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:本文提出了一个新的、可重新校正的智能表面(RIS), 借助数字双双(DT) 管理,以用户为中心的无细胞系统(UCCF) 。 具体地说,我们提议了一个新的学习框架,通过共同优化接入点和用户协会(AUA)、电力控制和RIS 光谱化,最大限度地提高总和率。 这个问题具有挑战性,而且从未解决过,因为其巨大的和复杂的解决方案空间令人无法接受。 我们的框架根据变数的不同性质,将AUAA同电控和RIS的变形(PCRB)系统(PCRB)脱钩,从而缩小了解决方案的空间。 为AUAA设计了新的适合位置的二进制粒粒粒子温优化(PABPSO)方法(PBSO),为AUAA设计了一个新的适应性二进制粒子优化(UBPBSO)方法。 为PCB开发了两个双延迟的深度确定性政策梯度梯度梯度模型(TD3),其中提出了新的和精细化的预处理层。 另一个重要方面是利用DTET框架来培训学习框架,以存储频道估算数据。 AUAUA、权力控制和升级的替代品的精度框架,在SAUDISAF的精度的精度框架的精度上,在SAFAF的精度上,其精度的精度的精度的精度的精度的精度的精度框架的精度的精度只能进行测试。