical, experimental, and industrial principles during the design, fabrication, evaluation, and system integration steps.