LiDAR semantic segmentation plays a pivotal role in 3D scene understanding for edge applications such as autonomous driving. However, significant challenges remain for real-world deployments, particularly for on-device post-deployment adaptation. Real-world environments can shift as the system navigates through different locations, leading to substantial performance degradation without effective and timely model adaptation. Furthermore, edge systems operate under strict computational and energy constraints, making it infeasible to adapt conventional segmentation models (based on large neural networks) directly on-device. To address the above challenges, we introduce HyperLiDAR, the first lightweight, post-deployment LiDAR segmentation framework based on Hyperdimensional Computing (HDC). The design of HyperLiDAR fully leverages the fast learning and high efficiency of HDC, inspired by how the human brain processes information. To further improve the adaptation efficiency, we identify the high data volume per scan as a key bottleneck and introduce a buffer selection strategy that focuses learning on the most informative points. We conduct extensive evaluations on two state-of-the-art LiDAR segmentation benchmarks and two representative devices. Our results show that HyperLiDAR outperforms or achieves comparable adaptation performance to state-of-the-art segmentation methods, while achieving up to a 13.8x speedup in retraining.

Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming. However, finding DOAs in THz UM-MIMO systems faces significant challenges: while reducing hardware complexity, the hybrid analog-digital (HAD) architecture introduces inherent difficulties in spatial information acquisition the large-scale antenna array causes significant deviations in eigenvalue decomposition results; and conventional two-dimensional DOA estimation methods incur prohibitively high computational overhead, hindering fast and accurate realization. To address these challenges, we propose a hybrid dynamic subarray (HDS) architecture that strategically divides antenna elements into subarrays, ensuring phase differences between subarrays correlate exclusively with single-dimensional DOAs. Leveraging this architectural innovation, we develop two efficient algorithms for DOA estimation: a reduced-dimension MUSIC (RD-MUSIC) algorithm that enables fast processing by correcting large-scale array estimation bias, and an improved version that further accelerates estimation by exploiting THz channel sparsity to obtain initial closed-form solutions through specialized two-RF-chain configuration. Furthermore, we develop a theoretical framework through Cramér-Rao lower bound analysis, providing fundamental insights for different HDS configurations. Extensive simulations demonstrate that our solution achieves both superior estimation accuracy and computational efficiency, making it particularly suitable for practical THz UM-MIMO systems.

This paper considers a fluid antenna system (FAS)-assisted secure over-the-air computation (AirComp) system, where an access point (AP) with FAS aggregates the data from multiple sensors. While an eavesdropper (Eve) equipped with FAS overhears the data from the sensors, the AP transmits the artificial noise (AN) to prevent eavesdropping. We propose to minimize the mean-square-error (MSE) at the AP under the constraints of the MSE threshold at the Eve, the transmit powers of the AP and each sensor, and the inter-antenna distance of the FAS at both the AP and Eve. Since the optimization variables, which include the aggregation parameters at the AP and the Eve, the AN beamforming of the AP, the transmit scaling factor of each sensor, the positions of fluid antennas (FAs) at the AP and the Eve, are coupled, the considered optimization problem is non-convex. To address this issue, we employ the alternating optimization (AO) algorithm to decompose the original optimization problem into four sub-problems. In particular, for the optimization of the aggregation parameters at the AP and the Eve, we obtain the corresponding closed-form solutions. For the sub-problem of the AN beamforming of the AP and the transmit scaling factor of each sensor, we use the semidefinite relaxation (SDR) algorithm to solve it. For two sub-problems related to the FAs’ positions at the AP and the Eve, we integrate the successive convex approximation (SCA) and majorization-minimization (MM) algorithms to construct appreciate upper and lower bounds on the objective function and constraints to obtain the locally optimal solutions. Simulation results demonstrate that FAS can significantly improve the security of the AirComp systems compared to other benchmarks based on FPA.

