Spherical Antenna Arrays: Principles, Applications, and Research Directions

With the development of 6G technologies, traditional uniform linear arrays (ULAs) and uniform planar arrays (UPAs) can hardly meet the demands of three-dimensional (3D) full-space coverage and high angular resolution. Spherical antenna arrays (SAAs), with elements uniformly distributed on a spherical surface, provide an effective solution. This article analyzes the issues of traditional arrays, summarizes the advantages and typical structures of SAAs, discusses their potential application scenarios, and verifies their superiority over UPAs via a case study. Finally, key technical challenges and corresponding research directions of SAAs are identified, providing a reference for their research and application in future wireless communications.

Ground Reflection-Aided TomoSAR Imaging with 5G NR Signals

Tomographic synthetic aperture radar (TomoSAR) enables three-dimensional imaging by resolving targets along the elevation dimension, which is essential for environment reconstruction and infrastructure monitoring. A critical challenge in TomoSAR is the severe multipath propagation that causes ghost targets, range offsets, and elevation ambiguities. To address this, this paper proposes an enhanced Newtonized orthogonal matching pursuit (NOMP) algorithm to extract the delay, Doppler, and complex amplitude parameters of each propagation path, effectively separating line-of-sight (LoS) and multipath components prior to TomoSAR processing. Additionally, a height fusion strategy combining TomoSAR estimates with LoS-ground reflection delay-based inversion improves elevation accuracy. Simulation results demonstrate that the proposed method achieves improved positioning and elevation accuracy while effectively suppressing multipath-induced artifacts.

Subspace Fusion Sensing for Cooperative ISAC

This paper proposes a subspace fusion sensing algorithm for cooperative integrated sensing and communication. First, we stack the received signals from access points (APs) into a third-order tensor and construct the equivalent virtual antenna (EVA) array via tensor unfolding. Then, a data association-free subspace-based fusion sensing algorithm is developed utilizing the EVA arrays from distributed APs. A derivation of Cramer-Rao lower bound (CRLB) is also presented. Finally, simulation results validate the effectiveness of the proposed algorithm compared to traditional techniques.

Theoretical and Empirical Study of Spatial Power Focusing Effect for Sparse Arrays at Terahertz Band

This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furthermore, the factors influencing the focusing effect, including phase noise and positional deviations, are theoretically analyzed and numerically simulated. Finally, a 300 GHz measurement platform based on a vector network analyzer (VNA) is constructed for experimental validation. The measurement results demonstrate close consistence with theoretical simulation results, confirming the spatial power focusing effect for sparse arrays.

A Two-Stage ISAC Framework for Low-Altitude Economy Based on 5G NR Signals

The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Synchronization Signal Block (SSB), presents fundamental limitations in sensing resolution. To address the issue, this paper proposes a two-stage coarse-to-fine sensing framework that utilizes standard 5G NR initial access signals augmented by a custom-designed sparse pilot structure (SPS) for high-precision unmanned aerial vehicles (UAV) sensing. In Stage I, we first fuse information from the SSB, Type\#0-PDCCH, and system information block 1 (SIB1) to ensure the initial target detection. In Stage II, a refined estimation algorithm is introduced to overcome the resolution limitations of these signals. Inspired by the sparse array theory, this stage employs a novel SPS, which is inserted into resource blocks (RBs) within the CORSET\#0 bandwidth. To accurately extract the off-grid range and velocity parameters from these sparse pilots, we develop a corresponding high-resolution algorithm based on the weighted unwrapped phase (WUP) technique and the RELAX-based iterative method. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to prune the redundant detections arising from beam overlap. Comprehensive simulation results demonstrate the superior estimation accuracy and computational efficiency of the proposed framework in comparison to other techniques.

Mutual Coupling Aware Channel Estimation for RIS-Aided Multi-User mmWave Systems

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency.

4D Imaging in ISAC Systems: A Framework Based on 5G NR Downlink Signals

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. This paper proposes a 4D imaging framework fully compliant with the 5G New Radio (NR) protocol, ensuring compatibility with cellular systems. Specifically, we develop an end-to-end processing chain that covers waveform generation, echo processing, and multi-BS point cloud fusion. Furthermore, we introduce Zoom-OMP, a coarse-to-fine sparse recovery algorithm for high-resolution angle estimation that achieves high accuracy with reduced computational cost. The simulation results demonstrate that the proposed framework achieves robust 4D imaging performance with superior spatial accuracy and reconstruction quality compared to conventional benchmarks, paving the way for practical ISAC-enabled environment reconstruction in 6G networks.

Large-Model AI for Near Field Beam Prediction: A CNN-GPT2 Framework for 6G XL-MIMO

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the far- field case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network-Generative Pre-trained Transformer 2 (CNN-GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner.

Performance Analysis of Cooperative Integrated Sensing and Communications for 6G Networks

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability and radar information rate. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems

This work considers the three-dimensional (3-D) positioning problem in a Terahertz (THz) system enabled by a modular extra-large (XL) array with sub-connected architecture. Our purpose is to estimate the Cartesian Coordinates of multiple user equipments (UEs) with the received signal of the RF chains while considering the spatial non-stationarity (SNS). We apply the hybrid spherical-planar wave model (HSPWM) as the channel model owing to the structual feature of the modular array, and propose a 3-D localization algorithm with relatively high accuracy and low complexity. Specifically, we first distinguish the visible sub-arrays (SAs) located in the VR and estimate the angles-of-arrival (AoAs) from each UE to typical visible SAs with the largest receive power via compressed sensing (CS) method. In addition, we apply the weighted least square (WLS) method to obtain a coarse 3-D position estimation of each UE according to the AoA estimations. Then, we estimate the AoAs of the other SAs with a reduced dictionary (RD)-CS-based method for lower computational complexity, and utilize all the efficient AoA estimations to derive a fine position estimation. Simulation results indicate that the proposed positioning framework based on modular XL-array can achieve satisfactory accuracy with evident reduction in complexity. Furthermore, the deployment of SAs and the allocation of antenna elements need to be specially designed for better positioning performance.