Joint Activity and Channel Estimation for Extra-Large MIMO Systems

• Published in: IEEE transactions on wireless communications Vol. 21; no. 9; pp. 7253 - 7270
• Main Authors: Iimori, Hiroki, Takahashi, Takumi, Ishibashi, Koji, de Abreu, Giuseppe Thadeu Freitas, Gonzalez G., David, Gonsa, Osvaldo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Approximation algorithms; Arrays; Bayes methods; Bayesian analysis; bayesian inference; Channel estimation; Estimation; extra-large multiple-input multiple-output (XL-MIMO); Grant-free access; Inference algorithms; Mathematical models; Message passing; MIMO communication; Normal distribution; Parameterization; Signal to noise ratio; spatial non-stationarity; Statistical inference; Visibility; Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3157271

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 (<inline-formula> <tex-math notation="LaTeX">a.k.a </tex-math></inline-formula>. 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.