Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ( a.k.aa.k.a . spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

中文翻译：

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超大型 MIMO 系统的联合活动和信道估计


超大 MIMO (XL-MIMO) 系统会受到形成可见区域 (VR) 的空间非平稳性的影响，从而导致信道矩阵的子阵列稀疏结构。当 XL-MIMO 系统在无授权接入模式下运行时，在给定时隙内只有一小部分潜在用户处于活动状态，因此信道矩阵具有双稀疏和用户特定的结构，使得活动每个用户和每个子阵列的分布可以通过嵌套伯努利高斯分布联合建模。本文考虑了 XL-MIMO 系统中受这种定义的空间非平稳性影响的联合活动和信道估计 (JACE) 问题，解决了这一具有挑战性的推理问题。我们的主要贡献是 1) 引入新颖的伯努利-高斯模型来同时捕获上述两种不同的结构化稀疏性，以及 2) 一种新的双线性贝叶斯推理算法，能够联合估计相关的信道系数、用户活动模式、子阵列活动模式（又称空间非平稳性），由基于期望最大化 (EM) 的自动参数化推动。此外，为了阐明 VR 的真实建模，我们还引入了基于 Matérn 聚类点过程（MCPP）的方法来模拟由于空间非平稳性而产生的聚类活动模式。数值模拟证实了所提出的双线性 JACE 算法的有效性，表明所提出的方法不仅显着优于现有技术（SotA），而且可以在宽信号范围内达到精灵辅助方案的性能在均匀随机和基于 MCPP 的子阵列活动场景中的信噪比 (SNR) 范围。