deploying Reconfigurable Intelligent Surfaces for Interference Reduction

部署可重构智能表面以减少干扰

• 批准号: EP/Y023374/1
• 负责人: Haonan Hu
• 金额: $ 31.94万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY023374%2F1
• 关键词: deploying; Reconfigurable; Intelligent; Surfaces; Interference

A 1,000-fold mobile data traffic growth was predicted from 2020 to 2030, with more than 80% of that happening indoors. Emerging indoor applications of the sixth generation of mobile communication (6G) place higher requirements on 6G indoor network capacity. Reconfigurable intelligent surface (RIS), one of the promising technologies identified for 6G, has the potential to address the 6G indoor network capacity requirement. RIS has attracted a lot of interests with a focus on coverage extension. However, the Fellow believes that enhancing network capacity by reducing interference is the most promising use case for RIS. In order to realise the full potential of RIS for interference reduction, the following challenges need to be addressed urgently. (1) How to identify interference paths for typical indoor environments to guide RIS deployment for interference reduction? (2) How to optimally place RISs (e.g., number and location) to absorb interference? (3) What is the upper bound of network capacity for a wireless network involving RISs? and (4) Which indoor scenarios will benefit from RIS deployment from a life-cycle point of view? To address the above challenges, the Fellow has defined the following research and innovation (R&I) objectives: (1) To model interference paths and distributions in typical indoor scenarios; (2) To find optimal deployment of RISs to enhance the indoor wireless network capacity; (3) To obtain the capacity upper bounds for networks involving RISs; and (4) To quantify the life-cycle benefits of RIS deployment for typical indoor scenarios. Achieving the above objectives will reveal the topology of interference paths in indoor environments so that to provide candidate locations for RIS deployment, develop an optimisation framework and associated algorithms for RIS deployment, quantify the capacity enhancement, and identify the most promising indoor scenarios for RIS deployment.

预计从2020年到2030年，移动的数据流量将增长1,000倍，其中80%以上发生在室内。新兴的第六代移动的通信（6 G）室内应用对6 G室内网络容量提出了更高的要求。可重构智能表面（RIS）是为6 G确定的有前途的技术之一，具有解决6 G室内网络容量需求的潜力。RIS吸引了很多关注，重点是覆盖范围的扩展。然而，研究员认为，通过减少干扰来增强网络容量是RIS最有前途的用例。为了充分发挥RIS减少干扰的潜力，迫切需要解决以下挑战。(1)如何识别典型室内环境的干扰路径，以指导RIS部署以减少干扰？(2)如何以最佳方式放置RIS（例如，数量和位置）来吸收干扰？(3)涉及RIS的无线网络的网络容量上限是多少？以及（4）从生命周期的角度来看，哪些室内场景将受益于RIS部署？针对上述挑战，研究员确定了以下研究和创新（R&I）目标：（1）模拟典型室内场景中的干扰路径和分布;（2）找到RISs的最佳部署，以提高室内无线网络容量;（3）获得包含RISs的网络的容量上限;（4）获得RISs的网络容量上限。以及（4）量化RIS部署在典型室内场景下的生命周期效益。实现上述目标将揭示室内环境中干扰路径的拓扑结构，以便为RIS部署提供候选位置，为RIS部署开发优化框架和相关算法，量化容量增强，并确定最有前途的RIS部署室内场景。