As the scale of antenna array increases, centralized methods based on deep learning (DL) encounter significant memory constraints on devices. To address this issue, we propose a distributed DL-based framework for gridless direction-of-arrival (DOA) estimation, which substantially alleviates memory constraints for devices operating in large-scale antenna scenarios. We employ an overlapped subarray selection strategy that partitions the complete array into multiple subarrays, allowing for partial array elements overlapped between adjacent subarrays. This strategy effectively compensates for the loss of cross-correlation information between subarrays, thereby enhancing the precision of DOA estimation. Within this framework, each subarray is paired with an independent subprocessor responsible for compressing the received data and transmitting the results to a fusion center. The fusion center leverages a graph neural network (GNN) to effectively extract DOA estimation information from complex datasets. This framework treats DOA estimation as a regression task, leveraging Toeplitz prior to achieve high-precision gridless DOA estimation through postprocessing. Additionally, we introduce a hybrid data-driven and model-based framework that significantly reduces computation time while ensuring the accuracy of DOA estimation, making it particularly suitable for real-time applications. Simulation results demonstrate that our proposed distributed DL methods achieve DOA estimation accuracy comparable to that of centralized DL methods, while exhibiting lower time complexity and reduced memory requirements for devices.

Channel estimation is fundamental to leveraging the performance gains of reconfigurable intelligent surfaces (RISs), one of the key technologies for 6G. Due to the passive nature of RIS, most current research focuses on cascaded channel estimation. However, individual channel information is crucial for practical applications such as flexible precoding design. In this paper, we propose a hybrid RIS architecture integrated with dynamically controllable active elements, which reduces the cost of RIS deployment. Based on this novel architecture, we introduce an atomic norm minimization (ANM)-based individual channel estimation method, exploiting the sparse characteristics of high frequency channels. We theoretically prove that our proposed method retains its applicability even in the presence of random element damage in RIS. Furthermore, we extend the solution for individual channel estimation to passive RIS scenario under the mild condition that the locations of base station and RIS are known. Simulation experiments demonstrate that the proposed methods achieve super-resolution channel estimation, surpassing the performance of existing methods such as orthogonal matching pursuit.

Most existing near-field (NF) source localization methods are based on uniform/sparse symmetric linear arrays. But planar arrays will be more common in the future. In this paper, we propose an NF source localization method for rectangular array, which can be uniform rectangular array (URA) or centrally symmetric sparse rectangular array (SRA). We first use the forth-order cumulant to formulate a low-rank matrix reconstruction (LRMR) problem for angle estimation, and then, we use 1D-MUSIC to find the range estimates. We also consider the dual problem of the LRMR problem to reduce computations. Our method shows similar estimation accuracy to the maximum likelihood method while enjoys much less computations. Simulations results are provided to demonstrate the advantages of our method.

Most studies of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar are based on uniform linear array (ULA), and thus the extended aperture characteristic of sparse arrays cannot be utilized. To solve this problem, a gridless angle and range estimation method for monostatic FDA-MIMO radar with sparse linear receiving array is presented. First, a monostatic FDA-MIMO radar model is established with angle and range decoupled, and then an optimization problem is proposed based on low-rank matrix reconstruction, and solved by alternating projections. Simulation results show that our method not only achieves high-resolution angle and range estimation performance, but also reduces computational complexity.

Extremely large-scale antenna array (ELAA) is a promising technique in 6G and autonomous driving thanks to its high spatial resolution. However, due to the extremely large array aperture, the sources may only "see" a portion of the array, called visibility region (VR). Since the information of VR is usually unknown a priori, traditional methods may encounter performance degradation. In this paper, we show that under the exact steering vector model of ELAA, the eigenvectors of the signal subspace and the steering vectors are approximately collinear in most scenarios. Thus, the angle and range can be easily estimated by using the eigenvectors of the signal subspace. Moreover, the VRs of each source can be also estimated from the eigenvectors of signal subspace. Numerical results show that the estimation performance of our method is comparable to MUSIC with known VR information.

