This paper addresses reconfigurable intelligent surface (RIS)-assisted indoor localization and synchronization in the presence of multipaths. Considering the far field condition, direct range estimation becomes infeasible and there exists clock offset in the system, compounding the difficulty for a single RIS to accomplish user equipment (UE) positioning and synchronization. To tackle this challenge, a dual-RISs system is proposed. The extra degrees of freedom it offers can effectively resolve the problem It uses initial RIS phase design to separate components from dual RISs. Then, atomic norm minimization is employed to reconstruct the separated received signals, and 2D-MUSIC with single-snapshot estimates the angles-of-departure (AODs) of the UE and scatterers. After removing angle terms, root-MUSIC estimates the time-of-arrival (TOA). The UE’s position is derived via least squares using AODs from dual RISs. Combining the UE’s position with LOS path TOAs yields the clock offset. Scatterer positions are obtained using geometric relationships with the UE’s position, NLOS path TOAs, and clock offset. Channel parameters are refined via maximum likelihood estimation. Simulation results prove the method’s effectiveness.

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system's spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system's ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system's performance.

Based on the exact spherical wavefront model, an under-determined estimation method for three-dimensional (3-D) parameters of near-field (NF) sources using L-shaped nested arrays is proposed, referred to as the cumulant algorithm. This algorithm leverages the temporal-spatial domain cumulants of NF sources by constructing virtual data through delayed fourth-order cumulant (FOC) calculations of the original received data. Subsequently, a spatial-spectrum-based subspace method is applied for 3-D NF localization, which involves a 3-D spectral search procedure. Additionally, the maximum number of identifiable NF sources of the proposed algorithm is analyzed. Simulation results demonstrate that, based on the exact spherical wavefront model, the proposed algorithm can achieve underdetermined 3-D parameter estimation of NF sources without any matching process, and it performs better in localization than existing methods.

In this paper, a high-precision three-dimensional (3-D) near-field (NF) localization method is proposed under an underdetermined case based on a symmetric enhanced nested array (SENA). Firstly, the symmetry of the array and the fourth-order cumulant (FOC) are utilized to construct the equivalent virtual far-field (FF) reception data. Then, a gridless sparse and parametric approach (SPA), combined with an l1-SVD based pairing procedure, is used to obtain estimates for two paired angles. Finally, a one-dimensional (1-D) spectral estimator is applied to obtain the estimate of range parameter. Simulation results show the effectiveness of the proposed method.

Most existing direction of arrival (DOA) estimation methods are based on antenna arrays for line-of-sight (LOS) propagation. In this article, a different and challenging DOA estimation problem with a single receiving antenna in the non-line-of-sight (NLOS) scenario is addressed, where a reconfigurable intelligent surface (RIS) is combined with two robust array spatial covariance matrix (SCM) reconstruction schemes to solve the problem. In detail, a two-stage approach for high-efficiency RIS phase shifting is first designed, and then the Tikhonov regularization criterion and total least-squares (TLS) criterion are respectively exploited for SCM reconstruction with and without phase shift error (PSE), yielding an improved DOA estimation performance with reduced complexity. Theoretical analysis on the performance of SCM reconstruction is conducted, and simulation results are provided to show the effectiveness of the proposed solutions.

Most of existing direction of arrival (DOA) based vehicle positioning techniques are established on array sample covariance matrix and multiple measurement data, which suffer from severe performance degradation in case of a single snapshot. In this paper, a challenging vehicle positioning scheme based on single-snapshot DOA and impinging signal magnitude-phase estimation is proposed. In detail, DOA is initially estimated by applying the generalized approximate message passing combined with belief propagation (GAMP-BP) algorithm under the assumption of complex discrete random variable with distinct phase information. Depending on the initial DOA estimates, two efficient approaches are respectively investigated for final DOA and signal magnitude-phase estimation, where the refine-grid GAMP combined with the least squares algorithm (GAMP-LS) and the special reweighted sparse total least-squares (SRE-STLS) are respectively adopted. With available DOA and magnitude-phase estimates, a principle for selecting reliable DOA sets is further designed, finally enabling improved vehicle positioning without ambiguity under multiple collaborative road side units (RSUs). Simulations are performed to show the effectiveness of the proposed solution.

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms.

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant attention.With this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1,j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF.

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

In this paper, based on sum-difference co-array analysis, a new fourth-order sparse array called sum-difference-FODC (SD-FODC) is proposed, allowing the construction of a fourth-order DCA with long consecutive lags using the continuous segments in the second-order DCA and SCA of the original array. It has a closed-form expression for sensor positions that can be generated by two arbitrary nonuniform linear arrays (NLAs) called generators. If the second-order SCA and DCA of the generators have long consecutive segments, the designed fourth-order sparse array can achieve a large number of uDOFs. Numerical results are provided to demonstrate the superior performance of the proposed design.

