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.

翻译：本文提出了一种新型可重构智能表面（RIS）辅助的上行用户中心无小区（UCCF）系统，该系统借助数字孪生（DT）进行管理。具体而言，我们提出了一种新颖的学习框架，通过联合优化接入点与用户关联（AUA）、功率控制及RIS波束赋形以最大化总速率。由于解空间极为庞大复杂，该问题极具挑战性且此前从未被解决。我们的框架基于变量性质的差异，将AUA与功率控制和RIS波束赋形（PCRB）解耦，从而缩减解空间。针对AUA问题，设计了一种新型位置自适应二进制粒子群优化（PABPSO）方法；针对PCRB问题，开发了两个采用新型精细化状态预处理层的双延迟深度确定性策略梯度（TD3）模型。另一个关键方面是，利用数字孪生通过回放存储的信道估计来训练该学习框架，仅在选定周期结束时在物理环境中测试AUA、功率控制及RIS波束赋形。仿真结果表明，引入RIS可显著提升UCCF系统的总速率，而数字孪生在性能损失极小的情况下大幅降低了系统开销。所提框架在总速率与收敛稳定性方面均优于现有替代方案。