BD-RIS通过量子增强实现6G波束成形,提升通信效率与灵活性。
Quantum Intelligence Meets BD-RIS-Enabled AmBC: Challenges, Opportunities, and Practical Insights
- 提出基于量子-经典混合模型的波束成形算法,优化信号控制。
- 在真实场景下验证,相比传统方法提升3.2%的总速率,计算成本更低。
- 适合研究6G智能表面与量子机器学习融合的学者参考。
超对角可重构智能表面(BD-RIS)是一种新型RIS,通过简化单元间连接实现更灵活的幅度与相位调控,具有显著优势。然而其实际应用面临诸多挑战,尤其在特定环境下的被动波束成形设计成为研究重点。本文系统介绍BD-RIS的架构原理、分类及其潜在优势,并梳理最新进展与关键挑战。进一步,以DeepSense 6G数据集中的真实通信场景8为基准,对比四种算法的波束成形性能,结果表明所提方案在总速率上提升3.2%,同时降低计算开销。为探索6G BD-RIS中量子增强潜力,引入多种混合量子-经典机器学习模型,有效提升波束预测精度,揭示其在实际部署中的可行性与前景。
原文摘要 · Abstract (English)
A beyond-diagonal reconfigurable intelligent surface (BD-RIS) is an innovative type of reconfigurable intelligent surface (RIS) that has recently been proposed and is considered a revolutionary advancement in wave manipulation. Unlike the mutually disconnected arrangement of elements in traditional RISs, BD-RIS creates cost-effective and simple inter-element connections, allowing for greater freedom in configuring the amplitude and phase of impinging waves. However, there are numerous underlying challenges in realizing the advantages associated with BD-RIS, prompting the research community to actively investigate cutting-edge schemes and algorithms in this direction. Particularly, the passive beamforming design for BD-RIS under specific environmental conditions has become a major focus in this research area. In this article, we provide a systematic introduction to BD-RIS, elaborating on its functional principles concerning architectural design, promising advantages, and classification. Subsequently, we present recent advances and identify a series of challenges and opportunities. Additionally, we consider a specific case study where beamforming is designed using four different algorithms, and we analyze their performance with respect to sum rate and computation cost. To augment the beamforming capabilities in 6G BD-RIS with quantum enhancement, we analyze various hybrid quantum-classical machine learning (ML) models to improve beam prediction performance, employing real-world communication Scenario 8 from the DeepSense 6G dataset. Consequently, we derive useful insights about the practical implications of BD-RIS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。