This paper proposes a low-complexity sparse representation algorithm for direction-of-arrival (DOA) estimation of coherent signals under mutual coupling for uniform linear arrays (ULAs). At first, the problem is formulated as a block sparse signal recovery problem from array measurements based on the Toeplitz structure of the mutual coupling matrix. Then, it is addressed by the expectation-maximization (EM)-Gaussian scale mixture (GSM)-damping generalized approximate message passing (DGAMP) algorithm at each snapshot. Finally, the DOA estimates are obtained by applying a block norm operation to the signals from all snapshots and identifying the peaks of the resulting vector. By directly utilizing the array measurements, the algorithm successfully avoids the rank deficiency in the array covariance matrix resulting from coherent signals. The proposed algorithm has significantly lower complexity than existing sparse representation algorithms, and enables hardware concurrency for real-time DOA estimation since each snapshot is processed independently. Simulation results demonstrate that the performance of the proposed method is superior to state-of-the-art methods.

Classical source number estimators are usually derived under the assumption of uniform white noise; hence, they are ineffective with unknown nonuniform noise. Current advanced estimators designed for nonuniform noise are computationally expensive and often unable to yield satisfactory performance in unfavorable conditions, such as low signal-to-noise ratio, small number of snapshots, and sources with different transmit power. To address this, this paper proposes a new method to estimate the number of sources under nonuniform noise. The proposed method first constructs a likelihood ratio statistic as a function of the maximum likelihood estimation of the array covariance matrix, which is estimated by a subspace estimation algorithm. Then, based on the asymptotic theory of the likelihood ratio, the number of sources is estimated via a sequence of hypothesis tests. Theoretical analysis demonstrates that the proposed estimator is consistent in the general asymptotic regime. Simulation results show that the proposed estimator achieves a higher correct detection probability in unfavorable conditions and is more robust against nonuniformity of noise than state-of-the-art estimators.

The instrumental variable pseudolinear Kalman filter (IV-PLKF) algorithm, used for 3D angle-of-arrival (AOA) target tracking, has been proven to be more robust to initialization errors, with superior estimation performance and lower computational complexity compared to other state-of-the-art methods. However, the IV-PLKF algorithm requires prior knowledge of the state and angle measurement noise information, which is not available in practice. Improper selection of these values or mismatches due to time-varying changes can significantly impact the stability and estimation performance of the algorithm. To address this issue, this paper proposes a new adaptive fading (AF-) IV-PLKF algorithm that adaptively mitigates the possible scale mismatches in the state and measurement noise covariance matrices and the IV parameters. Simulation results demonstrate that the proposed algorithm outperforms the conventional IV-PLKF under mismatched state and measurement noise covariance scenarios. Moreover, the proposed method can even achieve comparable estimation performance to that of IV-PLKF with perfect knowledge of the noise information.

This paper proposes a novel equalizer for a hybrid-MIMO system combining both linear and nonlinear magnitude-only radio-frequency chains. A dropout-and-constellation-deletion parametric bilinear generalized approximate message passing (DCDP-BIG-AMP) algorithm is developed, and then applied for joint channel and data estimation (JCDE) in the hybrid-MIMO. Compared to parametric bilinear generalized approximate message passing (P-Bi-GAMP), DCDP-BIG-AMP randomly selects only a portion of variables to be updated and deletes constellation points which are changed slightly during the DCDP-BIG-AMP iterations. Consequently, the computational complexity is decreased significantly with the cost of minor performance degradation based on our experimental results. Its core operation is cyclic convolution, which is accelerated by fast Fourier transform (FFT). Subsequently, it is suitable for parallel hardware implementation. Moreover, the expectation maximization (EM) framework is combined to learn the unknown prior distribution parameters of channel. Simulation results show that DCDP-BIG-AMP successfully exploits the nonlinear magnitude measurements, and enables hybrid-MIMO to outperform the traditional MIMO on communication performance and energy-efficiency. DCDP-BIG-AMP based JCDE alternatively enhances the channel estimation (CE) and multiuser detection (MUD) performances, and converges quickly after only about 5 iterations. The dropout and constellation deletion mechanisms well balance between computational complexity and performance degradation.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

