Embracing Diffraction: A Paradigm Shift in Wireless Sensing and Communication
원문 초록
Wireless signals are integral to modern society, enabling both communication and increasingly, environmental sensing. While various propagation models exist, ranging from empirical methods to full-wave simulations, the phenomenon of electromagnetic diffraction is often treated as a secondary effect or a correction factor. This paper positions diffraction as a fundamentally important and underutilized mechanism that is rich with information about the physical environment. Specifically, diffraction-inducing elements generate distinct signatures that are rich with information about their underlying properties such as their geometries. We then argue that by understanding and exploiting these relationships, diffraction can be harnessed strategically. We introduce a general optimization framework to formalize this concept, illustrating how diffraction can be leveraged for both inverse problems (sensing scene details such as object geometries from measured fields) and forward problems (shaping RF fields for communication objectives by configuring diffracting elements). Focusing primarily on edge diffraction and Keller's Geometrical Theory of Diffraction (GTD), we discuss specific applications in RF sensing for scene understanding and in communications for RF field programming, drawing upon recent work. Overall, this paper lays out a vision for systematically incorporating diffraction into the design and operation of future wireless systems, paving the way for enhanced sensing capabilities and more robust communication strategies.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.