As an approach to improve the uniform degrees-of-freedom (uDOFs) of sparse linear arrays, array motion, combined with the array dilation method, has received increasing attention, which increase the maximum uDOFs by several times through multiple uniform shifts and synthesis from array motion. In this work, an novel array dilation scheme adapted to arbitrary asymmetrically defined r-th-order cumulants with multiple shifts is proposed. By increasing the cumulants order or using longer shift sequences, a much higher number of uDOFs is achieved. Simulation results demonstrate the effectiveness of the proposed array dilation scheme.

To realize three-dimensional (3-D) underdetermined parameter estimation for near-field (NF) sources by L-shaped nested arrays, two NF localization methods based on the exact spherical wavefront model are proposed, including the cumulant-based method and the covariance-based method, which utilizes the temporal-spatial domain cumulants and correlations of NF sources, respectively. The cumulant algorithm constructs virtual received data through delayed fourth-order cumulant calculations of the original received data, while the covariance algorithm involves delayed autocovariance and delayed cross-covariance calculations. Subsequently, spatial-spectrum based subspace method is applied to both cumulant-based and covariance-based 3-D NF localization, where the former requires a 3-D spectral search procedure and the latter involves a one-dimensional and a two-dimensional spatial spectrum estimator to obtain the 3-D parameters. Furthermore, the computational complexity, the maximum number of identifiable NF sources and performance analysis of two proposed algorithms are provided. Simulation results demonstrate that the two proposed algorithms can achieve underdetermined 3-D parameter estimation for NF sources without any matching processes procedure. While the covariance algorithm has lower computational complexity, the cumulant algorithm performs better in parameter estimation.

High-precision direction-of-arrival (DOA) estimation, as a key sensing capability for 6G-enabled applications such as autonomous driving and extended reality, is increasingly dependent on the effective exploitation of spatial degrees of freedom (DOFs). This paper integrates two frontier DOFs-oriented paradigms and proposes a fluid antenna-enabled hybrid analog-digital (FA-HAD) architecture, which features an extremely lightweight front-end configuration mechanism and efficient spatial DOFs exploitation. Within this architecture, a collaborative spatial-phase sampling strategy is first developed to enable real-time 2-D DOA estimation under compressive observations, and a single-source CRLB analysis is provided to quantify the achievable performance limit, offering quantitative guidance for accuracy-overhead trade-offs. Furthermore, an efficient virtual-array spatial covariance matrix reconstruction method is proposed to recover a physically meaningful covariance representation, thereby providing a covariance-domain interface that is directly reusable by a broad class of existing covariance-based array processing and array design techniques, which strengthens the scalability and transferability of the proposed architecture. Building upon the reconstructed SCM, a Jacobi-Anger expansion based dimension-reduced MUSIC estimator is further derived for arbitrary planar arrays with a favorable computational cost. Simulation results demonstrate that the proposed FA-HAD framework attains DOA accuracy close to fully digital systems while substantially reducing RF hardware complexity and training overhead.

This paper addresses the jamming detection problem in colored noise environments. Low-power attacks are particularly dangerous due to their stealthiness and persistent harmful effects. The fluctuations caused by colored noise backgrounds further exacerbate this issue, making it easier for these attacks to remain concealed within the noise. We apply a source enumerator, namely the second-order-difference (SOD) algorithm that is robust against both white and colored noise, for jamming detection, and the resulting method is referred to as the SOD jamming detection (SODJD) method. Our analysis of jamming power’s impact on SODJD performance demonstrates that while SODJD can address colored noise, it is limited in scenarios with low-power signals mixed with several higher-power signals. To overcome these limitations, we propose an invariant SOD jamming detection (ISODJD) method by redesigning the criterion rule and introducing an adjustable parameter. Further analysis reveals that larger parameter values lead to improved detection performance, and the increased false alarm rate can be mitigated in massive channel systems, making ISODJD suitable for cooperative wireless communication scenarios. We implement our schemes and conduct extensive performance comparisons. Simulations demonstrate the effectiveness and superiority of the proposed method. Compared to the SODJD method, a 60-antenna MIMO system achieves a 4 dB improvement in detection sensitivity for low-power jamming signals under colored noise, aligning with our theoretical analysis.

