Extremely large-scale multiple-input multiple-Output (XL-MIMO) technique has recently emerged as a promising candidate to improve the capacity and spectral efficiency for future sixth-generation (6 G) systems. However, efficient channel estimation that considers the corresponding near-field effect of modeling the channel under spherical wavefront assumption is challenging. In this case, the high-storage and high-complexity requirements of sparse channel representation with angle-distance sampling are major barriers. For this reason, we develop a novel channel estimation scheme for XL-MIMO systems within variational Bayesian learning (VBL) framework, where the sparse channel representation in angular domain is adopted. Firstly, considering the property that channel sparsity in angular domain plays a decisive role for near-field channels, we formulate the sparse channel representation and recovery problem with just angular sampling, which differs from those schemes of adopting angle-distance sampling. However, this inaccurate angular sampling degrades the performance of existing channel estimation methods. To deal with it, we propose an incremental variational Bayesian learning (IVBL) channel estimation scheme that adaptively selects columns in the angular-domain sparse representation dictionary based on derived threshold conditions, effectively capturing near-field angular spread. This approach enables channel recovery through dynamic angulardomain dictionary matching and weight adjustment. Finally, simulation results are provided to show the superiority of the proposed channel estimation method in terms of pilot overhead and computational complexity.

With the rapid development of Internet of Things (IoT) technology, an increasing number of resource-constrained devices operate in dynamic and heterogeneous network environments, posing challenges for efficient image transmission. Multi-user semantic communication (SC) enables reduced bandwidth consumption and enhanced noise resilience by understanding the intrinsic meaning of information and sharing common semantic features across devices, offering great potential for widespread applications in various IoT scenarios. However, current multi-users SC approaches for image transmission lack adaptability and fail to consider both content and style features, leading to degraded image reconstruction quality. Moreover, semantic redundancy among devices remains underutilized, limiting bandwidth efficiency in IoT networks. To address these limitations, in this paper, a novel multi-user content-style adaptive semantic communication system for image transmission in IoT scenarios is proposed. Specifically, a dual-branch semantic information extraction and adaptive recovery scheme is first established, which simultaneously captures and adaptively fuses semantic content and style features to improve reconstruction quality. Secondly, an adaptive common information extraction and enhanced coding module is introduced for resource-limited IoT devices, which dynamically adjusts the transmission rate based on varying channel conditions and the computational capabilities of different users, further optimizing communication performance. Finally, experimental results show that the proposed method improves peak signal-to-noise (PSNR) by at least 10% under poor SNR conditions for multi-users semantic communication, compared to baseline methods.

Reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) has recently emerged as a promising candidate to improve the energy and spectral efficiency of Internet of Things (IoT) systems. This paper aims to develop an efficient channel estimation scheme for RIS-assisted MIMO IoT systems within structured Bayesian learning framework. However, the high-dimensional channel matrix with considering its underlying structured sparsity makes efficient channel estimation scheme design a challenging task. To deal with it, we firstly formulate the cascaded RIS-assisted MIMO channel estimation as a generic sparse signal recovery problem with considering the constructed two-level block-structured sparsity of channels. Secondly, we design a flexible prior model to characterize such structured sparsity of channels, in which hierarchical hyperparameters are introduced, and the iterative Bayesian learning-based method is developed to autonomously estimate channels and the hyperparameters associated with the prior model. Thirdly, to relieve the high-computational complexity involving matrix inversion when calculating the posterior of channels, we develop efficient methods from two perspectives. On the one hand, an inverse-free method is developed by relaxed evidence lower bound (ELBO) maximization with an adjustable factor of reducing the gap between the standard ELBO and relaxed ELBO. On the other hand, a method of reducing the dimension of sparse representation matrix aided by external block-structured sparsity is developed. Finally, the computational complexity and convergence properties of the proposed methods are analyzed in detail. Simulation results are provided to verify the superiority of the devised channel estimation methods.

