Abstract
In this paper, we propose a novel joint activity and channel estimation (JACE) algorithm for grant-free extra large MIMO (XL-MIMO) systems subject to spatial non-stationarity phenomena by means of a Bayesian bilinear inference framework. In XL-MIMO systems, the signal from each user is visible only by a small portion of its antenna arrays, which are typically distributed over the surface of a certain structure. The sporadic user activity due to grant-free access, as well as the spatial non-stationarity, jointly imposes a challenging JACE problem involving a nested Bernoulli-Gaussian random variable. In order to address this issue, we decompose the latter into a bilinear inference problem of two independent random quantities, deriving novel message passing rules based on Gaussian approximation and bilinear inference. Performance evaluation via software simulations is offered to demonstrate the effectiveness of the proposed algorithm, which achieves the Genie-aided ideal estimation performance.
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