Reconfigurable Intelligent Surfaces for 5G and Beyond Applications

虽然5G技术正处于商业化阶段，但学术界和产业界都已启动了塑造Beyond 5G(B5G)的研究活动。6G等B5G无线网络的目标是提供比5G更严格的要求，如超高数据速率和能效、极高可靠性和低时延、全球覆盖。过渡到毫米波并在基站(BS)和移动用户使用多天线将能够提高5G的能量和频谱效率。然而，毫米波较高的传播损耗限制了这些频率下通信的发展和应用。幸运的是，最近提出的可重构智能表面(RIS)可以通过智能地设计波传播环境来提高无线网络的容量、覆盖范围和效率。尽管RISS在模拟中显示了有希望的结果，但由于RIS系统在设计、可重构性和单元控制方面的实现问题，目前还没有实验证实RIS系统。事实上，在大规模表面上实现可重构性和智能化是极其困难的，因为以目前最先进的技术，RISS将涉及笨重、复杂、耗电和昂贵的系统。为了应对这些挑战，我们提出的解决方案是在RIS结构中引入集成感知、智能和自适应的新概念，以实现控制无线介质和构建智能无线电环境(SRE)的最佳性能。在该计划中，将在RISS上创建三个主要功能：(A)作为反射器或辐射器的可重构变形表面；(B)传感和能量收集；以及(C)智能波束整形，以控制无线介质。这些功能将通过将超表面的属性重新配置为反射器或辐射器RIS的新技术来实现，通过自供电近场传感的新概念进行电磁场测量，以及通过使用深度学习(DL)算法进行非线性优化。具有上述功能的RIS上的成果将被用于开发无线医疗监护系统。选择医疗保健方案作为试点系统，是因为在当前和以后的大流行病中进行全面远程健康监测的重要性和需要。为了让所有具有各种身体因素的患者获得最佳性能，该系统中的DL算法将通过与来自所有可用群体的代表进行广泛的DL培训来保护，使其不受年龄、性别和种族偏见的影响。拟议计划的潜在影响包括：(A)重塑无线网络设计的概念；(B)引入巨大的工业应用和商业化能力；以及(C)在设计、制造、评估和系统集成步骤中，对至少七(7)名总部人员进行理论、实验和工业原理方面的培训。

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.