Most existing antenna array-based source localization methods rely on fixed-position arrays (FPAs) and strict assumptions about source field conditions (near-field or far-field), which limits their effectiveness in complex, dynamic real-world scenarios where high-precision localization is required. In contrast, this paper introduces a novel scalable fluid antenna system (SFAS) that can dynamically adjust its aperture configuration to optimize performance for different localization tasks. Within this framework, we develop a two-stage source localization strategy based on the exact spatial geometry (ESG) model: the first stage uses a compact aperture configuration for initial direction-of-arrival (DOA) estimation, while the second stage employs an expanded aperture for enhanced DOA and range estimation. The proposed approach eliminates the traditional need for signal separation or isolation to classify source types and enables a single SFAS array to achieve high localization accuracy without field-specific assumptions, model simplifications, or approximations, representing a new paradigm in array-based source localization. Extensive simulations demonstrate the superiority of the proposed method in terms of localization accuracy, computational efficiency, and robustness to different source types.

Fluid antenna system (FAS), which continuously repositions a single physical element across a deployment region [0, D], breaks this limit by freeing antenna positions from the discrete grid entirely. This paper establishes the theoretical foundations of sparse FAS design for direction-of-arrival (DOA) estimation and shows that continuous position freedom unlocks three compounding advantages over the classical designs. First, we derive a universal dual DOF bound and prove that FAS-optimized positions can approach it, growing the DOF linearly with D/λ , where λ is the signal wavelength, rather than saturating at O(N^2). Second, the CRB scales as O(1/D^2L) for L sources, a (D/(N^2 d_0))^2L improvement over the best grid design, with d_0 = λ/2 and D-optimal positions admitting closed-form solution for single sources and efficient Frank-Wolfe algorithm for multiple sources. Third, we propose a two-stage FAS-MUSIC approach that combines coarray MUSIC disambiguation with full-aperture local maximum likelihood (ML) refinement to track the CRB, overcoming the grating-lobe ambiguity inherent in large-aperture non-uniform arrays. Robustness to minimum spacing constraints, mutual coupling, and finite position accuracy is also analyzed. Extensive simulations show that FAS-MUSIC achieves 17.5× lower root mean squared error (RMSE) than uniform linear array (ULA) MUSIC and that FAS with 4 antennas outperforms MRA with 8 antennas, gains that are unattainable by any grid-constrained design.

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nystr & ouml;m approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.

Lane-level autonomous driving relies on high-accuracy vehicle positioning. Among different vehicle positioning approaches, direction-of-arrival (DOA)-based solutions are competitive as they avoid measuring delay information. However, a large distance between the vehicle and road side unit (RSU) compared with the antenna array aperture limits the positioning performance of the existing methods. To address this issue, in this letter, we propose an iterative positioning method using two collaborative RSUs and the spatial geometry, where the DOAs and the positions of vehicles are iteratively estimated. Numerical simulations demonstrate that the proposed method can achieve a positioning performance of millimeter-grade, improving the positioning performance by one to two orders of magnitude, compared with state-of-the-art methods.

Estimation of direction of arrival (DOA) for closely spaced sources under low signal-to-noise ratio (SNR) conditions remains a critical challenge. Classical subspace-based algorithms often fail in such scenarios, while existing deep learning models lack effective mechanisms to explicitly exploit the specific geometry of conformal arrays. To address this challenge, a novel multi-architecture fusion network termed the Graph-Filtered Regression Network (GFR-Net) is proposed. The network first leverages a Multi-Layer Perceptron (MLP) module to classify input signals into distinct SNR levels, thereby adapting to the modeling requirements of signals across different SNR regimes. Next, a Graph Neural Network (GNN) is employed to construct a spatial filtering network, which enhances the structural integrity of the signals by capturing inherent spatial correlations. Finally, a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid network performs spatiotemporal modeling on the filtered signals, enabling accurate DOA regression estimation. Simulation results demonstrate that the proposed GFR-Net significantly improves estimation accuracy and angular resolution for closely spaced sources under low SNR conditions, outperforming conventional subspace-based and representative deep learning methods in terms of robustness and reliability.