About 30 years have passed since Michael F. Goodchild proposed the term geographical information science (GIScience) in 1992. In the past 30 years, GIScience has made great progress in expanding research findings and perfecting theories and methods. To understand the development progress of GIScience, this research conducts a bibliometric analysis of 9400 publications between 1991 and 2020 in 10 international refereed journals and 2 international conferences of GIScience. We analyze the publication statistics and trends in GIScience from two aspects of journals/conferences and countries/territories. Based on the community detection of the citation network, we extract 15 research themes and show their leading authors and highly cited articles. Furthermore, the change of publication number in different themes over time can indicate the evolution of some research focuses in GIScience. The results demonstrate that the publication proportions of some themes grow rapidly, such as “moving object,” “volunteered geographic information,” and “geographically weight regression,” while the publication proportions of some themes are decreasing, such as “digital elevation model,” “planning support system,” and “ontology.” In the discussion, the journal distribution of papers on different themes is discussed. Moreover, we suggest a few research directions that are worthy of attention in the future.

Most existing deep learning (DL) based direction-of-arrival (DOA) estimation methods treat direction finding problem as a multi-label classification task and the output of the neural network is a probability spectrum where the peaks indicate the true DOAs. These methods essentially belong to grid-based methods and may encounter grid mismatch effect. In this paper, we focus on gridless DL based DOA estimation under generalized linear array which can be regarded as a uniform linear array (ULA) with/without “holes”. By using the Toeplitz structure, a deep convolutional neural network (CNN) is proposed to estimate the noiseless covariance matrix of the aforementioned ULA with “no holes,” based on which the DOAs can be retrieved by using root-MUSIC. To increase the generalization, the parameters of the CNN with respect to different number of sources are pre-trained and stored in a database. We then propose another CNN for source enumeration in order to choose suitable parameters from the database. Our method can find more sources than sensors and do not suffer from the grid mismatch effect.

A new extreme learning machine (ELM) localization technique that uses received signal strength indicator fingerprints only is proposed for multifloor environments. This structured scheme forms multiple individual ELMs for the floors as well as for the geographically formed data clusters of each floor. Multifloor environments often have huge amount of training and online measurement data. To maximize efficiency, we develop a data preprocessing algorithm, aiming to: 1) efficiently extract out only the essential information from the vast amount of data sets and reduce the data dimension and 2) transform the floor-level data sets and positioning data sets of each floor into a proper structure that is suitable for the proposed ensemble ELM technique. The proposed solution is unique in that its offline phase exploits multiple individual ELMs for all floors to generate a set of floor-level classification functions with the preprocessed training data sets, and for each floor, it exploits multiple ELMs for the data clusters to generate a set of position regression functions. The online phase executes a coarse localization step to estimate the floor by using the floor-level classification functions and a refined step to estimate the position on the floor by using the position regression functions. The proposed algorithm and several existing algorithms are implemented to perform localization using the same measured datasets in a multistory building. For both floor estimation and localization on the floor, it outperforms existing schemes. For most cases, the performance gap is substantial.

Most localization methods for mixed far-field (FF) and near-field (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generalized symmetric linear arrays, which include ULAs, Cantor array, Fractal array and many other SLAs. Our method consists of two steps. In the first step, the high-order statistics of the array output is exploited to increase the degree of freedom. Then the direction-of-arrivals (DOAs) of the FF and NF sources are jointly estimated by using the recently proposed atomic norm minimization (ANM), which belongs to the gridless super-resolution method since the discretization of the parameter space is not required. In the second step, the ranges are given by MUSIC-like one-dimensional searching. Simulations results are provided to demonstrate the advantages of our method.

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.

This paper investigates the secure transmission in satellite communication systems, where a geostationary orbit (GEO) satellite serves an earth station while multiple eavesdroppers attempt to intercept the confidential message. Assuming that only the imperfect channel state information (CSI) of the wiretap channels are available, we propose a secure beamforming scheme to maximize the secrecy energy efficiency (SEE) of the earth station while satisfying the signal-noise-ratio (SNR) requirement at earth station, the secrecy constraints at eavesdroppers, and per-antenna power constraints at satellite antenna feeds. Since the formulated optimization problem is mathematically intractable, we propose a two-stage beamforming scheme to convert the original nonconvex problem into a solvable one and obtain the beamforming weight vectors. Numerical results are finally provided to verify the effectiveness of our proposed scheme.

