This paper investigates the problem of joint massive devices separation and channel estimation for a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme in the sixth-generation (6G) wireless networks. In particular, by associating the data sequences to a rank-one tensor and exploiting the angular sparsity of the channel, the detection problem is cast as a high-order coupled tensor decomposition problem. However, the coupling among multiple devices to RIS (device-RIS) channels together with their sparse structure make the problem intractable. By devising novel priors to incorporate problem structures, we design a novel probabilistic model to capture both the element-wise sparsity from the angular channel model and the low rank property due to the sporadic nature of URA. Based on the this probabilistic model, we develop a coupled tensor-based automatic detection (CTAD) algorithm under the framework of variational inference with fast convergence and low computational complexity. Moreover, the proposed algorithm can automatically learn the number of active devices and thus effectively avoid noise overfitting. Extensive simulation results confirm the effectiveness and improvements of the proposed URA algorithm in large-scale RIS regime.
翻译:本文调查了第六代(6G)无线网络中可重新配置智能表面(RIS)辅助的无源随机访问(URA)计划的联合大规模装置分离和频道估计问题,特别是将数据序列与一阶强相连接,利用该频道的角宽度,发现问题被描绘成一个高阶和高压分解问题,然而,多种装置与RIS(devi-RIS)频道的混合,加上其稀疏结构,使得问题难以解决。我们设计了新的前科,以纳入问题结构,我们设计了一个新的概率模型,以捕捉角通道模型中的元素偏移和由于URA的零星性质造成的低级属性。我们根据这一概率模型,在变异推框架以及快速趋同和低计算复杂性下,开发了一种同时以抗震动为基础的自动检测算法。此外,拟议的算法可以自动学习主动装置的数量,从而有效地避免噪音过大。广泛的IRA模拟结果证实了拟议的大规模系统系统分析法的有效性和改进。