Explore the vast range of titles available.

Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.

The amplitude-and-phase error (APE) between phased array channels is notorious in radar signal processing. This error can cause an inaccurate estimate of unknown steering vector of the target echo signal and eventually result in amplitude-phase discontinuity of the phased array output. Thus, how to handle the APE is a meaningful problem, particularly for the varying jamming environment, of which the signal-to-noise ratio is very low. In this study, the authors have developed a new method to obtain real-time amplitude and phase differences between two consecutive weight update periods based on the accurate estimation of steering vector. Such differences can be used to obtain a real-time weight vector with negligible amplitude-phase distortions, and hence improves the phased array signal-processing performance. The proposed method is very flexible: it works well in different array configurations, such as linear, rectangular and Y-shape arrays, and can be efficiently implemented in any eigenstructure-based direction-of-arrival system.

The frequency diverse array (FDA) exhibits distinct characteristics from traditional phased arrays by introducing small frequency offsets among the array elements. This paper delves into the relationship between time‐dependent frequency offset (TDFO) and the FDA beam direction in a specific direction and derives the corresponding variation in beam dwell time. By applying the particle swarm optimisation (PSO) algorithm to optimise TDFO parameters, we effectively enhance control over the FDA beam's dwell time in the targeted direction. Simulation results validate the feasibility of this approach in multi‐target and multi‐user communication scenarios. Not only does this approach improve the flexibility of the FDA beam, it also provides novel insights for dynamic beam control in complex communication environments. This letter addresses the lack of effective control over FDA beam dwell time in specific directions. By re‐analyzing the TDFO–FDA beam relationship and using PSO to optimise TDFO, it achieves different dwell times in various directions. The research enhances FDA's dynamic beam control and validates its feasibility in complex communication scenarios.

Next‐generation wireless networks are migrating to higher frequency bands to access abundant spectrum resources. The short wavelengths at these frequencies enable large‐scale antenna arrays with dynamic beamforming to counteract significant path loss. However, beam blockage becomes much more likely at these frequencies. This makes accurate beam quality assessment, which typically relies on the signal‐to‐interference‐plus‐noise ratio (SINR), essential for timely channel reconstruction. In this paper, we identify a phenomenon known as the self‐induced effect, where a high SINR may be estimated even when no signal is actually reaching the receiver. This effect prevents accurate beam quality assessment and limits the effective utilisation of reconfigurable intelligent surface for channel reconstruction. To address this challenge, we present a simple and efficient scheme to eliminate this effect. Numerical results show the effectiveness of the proposed scheme. In this paper, we identify a phenomenon known as the self‐induced effect, where a high signal‐to‐interference‐plus‐noise ratio may be estimated even when no signal is actually reaching the receiver. This effect prevents accurate beam quality assessment and limits the effective utilisation of reconfigurable intelligent surface for channel reconstruction. To address this challenge, we present a simple and efficient scheme to eliminate this effect. Numerical results show the effectiveness of the proposed scheme.

In this letter, we propose a novel algorithm for sparse L‐shaped array that addresses gain/phase uncertainties and off‐grid direction of arrival estimation using compressed sensing theory. Firstly, the receiving signal of the sparse array is structured into an errors in variables model. Then, a novel algorithm termed Improved Orthogonal Matching Pursuit‐Total Least Squares (IOMP‐TLS) is proposed for off‐grid signal reconstruction and gain/phase uncertainties estimation based on an enhanced greedy algorithm. Finally, the simulations show that our proposed algorithm is superior to many other methods in estimation performance. A novel algorithm was proposed for sparse L‐shaped array that addresses gain/phase uncertainties and off‐grid direction of arrival estimation using compressed sensing theory. The receiving signal of the sparse array is structured into an errors in variables model. Then, a novel algorithm termed IOMP‐TLS is proposed for off‐grid signal reconstruction and gain/phase uncertainties estimation based on an enhanced greedy algorithm.

To reduce mutual coupling of an improved nested array (INA), the authors propose an augmented improved nested array (AINA) by splitting the dense subarray of the INA into two parts and rearranging one of them on the right side of the last element in the INA, which enhances the number of unique lags in difference coarray and reduces the number of element pairs with small separations. The closed‐form expressions for the physical element locations, the range of consecutive lags and the number of unique lags are derived for any given element number. It is indicated that the AINA possesses higher degrees of freedom and less mutual coupling than the common sparse arrays. Numerical simulations verify the superiority of the proposed configuration.

Since its inception, the frequency diverse array (FDA) has attracted extensive attention and research because of its unique properties. However, most of the research primarily focuses on linear geometry FDA, with fewer studies on planar FDA. This letter proposes a design method based on planar rectangular FDA (PRFDA), which adjusts the frequency of each array element nonlinearly so that the beam direction of planar rectangular FDA can change linearly or nonlinearly with time, forming the desired transmit beampattern. Theoretical analysis and simulation results verify the effectiveness of the proposed method. We begin by deriving the precise transmit beampattern for the PRFDA and analyzing the impact of its diverse frequency offset settings on beam direction. Building on this groundwork, we propose a DM method rooted in the PRFDA framework, facilitating linear or nonlinear scanning of its beam across three‐dimensional space.

The conventional frequency diverse array (FDA) system, using linear frequency offsets, generates periodic grating lobes and coupling effects, which may increase interference for potential users and difficulty in controlling parameters. To mitigate these issues and generate a dot‐shaped beampattern, nonlinear frequency offsets are introduced to break periodicity and decouple the interdependence of parameters in the range and angle dimensions. Furthermore, to obtain the optimal nonlinear frequency offsets, we propose an algorithm based on the FDA multiple‐input multiple‐output (FDA‐MIMO) structure that integrates the revised dingo optimization algorithm (RDOA) with a Kaiser window function (referred to as the RDOAK algorithm). Specifically, the RDOA is used to optimize the nonlinear frequency offset coefficients, while the Kaiser window function is applied to adjust the waveform. Simulation results demonstrate the superior performance of our proposed RDOAK approach in preventing the mainlobe shift of the beampattern, eliminating grating lobes, suppressing jammings, and achieving a narrower mainlobe width in the range dimension compared to other widely used algorithms. Our study explores the issues of grating lobes and parameters coupling between angle and range dimensions caused by the traditional frequency diverse array (FDA) with linear frequency offsets. We have proposed a revised dingo optimization algorithm with a Kaiser window function (RDOAK) algorithm based on FDA multiple‐input multiple‐output (FDA‐MIMO) structure that integrates a modified dingo optimization algorithm with the Kaiser window function to design nonlinear frequency offset, forming a dot‐shaped beampattern, eliminating the grating lobes. Then the outcomes of the proposed algorithm demonstrate a narrower mainlobe width in the range dimension simultaneously without the mainlobe shift on the beampattern and an enhanced ability to suppress interference.

Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy. This letter presents an efficient gridless Two‐dimensional direction‐of‐arrival estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. By introducing generalized matrix‐form atomic norm minimization, the applicability of atomic norm techniques are extended to a broader range of array geometries. Combining generalized matrix‐form atomic norm minimization with the alternating direction method of multipliers, the approach achieves both high accuracy and computational efficiency.

We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions.