发表机构
Carleton University; Dalhousie University; Lakehead University; Toronto Metropolitan University; Karunya Institute of Technology and Sciences（卡尔顿大学; 达尔豪斯大学; 湖首大学; 多伦多都会大学; 卡鲁尼亚科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对STAR-RIS在多普勒效应下适应慢的问题，提出带元素记忆与元参数的有状态RIS模型，实现分层适应，提升频谱效率与适应速度。
AI 中文摘要
在同时传输和反射可重构智能表面（STAR-RIS）的实时优化中，由于多普勒效应和适应速度降低，产生了局限性。现有的RIS无记忆表面模型采用准静态优化，导致高延迟，限制了其在认知自主网络中的应用。为克服这一局限，我们提出了一种有状态RIS模型，该模型包含逐元素记忆，可在相干间隔内局部存储并更新其相位历史。因此，超表面成为具有元素级记忆的有状态表面，能够利用时间信道相关性。这种逐元素记忆辅以元参数，可实现更快的收敛，并在梯度时间尺度和情节时间尺度上产生分层适应。所提出的框架支持自主和经验驱动的学习，使STAR-RIS架构和记忆能够根据多普勒效应进行适应，正如在认知通信系统中所见。结果表明，在各种移动模式、衰落模型、干扰水平和阵列尺寸下，频谱效率和适应速度均有所提升。分析性现场可编程门阵列（FPGA）延迟估算表明，关键路径落在相干窗口内。
英文摘要
In real-time optimization of simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR-RIS), limitations arise due to Doppler effects and reduced adaptation speed. Existing RIS memoryless surface models use quasi-static optimization and incur high latency, which limits their application in cognitive autonomous networks. To overcome this limitation, we propose a stateful RIS model that includes an element-wise memory, which locally stores and updates its phase history across coherence intervals. Hence, the metasurface becomes a stateful surface with element-level memory that enables the exploitation of temporal channel correlations. This element-wise memory is complemented with a meta-parameter, yielding faster convergence and giving rise to hierarchical adaptation in gradient and episodic timescales. The proposed framework supports autonomous and experience-driven learning, which enables the STAR-RIS architecture and memory to adapt according to the Doppler effect, as seen in cognitive communication systems. Results demonstrate an improvement in spectral efficiency and adaptation speed across various mobility patterns, fading models, interference levels, and array sizes. An analytical field- programmable gate array (FPGA) latency estimate indicates that the critical path fits within the coherence window.
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