In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, the fourth-order cumulant of the near-field observations with multiple delay lags is exploited to construct virtual far-field pseudo-observations, leading to increased degrees of freedom (DOF); then, 2-D angles of the nearfield sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the one-dimensional (1-D) MUSIC algorithm is applied by resorting to the conjugate symmetry property of the signal's autocorrelation function. Numerical results are provided to demonstrate the superiority of our method.

A new near-field source localization method is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and range estimation based on a symmetrical cross array. It first employs the conjugate symmetry property of the signal auto-correlation at different time delays to construct a conjugate augmented spatial-temporal cross correlation matrix, then the extended steering vector is decoupled to avoid the usual multiple-dimensional (M-D) search based on the properties of the Khatri-Rao product, and finally three one-dimensional (1-D) MUSIC type searches are employed to obtain the results. The proposed method can realize automatic pairing of multiple parameters associated with each source and it also works in the underdetermined case.

In this article, a novel vehicle positioning system architecture based on direction-of-arrival (DOA) estimation of incoherently distributed (ID) sources is proposed employing massive multiple-input–multiple-output (MIMO) arrays. Such an architecture with the associated signal model is more consistent with the actual array application and multipath transmission scenarios. First, an end-to-end two-dimensional (2-D) DOA estimation of ID sources utilizing a dual one-dimensional (1-D) convolutional neural network (D1D-CNN) under the deep learning (DL) framework is performed, where the normalized covariance matrix data is used for both offline training and online estimation. Then, the received SNR information is exploited to select a set of DOA estimates provided by multiple collaborative BSs for positioning. Moreover, transfer learning and an attention mechanism are employed to promote its generalization ability and achieve robustness against array perturbations. Simulation results are provided to show that the proposed method outperforms the state-of-the-art methods in terms of computational complexity, positioning accuracy, and robustness against array perturbations.

Light field image (LFI) contains the intensity and direction information of the scene. The huge amount of data and different visualization methods of LFI brings great challenges to LFI processing and its blind LFI quality assessment. This paper analyzes the human visual perception from the LFI's visualization, and proposes a novel Visualization-based Blind Light Field Image quality assessment (VBLFI) model. With LFI's visualization and its depth cues, we compute mean difference image from LFI to reduce redundant information of LFI and to describe depth and structural information of LFI. LFI's multi-scale expression with curvelet transform is used to reflect the multi-channel characteristics of human visual system. So, the corresponding natural scene statistical features and energy features are extracted from the mean difference image and sub-aperture images of LFI in curvelet transform domain to form the feature vector, further used to predict the LFI quality. Compared to the representative 2D image quality assessment models and the state-of-the-art LFIQA models, the proposed VBLFI model has better prediction accuracy and stability in the public LFI databases.

In this paper, a gridless super-resolution direction-of-arrival (DOA) estimation method with unknown mutual coupling is proposed. A new clean steering vector is obtained based on the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM). Further, atomic norms associated with the array structure are generated, which can provide a breakthrough in solving super-resolution estimation problem by directly working on the continuous parameter domain. Finally, a semidefinite programming (SDP) method is derived to solve this atomic norm minimization problem. Simulations are provided to verify the effectiveness of the propose method.

In this paper, a novel noncircularity-based localization method for mixed near-field (NF) and far-field (FF) sources is proposed with a symmetric uniform linear array (ULA) in the presence of unknown mutual coupling (UMC). Based on the principle of rank reduction (RARE), the multiple parameters of the sources including direction of arrival (DOA), range and mutual coupling coefficient (MCC) are decoupled, so that only several one-dimensional (1-D) spectral searches are required for their estimation. Meanwhile, the proposed method can also distinguish the types of sources without any extra processing. Simulation results are provided to demonstrate the effectiveness of the proposed method for the classification and localization of mixed sources under UMC.

A two-dimensional (2-D) direction of arrival estimation method for L-shaped array with automatic pairing is proposed. It exploits the conjugate symmetry property of the array manifold matrix to increase the effective array aperture and the number of virtual snapshots simultaneously, and then applies the principle of MUSIC to construct an angle cost function and transforms the conventional 2-D search into 1-D via a Rayleigh quotient, which can greatly reduce the computation complexity. Finally, the azimuth and elevation angles are estimated without pair matching. Simulation results show that the proposed method has a better performance and can resolve more sources than some existing computationally efficient methods.