Joint Activity and Channel Estimation for Extra-Large MIMO Systems

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 duri...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3157271