C-WAN for FTTR: Enabling Low-Overhead Joint Transmission with Deep Learning

Abstract: 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 network (C-WAN) architecture inherently provides high-precision synchronization through fiber-based clock distribution and centralized scheduling, efficient JT still requires accurate CSI with low signaling cost. In this paper, we propose a deep learning-based hybrid model that synergistically 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.

Keywords: Fiber-to-the-Room (FTTR); Joint Transmission (JT); Centralized Wireless Access Network (C-WAN); Deep Learning; Channel State Information (CSI)