In this paper, we propose a novel three-dimensional (3-D) irregular-shaped channel model for characterizing typically ellipsoidal stadium scenes, which is specifically composed of multiple-confocal truncated semi-ellipsoids and a rectangular parallelepiped. Firstly, we derive the channel impulse responses (CIRs) of the proposed model under the assumption of the spherical wavefront. Secondly, considering the influence of moving obstructions in the stadium, we develop an algorithm for describing occlusion obstacles based on the geometric theory, by means of which the channel model is established through combining with the truncation angle of the truncated semi-ellipsoid. Finally, the impacts of the size of the obstacle, the movement of the obstacle and the truncation angle of the truncated semi-ellipsoid on the proposed channel model are investigated via key statistical properties, e.g., the spatial-temporal cross-correlation function. Our numerical and simulation results show that our proposed model can capture the channel characteristics in the stadium scenes, which can provide guidance for the corresponding system design and performance evaluation in the future.

Museum exhibitions on traditional Chinese paintings are gaining popularity for educational and cultural value. Chinese paintings are characterized by a long history and implicit emotional expression, and it is challenging for non-professional and non-Chinese visitors to understand. To enhance museum visitors’ interest and comprehension of Chinese artworks, we design an EEG-based interactive installation. The installation simulates the process of creation of a work of art, in this case a painting. Visitors can control the generation of lines, colors, and movements of characters by wearing a commercial EEG headset. Our interactive design contributes a novel experience of ’painting with your mind’ and at the same time transform the exhibition into an enjoyable game experience.

In this paper, we develop a novel super-resolution block-sparse channel estimation approach for uplink massive multi-input multi-output (MIMO) based 5G mobile wireless networks. We first introduce a pattern-coupled Bernoulli-Gaussian (PC-BG) prior to characterize block-sparse channels resulting from a small number of scatterers with a small range of angular spread in the angular domain. Then, we propose a Bayesian inference method to infer the channel vector as well as its hyperparameters associated with the PC-BG prior. Specifically, the proposed algorithm is developed within an expectation-maximization (EM) framework and integrated with the generalized approximate message passing (GAMP) technique of approximating the intractable posterior distribution. Finally, instead of adopting some predefined basis/dictionary, such as a discrete Fourier transform (DFT) basis, we propose to learn the offgrid gap between sampled grid-point and true angle of arrivals (AoAs) iteratively for better sparse channel representation, which thus can improve the sparse channel recovery performance. Our numerical analyses validate and evaluate the effectiveness of our proposed scheme.

In this paper, to investigate sparse channel estimation in OFDM communication systems, we propose a novel binary particle swarm optimization (BPSO) based pilot pattern design scheme and develop an efficient sparse Bayesian learning (SBL) scheme for sparse channel recovery. First, through modifying the mutual incoherence property (MIP) criterion, we outline a new penalty function for optimizing pilot pattern design, which comprehensively takes into account the overall coherence of the measurement matrix. Second, we modify the conventional BPSO algorithm by proposing a new adaptive inertia weight scheme, in which the inertia weight varies with the current state of particle swarm and the number of iterations. Furthermore, we map the pilot pattern design into the framework of the modified BPSO algorithm. Third, we develop a partitioned matrix iterative mechanism to compute matrix inversion in each iteration involved in SBL techniques. Finally, our numerical and simulation results illustrate the efficacy of the BPSO based pilot pattern design scheme and our proposed SBL algorithms in terms of mean-square estimation (MSE) error performance.

We address the problem of sparse channel representation for downlink channel estimation in multi-user frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Existing methods typically adopt discrete Fourier transform (DFT) matrix as a sparse basis to represent sparse channels. However, a sparse basis constructed through dictionary learning method has proven to have strong sparse channel representation. In this work, we develop a discriminative dictionary learning-based sparse channel representation for downlink channel estimation in a multi-user FDD massive MIMO systems. Considering partially shared support between near users, we present a new discriminative dictionary learning (DDL) method for sparse channel representation, based on which the channel estimation scheme is developed. Compared with learning a shared dictionary for all users, it can provide a better representation, thus improving the performance of recovery in the compressive sensing process. Numerical results demonstrate the superior performance of discriminative dictionary as compared to the shared dictionary in terms of normalized mean square error (NMSE) and symbol error rate (SER).

