Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method

In this paper, channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of the spherical wavefront effect and the spatially non-stationary (SnS) property. Due to the diversities of SnS characteristics among different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase channel estimation scheme. In the first phase, the angles of departure (AoDs) on the user side are estimated, and a carefully designed pilot transmission scheme enables the decomposition of the received signal from different paths. In the second phase, the subchannel estimation corresponding to different paths is formulated as a three-layer Bayesian inference problem. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS property in the antenna domain, and the third layer decouples the subchannels from the observed signals. To efficiently facilitate Bayesian inference, we propose a novel three-layer generalized approximate message passing (TL-GAMP) algorithm based on structured variational massage passing and belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed algorithm, showcasing its robustness to different channel scenarios.

Joint Visibility Region and Channel Estimation for Extremely Large-scale MIMO Systems

In this work, we investigate the channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, considering both the spherical wavefront effect and spatial non-stationarity (SnS). Unlike existing non-stationary CE methods that rely on the statistical characteristics of channels in the spatial or temporal domain, our approach seeks to leverage sparsity in both the spatial and wavenumber domains simultaneously to achieve an accurate estimation.To this end, we introduce a two-stage visibility region (VR) detection and CE framework. Specifically, in the first stage, the belief regarding the visibility of antennas is obtained through a structured message passing (MP) scheme, which fully exploits the block sparse structure of the antenna-domain channel. In the second stage, using the obtained VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations demonstrate that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy, especially in low signal-to-noise ratio (SNR) scenarios.

Levenberg-Marquardt Method Based Cooperative Source Localization in SIMO Molecular Communication via Diffusion Systems

Molecular communication underpins nano-scale communications in nanotechnology. The combination of multinanomachines to form nano-networks is one of the main enabling methods. Due to the importance of source localization in establishing nano-networks, this paper proposes a cooperative source localization method for Molecular Communication via Diffusion (MCvD) systems using multiple spherical absorption receivers. Since there is no exact mathematical expression of the channel impulse response for multiple absorbing receivers, we adopt an empirical expression and use Levenberg-Marquardt method to estimate the distance of the transmitter to each receiver, based on which the location of the transmitter is obtained using an iterative scheme where the initial point is obtained using a multi-point localization method. Particle based simulation is carried out to evaluate the performance of the proposed method. Simulation results show that the proposed method can accurately estimate the location of transmitter in short to medium communication ranges.