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，移动数据流量将增长 1,000 倍，其中 80% 以上发生在室内。第六代移动通信（6G）新兴的室内应用对6G室内网络容量提出了更高的要求。可重构智能表面 (RIS) 是 6G 确定的有前景的技术之一，有潜力满足 6G 室内网络容量需求。 RIS 吸引了很多人的兴趣，重点关注覆盖范围的扩展。然而，该研究员认为，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 在减少干扰方面的潜力，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，指导RIS部署减少干扰？ (2)如何优化放置RIS（例如数量和位置）以吸收干扰？ (3) 涉及RIS 的无线网络的网络容量上限是多少？ (4) 从生命周期的角度来看，哪些室内场景将受益于 RIS 部署？为了应对上述挑战，院士定义了以下研究和创新（R＆I）目标：（1）对典型室内场景中的干扰路径和分布进行建模； (2)寻找RIS的最佳部署，以增强室内无线网络容量； (3) 获取涉及RIS的网络的容量上限； (4) 量化典型室内场景的 RIS 部署的生命周期效益。实现上述目标将揭示室内环境中干扰路径的拓扑结构，从而为RIS部署提供候选位置，开发RIS部署的优化框架和相关算法，量化容量增强，并确定最有希望的RIS部署室内场景。