In this paper, we investigate the secure communication of a satellite-terrestrial integrated network (STIN). By supposing that the satellite employs multi-beam antenna while the base station (BS) is equipped with a uniform planar array (UPA), we first formulate a joint constrained optimization problem to maximize the sum rate of STIN while satisfying both the quality-of- service (QoS) requirement of the information receivers and earth stations (ESs), the energy harvest (EH) requirement of the energy receivers (ERs), the secrecy constraint at ERs. Since the formulated optimization problem is non-convex and mathematically intractable, we then propose a joint beamforming (BF) scheme to obtain the optimal solutions through an iterative algorithm, which exploits the sequential convex approximation (SCA) and Taylor expansion to convert the original non-convex problem into a solvable one. Finally, simulation results are given to demonstrate the effectiveness of the proposed joint BF schemes.

Channel estimation is essential for precoding/combining in millimeter wave (mmWave) communications. However, accurate estimation is usually difficult because the receiver can only observe the low-dimensional projection of the received signals due to the hybrid architecture. We take the high speed scenario into consideration where the Doppler effect caused by fast-moving users can seriously deteriorate the channel estimation accuracy. In this paper, we propose to incorporate the nested array into analog array architecture by using RF switch networks with an objective of reducing the complexity and power consumption of the system. Based on the covariance fitting criterion, a joint Doppler and channel estimation method is proposed without need of discretizing the angle space, and thus the model mismatch effect can be totally eliminated. We also present an algorithmic implementation by solving the dual problem of the original one in order to reduce the computational complexity. Numerical simulations are provided to demonstrate the effectiveness and superiority of our proposed method.

The atomic norm minimization (ANM) has been successfully incorporated into the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem for super-resolution. However, its computational workload might be unaffordable when the number of snapshots is large. In this paper, we propose two gridless methods for 2-D DOA estimation with L-shaped array based on the atomic norm to improve the computational efficiency. Firstly, by exploiting the cross-covariance matrix an ANM-based model has been proposed. We then prove that this model can be efficiently solved as a semi-definite programming (SDP). Secondly, a modified model has been presented to improve the estimation accuracy. It is shown that our proposed methods can be applied to both uniform and sparse L-shaped arrays and do not require any knowledge of the number of sources. Furthermore, since our methods greatly reduce the model size as compared to the conventional ANM method, and thus are much more efficient. Simulations results are provided to demonstrate the advantage of our methods.

In this paper, a new method for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed. We first reconstruct the covariance matrix of the coarray with block-Toeplitz structure and then retrieve the DOAs. Our method is computationally efficient as supported by the derived closed-form expression for the estimated covariance matrix. Unlike other methods, which require fully loaded arrays, the proposed method can be applied in the case of common rectangular arrays with arbitrary geometries. The estimated azimuth and elevation angles are automatically paired. Moreover, our method is of high estimation accuracy and immune to the angle ambiguity effect. Numerical simulations are carried out to verify the effectiveness of the proposed method.

This paper addresses the issue of direction-of-arrival (DOA) estimation with an objective to eliminate the off-grid effect of the sparsity-based methods and enlarge the maximum number of distinguishable signals in the subspace-based methods. We first reconstruct the covariance matrix of the array output in the Toeplitz structure and then employ the reconstructed covariance matrix together with root-MUSIC to estimate the DOAs. The proposed covariance matrix reconstruction approach (CMRA) can be used for uniform and sparse linear arrays. It can also estimate the DOAs of multiple signals that are larger than the number of sensors by taking advantage of the array geometry. In contrast to the sparsity-based methods, CMRA is formulated in the continuous angle space rather than the discretized one, and hence it is immune to the off-grid effect. Simulations are carried out to verify the effectiveness of our method.