With the rapid expansion of low-altitude economy (LAE) services and the growing demand for integrated sensing and communication (ISAC) in air-ground networks, reliable direction-of-arrival (DOA) estimation has become essential for both directional communication and sensing functions. DOA underpins beam alignment, spatial-reuse scheduling, and ISAC-critical tasks such as airspace situational awareness and multi-target monitoring. Hybrid analog-digital (HAD) architectures have emerged as a practical solution for large-aperture directional operation under stringent radio frequency (RF), analog-to-digital converter (ADC), and size, weight, and power (SWaP) constraints. However, HAD compresses antenna-domain observations through analog combining, fundamentally reshaping the measurement model and introducing new algorithmic and system-level challenges for DOA estimation. This article first reviews the principles and representative architectures of HAD, highlighting their advantages for scalable beam-centric and ISAC-oriented operation in LAE scenarios. We then provide a structured overview of HAD-enabled DOA estimation methodologies, including spatial covariance matrix (SCM) reconstruction, multi-combiner scan-based acquisition, and pilot-aided estimation, along with key design tradeoffs. Finally, we discuss open challenges and outline reliability-driven research directions toward robust, deployable HAD-enabled DOA solutions for practical ISAC-enabled low-altitude environments.

Hybrid analog-digital structures (HADS) have emerged as an efficient solution for mitigating transmission loss and reducing power consumption in multiple-input multiple-output (MIMO) systems. However, the limited number of radio frequency (RF) chains and the presence of array mutual coupling (MC) pose significant challenges to achieving high-performance direction-of-arrival (DOA) estimation, thereby hindering effective downlink beamforming. To address these challenges, an efficient DOA estimation method specifically designed for HADS is proposed, effectively mitigating the impact of MC by employing a two-stage framework. The first stage reconstructs the spatial covariance matrix (SCM) by adjusting switch states and leveraging a middle subarray, combined with the real-valued subspace technique for initial DOA estimation. Using these initial estimates, MC is modeled and compensated by adjusting amplifiers and phase shifters. In the second stage, enhanced DOA estimation is achieved by fully exploiting the data from the entire array. Simulation results validate the effectiveness of the proposed method, demonstrating its capability to mitigate the MC effect and deliver accurate DOA estimation under practical conditions.

Sparse array design leveraging the fourth-order difference co-array (FODCA) can significantly enhance the total count of uniform degrees-of-freedom (uDOF). The unfolded coprime L -shaped array (UCLsA), owing to its high sparsity and reduced mutual coupling effect, has become an attractive structure for high-resolution two-dimensional (2D) direction-of-arrival (DOA) estimation. However, the FODCA of UCLsA exhibits numerous holes in its central region, resulting in constrained uDOF. To mitigate the aforementioned limitation, this paper introduces an unfolded coprime padded L -shaped array (UCPLsA), which is constructed by augmenting the UCLsA with two additional subarrays to achieve a substantially larger number of uDOF. First, a systematic analysis of the FODCA of UCLsA is conducted, and the exact representation of the central-hole locations is derived. Then, the position distribution of the newly generated fourth-order virtual elements in UCPLsA is analyzed, followed by the computation of its key weight functions. When compared to current 2D sparse array designs, the proposed UCPLsA provides two major augmented proficiencies, offering a larger number of uDOF and effectively mitigating the mutual coupling effect. Numerical experiments are presented to corroborate the superior performance of UCPLsA in relation to alternative 2D sparse arrays.

