深入探讨光纤到房间(Fiber to the Room,FTTR)技术在智慧家庭和智慧办公中的应用及其系统架构。FTTR利用光纤通信技术和无源光网络(Passive Optical Network,PON)架构,通过光线路终端(Optical Line Terminal,OLT)、光分配网络(Optical D...深入探讨光纤到房间(Fiber to the Room,FTTR)技术在智慧家庭和智慧办公中的应用及其系统架构。FTTR利用光纤通信技术和无源光网络(Passive Optical Network,PON)架构,通过光线路终端(Optical Line Terminal,OLT)、光分配网络(Optical Distribution Network,ODN)、光网络终端(Optical Network Terminal,ONT)组件提供高速稳定的网络服务。研究内容涵盖FTTR的技术原理、系统架构设计,并详细分析其在智慧家庭与智慧办公中的应用。研究表明,FTTR不仅显著提升用户体验,而且为企业和个人提供更加高效、智能和安全的工作生活环境。展开更多
Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of ...Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of FTTR networks presents significant chal⁃lenges in identifying sources of network performance degradation and conducting accurate root cause analysis.Conventional approaches often fail to deliver efficient and precise operational improvements.To address this issue,this paper proposes a Transformer-based multi-task learn⁃ing model designed for automated root cause analysis in FTTR environments.The model integrates multidimensional time-series data col⁃lected from access points(APs),enabling the simultaneous detection of APs experiencing performance degradation and the classification of underlying root causes,such as weak signal coverage,network congestion,and signal interference.To facilitate model training and evaluation,a multi-label dataset is generated using a discrete-event simulation platform implemented in MATLAB.Experimental results demonstrate that the proposed Transformer-based multi-task learning model achieves a root cause classification accuracy of 96.75%,significantly outperform⁃ing baseline models including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Random Forest,and eXtreme Gradient Boost⁃ing(XGBoost).This approach enables the rapid identification of performance degradation causes in FTTR networks,offering actionable in⁃sights for network optimization,reduced operational costs,and enhanced user experience.展开更多
光纤到房间(Fiber to the Room,FTTR)技术依托其大带宽、低时延和高可靠性的特点成为广电网络升级的核心方向。与此同时,Wi-Fi感知技术通过分析无线信号特征,实现了对用户行为和家庭环境的低成本监测。本文结合广电网络特点,探讨FTTR与W...光纤到房间(Fiber to the Room,FTTR)技术依托其大带宽、低时延和高可靠性的特点成为广电网络升级的核心方向。与此同时,Wi-Fi感知技术通过分析无线信号特征,实现了对用户行为和家庭环境的低成本监测。本文结合广电网络特点,探讨FTTR与Wi-Fi感知技术融合的优势、应用场景、面临的挑战以及未来展望。展开更多
The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home...The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.展开更多
Fiber-to-the-Room(FTTR)networks with multi-access point(AP)coordination face significant challenges in implementing Joint Transmission(JT),particularly the high overhead of Channel State Information(CSI)acquisition.Wh...Fiber-to-the-Room(FTTR)networks with multi-access point(AP)coordination face significant challenges in implementing Joint Transmission(JT),particularly the high overhead of Channel State Information(CSI)acquisition.While the centralized wireless access net⁃work(C-WAN)architecture inherently provides high-precision synchronization through fiber-based clock distribution and centralized sched⁃uling,efficient JT still requires accurate CSI with low signaling cost.In this paper,we propose a deep learning-based hybrid model that syner⁃gistically integrates temporal prediction and spatial reconstruction to exploit spatiotemporal correlations in indoor channels.By leveraging the centralized data and computational capability of the C-WAN architecture,the model reduces sounding frequency and the number of antennas required per sounding instance.Experimental results on a real-world synchronized channel dataset show that the proposed method lowers over-the-air resource consumption while maintaining JT performance close to that achieved with ideal CSI,offering a practical low-overhead solution for high-performance FTTR systems.展开更多
文摘深入探讨光纤到房间(Fiber to the Room,FTTR)技术在智慧家庭和智慧办公中的应用及其系统架构。FTTR利用光纤通信技术和无源光网络(Passive Optical Network,PON)架构,通过光线路终端(Optical Line Terminal,OLT)、光分配网络(Optical Distribution Network,ODN)、光网络终端(Optical Network Terminal,ONT)组件提供高速稳定的网络服务。研究内容涵盖FTTR的技术原理、系统架构设计,并详细分析其在智慧家庭与智慧办公中的应用。研究表明,FTTR不仅显著提升用户体验,而且为企业和个人提供更加高效、智能和安全的工作生活环境。
基金supported in part by the National Key R&D Program of China under Grant No.2024YFE0200504NSFC key international joint project under Grant No.62120106007Interdisciplinary Research Program of HUST under Grant No.2024JCYJ022.
