In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparse angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

In recent years, contactless sleep breath detection using WiFi signals has gained significant attention. In this paper, we present a position-free breath detection system that utilizes the channel state information (CSI) from a pair of WiFi devices. To address the “blind spot” issue, we propose a robust detection method that takes advantage of both the amplitude and phase of the CSI ratio, ensuring comprehensive coverage for breath detection. We also utilize the periodicity and variability features to select the most suitable breath pattern from the resulting signals. To mitigate the noise interference in non-ideal sleep positions, we propose a novel PCA-VMD fusion method to fully exploit the complementary advantage of sensing over different TX-RX pairs. In this way, we can extract the fine breath frequency component for the breath rate estimation. Extensive experiments are conducted to demonstrate the superiority of the proposed scheme to the existing state-of-the-arts.

Downlink channel estimation in millimeter-wave (mmWave) frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems is investigated in this letter. Considering the sparse scattering nature of the mmWave channels, the estimation problem is formulated as a sparse parameter estimation problem. A low-complexity orthogonal matching pursuit (OMP) estimation algorithm is proposed by exploiting the inherent correlation in the signal angular domain and the statistical scattering information. Analysis and simulation results verify that the proposed algorithm can achieve more than 90% complexity reduction with comparable or even better estimation performance than the existing OMP-based algorithms.

In this paper, we present two clustered sparse Bayesian learning (Cluster-SBL) channel estimation algorithms for millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems. Different from prior literature, channel path correlations at both the transmitter and the receiver are considered and exploited for estimation performance enhancement. Specifically, we first propose a Kronecker product-based multivariate Gaussian (KP-Gaussian) prior model for mmWave massive MIMO channels, which captures not only sparsity but also the widely existing clustering channel property in mmWave communications. Then, a channel approximation method is derived and leveraged to overcome the challenge caused by channel priors' uncertainty due to the varying propagation environments and improve the robustness of the channel estimation algorithm against channel sparsity. After that, the Cluster-SBL channel estimation algorithm, which is developed based on the expectation maximization (EM) framework and has superior estimation accuracy and high robustness, is proposed. Moreover, to reduce the computation complexity in the channel estimation for practical applications, we also propose an efficient Cluster-SBL(eCluster-SBL) channel estimation algorithm. It is highly beneficial to mmWave massive MIMO systems, especially when the angular resolution is relatively high. Numerical results reveal that, compared with the existing compressed sensing-based and Bayesian criterion-based algorithms, the proposed two channel estimation algorithms exhibit the better performance from both the aspect of estimation accuracy and the aspect of robustness.