This paper develops a method for the time-dependent reliability analysis of deteriorating structures using phase-type (PH) distributions. The deteriorating model consists of two aspects: the progressive deterioration posed by aging effects, and the shock deterioration caused by random shocks. Evaluating the distribution of the sum of random variables is a tough work in the model. For simplified problems that random variables follow specific distributions with the convolutions being derived analytically, it is effective to utilize semianalytical methods for the time-dependent reliability analysis. For other general problems (the distribution does not have additivity or is even unknown, only with limited datasets), the semianalytical method will no longer be applicable. In dealing with such problems, PH fitting is utilized in the proposed model, i.e., any distribution or general datasets are approximated as PH distributions using Expectation Maximization algorithms. Owing to the good properties in convolution of PH distributions, the time-dependent reliability can be evaluated conveniently. Numerical examples are given to demonstrate the efficiency of the model proposed, and its accuracy is verified by comparing the model results with those of Monte Carlo simulation. The interaction between the degradation process and the shock process is also modeled and displayed with an example and discussion.

In this paper, we propose a novel three-dimensional (3D) multi-confocal ellipsoid simulation model with uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) communication systems. Firstly, by employing the spherical wavefront, we characterize near-field effects including the angle of arrival (AoA) shifts and Doppler frequency variations in both space and time domains, and we derive the closed-form expressions of impulse responses of the theoretical model. Secondly, we develop a corresponding simulation model with finite and discrete scatterers within a cluster for the theoretical model. Additionally, we develop the cluster evolution algorithm with a 3D extension of our previously proposed scheme for modeling non-stationary properties of clusters. Their impacts on the proposed channel model are investigated via key statistical properties, e.g., the spatial-temporal cross-correlation function. Moreover, we also discuss the impacts of the range of offset angles and the number of scatterers within a cluster on statistical properties of the proposed simulation model. Finally, our numerical and simulation results show that our proposed simulation channel model is able to capture characteristics of massive MIMO channels while well agreeing with the results obtained from the theoretical modeling.

Over-the-air (OTA) radiated testing for multi-input multi-output (MIMO) capable mobile terminals has been actively discussed in the standardization in recent years, where multiprobe anechoic chamber (MPAC) method has been selected. Setting up a multiprobe configuration is costly, so finding ways to limit the number of probes will make the implementation of the test system simpler and cheaper. In this paper, two probe subset selection algorithms for three dimensional (3D) MPAC and fading emulator are proposed, namely, decremental selection algorithm (DSA) and error threshold selection algorithm based on alternating search (SAAS), where the goal is to minimize the number of probe antennas while ensuring the accuracy of the target channel emulation. Simulation results show that a small number of probe sets are selected under the given error threshold by the two algorithms, which greatly saves the cost of setup configuration. The performance of SAAS generally outperform that of DSA, especially when there are fewer probes selected.

We propose a novel channel model for massive multiple-input multiple-out (MIMO) communication systems that incorporate the spherical wave-front assumption and non-stationary properties of clusters on both the array and time axes. Because of the large dimension of the antenna array in massive MIMO systems, the spherical wave-front is assumed to characterize near-field effects resulting in angle of arrival (AoA) shifts and Doppler frequency variations on the antenna array. Additionally, a novel visibility region method is proposed to capture the non-stationary properties of clusters at the receiver side. Combined with the birth-death process, a novel cluster evolution algorithm is proposed. The impacts of cluster evolution and the spherical wave-front assumption on the statistical properties of the channel model are investigated. Meanwhile, corresponding to the theoretical model, a simulation model with a finite number of rays that capture channel characteristics as accurately as possible is proposed. Finally, numerical analysis shows that our proposed non-stationary channel model is effective in capturing the characteristics of a massive MIMO channel.