文摘Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of FTTR networks presents significant chal⁃lenges in identifying sources of network performance degradation and conducting accurate root cause analysis.Conventional approaches often fail to deliver efficient and precise operational improvements.To address this issue,this paper proposes a Transformer-based multi-task learn⁃ing model designed for automated root cause analysis in FTTR environments.The model integrates multidimensional time-series data col⁃lected from access points(APs),enabling the simultaneous detection of APs experiencing performance degradation and the classification of underlying root causes,such as weak signal coverage,network congestion,and signal interference.To facilitate model training and evaluation,a multi-label dataset is generated using a discrete-event simulation platform implemented in MATLAB.Experimental results demonstrate that the proposed Transformer-based multi-task learning model achieves a root cause classification accuracy of 96.75%,significantly outperform⁃ing baseline models including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Random Forest,and eXtreme Gradient Boost⁃ing(XGBoost).This approach enables the rapid identification of performance degradation causes in FTTR networks,offering actionable in⁃sights for network optimization,reduced operational costs,and enhanced user experience.
文摘光纤到房间(Fiber to the Room,FTTR)技术依托其大带宽、低时延和高可靠性的特点成为广电网络升级的核心方向。与此同时,Wi-Fi感知技术通过分析无线信号特征,实现了对用户行为和家庭环境的低成本监测。本文结合广电网络特点,探讨FTTR与Wi-Fi感知技术融合的优势、应用场景、面临的挑战以及未来展望。
文摘The convergence of optical and wireless technologies is driving the evolution of intelligent indoor networks,with Fiber-to-the-Room(FTTR)emerging as a key ar⁃chitecture for delivering gigabit connectivity in both home and enterprise environments.By deploying optical fiber directly to rooms and integrating it with advanced wireless so⁃lutions such as millimeter-wave and Wi-Fi 7,FTTR enables next-generation applications,including immersive Virtual Re⁃ality(VR)/Augmented Reality(AR)and industrial Internet of Things(IoT).Nevertheless,its large-scale deployment pres⁃ents challenges in network management,energy efficiency,in⁃terference mitigation,and intelligent root cause analysis.
文摘Fiber-to-the-Room(FTTR)networks with multi-access point(AP)coordination face significant challenges in implementing Joint Transmission(JT),particularly the high overhead of Channel State Information(CSI)acquisition.While the centralized wireless access net⁃work(C-WAN)architecture inherently provides high-precision synchronization through fiber-based clock distribution and centralized sched⁃uling,efficient JT still requires accurate CSI with low signaling cost.In this paper,we propose a deep learning-based hybrid model that syner⁃gistically integrates temporal prediction and spatial reconstruction to exploit spatiotemporal correlations in indoor channels.By leveraging the centralized data and computational capability of the C-WAN architecture,the model reduces sounding frequency and the number of antennas required per sounding instance.Experimental results on a real-world synchronized channel dataset show that the proposed method lowers over-the-air resource consumption while maintaining JT performance close to that achieved with ideal CSI,offering a practical low-overhead solution for high-performance FTTR systems.