Recently, array motion, as an approach to improve the uniform degrees-of-freedom (uDOFs) of sparse linear arrays (SLAs), has received increasing attention. By applying the dilation method to the array geometry, the maximum uDOFs of a dilated array on a moving platform can be tripled through the synthetic array resulting from array motion. Furthermore, through multiple uniform shifts and synthesis, the dilation coefficient can be further increased, as well as the number of uDOFs. However, the existing dilation scheme is only applicable to the second-order statistics based models and does not consider those designed based on high-order cumulants. Besides, in scenarios with multiple shifts, the shift interval is uniform, which actually includes redundancy. In this work, the concept of shift sequence is introduced, which has no uniformity constraint and can be any sparse set. On this basis, a generalized array dilation scheme adapted to arbitrary order cumulants and shift sequence is proposed, which is an extended version of the dilation scheme for second-order statistics based models with uniform shift sequence, and the optimal value of the dilation coefficient is derived based on symmetrically defined 2q-th-order cumulants and asymmetrically defined r-th-order cumulants, respectively. It is revealed that, by increasing the order to cumulants and using sparse shift sequences, a much higher number of uDOFs is achieved. Simulation results are provided to demonstrate the effectiveness of the proposed generalized array dilation scheme.

Unlike fixed-position arrays with static observation entropy, the scalable fluid antenna system (S-FAS) can dynamically adjust its aperture to form different observation spaces with configuration-dependent entropy budgets. This reconfigurability requires an information-theoretic framework beyond traditional algebraic identifiability analysis. This paper establishes an observation entropy framework for S-FAS, which unifies the derivation of identifiability limits, the diagnosis of processing bottlenecks, and system design optimization. For an S-FAS with mutual coupling suppression, we derive a complete capacity hierarchy among compressed, extended, and jointly stacked configurations. The entropy framework reveals that sequential two-stage processing suffers from an information bottleneck that restricts achievable capacity, while the noise entropy ratio can be used to distinguish fundamental performance limits from algorithmic deficiencies. A joint MUSIC algorithm is proposed to approach the theoretical joint capacity bound. Extensive Monte Carlo simulations, validated by both algebraic and information-theoretic criteria, verify the derived capacity hierarchy and identifiability boundaries.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

Direction-of-arrival (DOA) estimation with sufficient accuracy and efficiency for incoherently distributed (ID) sources remains a challenging problem. To this end, a computationally efficient two-dimensional (2-D) DOA estimation algorithm for ID sources is proposed, where the generalized array manifold (GAM) of an L-shaped array established on the first-order Taylor series approximation is exploited. By extracting the first column of the array cross-covariance matrix to eliminate the impacts of additive noise and angular spreads, and further applying the conjugate symmetry operation to enlarge the effective array aperture, the proposed algorithm provides an improved estimation performance with closed-form expressions. Moreover, a proper pairing criterion is designed to avoid the ambiguity problem, and simulation results are presented to show the superiority and effectiveness of the proposed algorithm.

This paper addresses the issue for near-field (NF) localization considering amplitude attenuation, proposing a recursive rank-reduction (RARE) method for three-dimensional (3-D) parameter estimation of NF sources incident on a symmetrical cross array. The proposed method constructs several one-dimensional (1-D) spectral peak search estimators to obtain two-dimensional angle and range parameters. Initially, using the received data from symmetrical array elements in one axis, a 1-D spectral peak search estimator is constructed. The origins of two types of pseudo peaks within this estimator are analyzed, and then, corresponding pseudo peaks removal methods, namely the initial screening method and the recursive RARE method, are presented to obtain the estimate of the first angle parameter. Subsequently, the estimated results are fed into another 1-D spectral peak search estimator constructed from the original received data to obtain the range parameter. Finally, the same process is applied to the other axis to obtain the second angle and range parameters, followed by a parameter matching operation for 3-D parameters. Compared to existing NF source localization methods, the proposed method more effectively eliminates pseudo peaks, and demonstrates superior parameter estimation performance under conditions of small number of snapshots and low signal-to-noise ratio, as validated by several simulation results.