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C-WAN for FTTR:Enabling Low-Overhead Joint Transmission with Deep Learning 认领 引用
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作者 ZHANG Yang CEN Zihan +1 位作者 ZHAN Wen CHEN Xiang 《ZTE Communications》 2025年第4期65-76,共12页
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
关键词 Fiber-to-the-Room(FTTR) Joint Transmission(JT) centralized wireless access network(C-WAN) deep learning Channel State Information(CSI)
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