发表机构
Advanced Wireless Technologies Lab, Fourier Research Center, Huawei Technologies; University of Rennes, INSA Rennes, CNRS, IETR UMR 6164; Université de Lille, CNRS, IEMN UMR 8520（华为技术有限公司先进无线技术实验室傅里叶研究中心; 雷恩大学，雷恩国立应用科学学院，法国国家科学研究中心，IETR UMR 6164; 里尔大学，法国国家科学研究中心，IEMN UMR 8520）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无源随机接入中的张量调制，提出离散变分贝叶斯CPD框架DVB-ALS，利用离散混合先验和结构化高斯先验联合估计因子，集成完整接收器，在高负载下性能优于现有方案。
AI 中文摘要
基于张量的调制（TBM）方案是无源随机接入（URA）的一种有前景的方法，其中用户分离依赖于通过典型多路分解（CPD）对接收信号张量进行分解，通常使用交替最小二乘（ALS）计算。然而，标准ALS将因子矩阵视为无结构，未能利用张量子星座的离散结构。我们提出DVB-ALS，一种具有特定先验的离散变分贝叶斯CPD框架，针对相应的编码策略的张量结构量身定制，并结合近似后验分布的迭代计算。一个Grassmannian因子上的离散高斯混合先验将估计软对齐到星座点。其余因子用结构化高斯先验联合建模，其后验均值被约束在Khatri-Rao积流形上，后验方差有上界以防止推理过程中的范数发散。所得到的闭式坐标上升算法联合估计所有潜在因子及其不确定性。我们将DVB-ALS集成到DVB-TBM中，设计一个完整的URA接收器，包括单用户解映射、带循环冗余校验（CRC）验证的极化译码以及连续干扰消除（SIC）。仿真结果显示，与标准ALS分解相比有显著增益，在URA设置中具有稳健的检测性能，在高系统负载下优于最先进的方案。
英文摘要
Tensor-based modulation (TBM) schemes are a promising approach for unsourced random access (URA), where user separation relies on decomposing the received signal tensor via the canonical polyadic decomposition (CPD), typically computed with alternating least squares (ALS). Standard ALS, however, treats the factor matrices as unstructured and fails to exploit the discrete structure of the tensor sub-constellations. We propose DVB-ALS, a discrete variational Bayesian CPD framework with specific priors, tailored to a tensor structure with the corresponding encoding strategy, combined with iterative computation of an approximate posterior distribution. A discrete Gaussian mixture prior on one Grassmannian factor softly aligns the estimates toward the constellation points. The remaining factors are jointly modeled with a structured Gaussian prior whose posterior mean is constrained to the Khatri-Rao product manifold and posterior variance upper-bounded to prevent norm divergence during inference. The resulting closed-form coordinate ascent algorithm jointly estimates all latent factors and their uncertainties. We integrate DVB-ALS into DVB-TBM to design a complete URA receiver with single-user demapping, polar decoding with cyclic redundancy check (CRC) verification, and successive interference cancellation (SIC). Simulation results show significant gains over standard ALS-based decomposition and robust detection performance in URA settings, outperforming state-of-the-art schemes under high system loads.
CommentsJournal paper, 14 pages, 8 figures