This paper proposes a nonlinear MIMO scheme named as halved-phase only MIMO (HPO-MIMO), whose base station (BS) is equipped with multiple HPO radio-frequency (RF) chains extracting$\pi $-periodic phase measurements from RF signals directly by phase detectors, and single classical RF chain sampling the combination of all the RF signals from HPO-RF chains. Compared to MIMO, HPO-RF receivers are simplified significantly, and have much lower power consumption and fabrication cost. Two types of channel estimators and multiuser detectors are developed for HPO-MIMO from the perspectives of numerical optimization and Bayesian inference. Firstly, because$\pi $-periodic phases provide tan-relationships between the Inphase (I) and Quadrature (Q) components, the channel estimation (CE) and multiuser detection (MUD) problems are resolved by minimizing$l_{2}$-norm cost functions with unit-norm constraint on the recovered vector. Their solutions are obtained by deriving the eigenvector corresponding to the minimum eigenvalue through shifted power method (SPW). Secondly, the CE and MUD problems are categorized as generalized linear mixing problems under (un-)quantized$\pi $-periodic phase measurements, and then handled by generalized approximate message passing (GAMP), where closed-form solutions for mean-and-variance messages involving$\pi $-periodic phases are derived. Finally, magnitude and$\pi $-phase ambiguities persisting in signal recovery are removed based on the complex-valued full measurements from the single classical RF chain. Simulation results show that GAMP-type algorithms outperform SPW-type ones, and handle nonlinear phase quantization losses. 16-sector quantized HPO-MIMO could work as well as its un-quantized correspondence. HPO-MIMO reserves the MIMO advantages, but is more energy-efficient than the latter.

The spatio-temporal sparsity (STS) is exploited to improve the channel estimation (CE) for nonlinear multiple input multiple output (NL-MIMO), whose base station (BS) acquires only phases-or-magnitudes of the received complex signals through low-power and low-cost phase/envelope detectors. The sparse CE problem is formulated as the generalized linear mixing one and resolved by a modified generalized vector approximate message passing (GVAMP) algorithm with expectation maximization (EM) mechanism. The prior distribution of sparse channel is modeled as Bernoulli Gaussian-mixture (BGM). Consequently, NL-MIMO channel responses and unknown parameters including BGM parameters and noise variance are updated by the GVAMP and EM procedure alternatingly. A gradient descent (GD) method is proposed for EM update of noise variance in NL-MIMO, where closed-form solution is missed due to the complex formulas of likelihood under phase/magnitude observations. Monte Carlo integration technique is exploited to numerically derive the gra-dient of the noise variance under phase/magnitude observations. Simulation results show that the transmission power and pilot length are significantly saved after exploiting the STS, and both the EM-GVAMP and GD estimators are validated.

This paper proposes a halved-phase only multipleinput- multiple-output (HPO-MIMO) whose base station (BS) acquires pi-periodic phases of complex envelope signals by phase detectors. Because phases are directly extracted from the received radio frequency (RF) signals, the RF receivers of HPO-MIMO are simplified significantly, and enjoy much lower power consumption and fabrication cost in comparison to their correspondences in MIMO. However, existing MIMO baseband algorithms will not work in HPO-MIMO because the latter losses magnitude information completely and its phase observations suffer from pi-ambiguities. Practical baseband algorithms are developed for HPO-MIMO by firstly categorizing the multiuser detection (MUD) and channel estimation (CE) problems as generalized linear mixing ones under pi-periodic phase measurements, and then resolving the latter by a modified generalized approximate message passing (GAMP). In GAMP, the mean-variance messages involving phase observations are numerically updated by the importance sampling technique. Scale-and-polarity ambiguities among the CE and MUD results are removed based on the measurements from a conventional RF chain. Simulation results validate the effectiveness of the proposed algorithms, and compare HPO-MIMO with existing MIMO schemes thoroughly.