This paper puts forth a new, reconfigurable intelligent surface
(RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the
assistance of a digital twin (DT). Specifically, we propose a novel learning
framework that maximizes the sum-rate by jointly optimizing the access point
and user association (AUA), power control, and RIS beamforming. This problem is
challenging and has never been addressed due to its prohibitively large and
complex solution space. Our framework decouples the AUA from the power control
and RIS beamforming (PCRB) based on the different natures of their variables,
hence reducing the solution space. A new position-adaptive binary particle
swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed
deep deterministic policy gradient (TD3) models with new and refined state
pre-processing layers are developed for the PCRB. Another important aspect is
that a DT is leveraged to train the learning framework with its replay of
channel estimates stored. The AUA, power control, and RIS beamforming are only
tested in the physical environment at the end of selected epochs. Simulations
show that using RISs contributes to considerable increases in the sum-rate of
UCCF systems, and the DT dramatically reduces overhead with marginal
performance loss. The proposed framework is superior to its alternatives in
terms of sum-rate and convergence stability.

提出了一种新的可重构智能表面（RIS）辅助、用户为中心的无线接入系统，通过数字孪生管理。该文档设计了一个新的学习框架，通过联合优化接入点和用户关联、功率控制和 RIS 波束成形来最大化总和速率，以提高 UCCF 系统的性能，此框架优于其他方法。