Spatially Reconfigurable Antenna Systems for 6G: EM-based Channel Modeling, Measurements, and Orientation Design

Spatially reconfigurable antenna systems (SRASs) are recognized as a key physical-layer technology for sixth-generation (6G) systems. By dynamically adjusting each antenna element's spatial configuration, e.g., position and orientation, SRASs can revamp favorable channel conditions for reliable high-rate data transmission. However, in widely adopted channel models, antennas are typically modeled as ideal isotropic radiators, and the vectorial nature of electromagnetic (EM) propagation is neglected. This oversimplified model precludes full exploitation of the degrees of freedom offered by SRASs for performance enhancement. To address this issue, in this paper, by leveraging the theoretical framework of spherical vector wave expansion, we develop an EM-based channel model tailored for SRAS-enabled multiple-input multiple-output (MIMO) systems. The proposed EM-based channel model is applicable to antennas with arbitrary structures and intrinsically accounts for the vectorial nature of EM propagation, thereby enabling accurate characterization of EM effects such as polarization mismatch on channel gain. Full-wave simulations and experimental measurements are conducted, and the results show excellent agreement with theoretical predictions. Simulation results also reveal that antenna orientation exerts a more pronounced influence on the achievable rate than antenna displacement. Therefore, building upon the derived channel model, a manifold optimization method is proposed to maximize the sum-rate of an SRAS-enabled multiuser-MIMO system by optimizing antenna orientations. Simulation results demonstrate that the proposed scheme improves the sum-rate by up to 16.6\% and 19.3\% compared to systems employing movable antennas and conventional fixed antennas, respectively.

Computational and Effective Degrees of Freedom for Spatially Stationary HMIMO Channel Modeling

This paper establishes a comprehensive theoretical framework for the continuous-to-discrete modeling of spatially stationary holographic MIMO (HMIMO) channels utilizing the Nystrom method with Gauss-Legendre quadrature (NGLQ). Starting with an operator-theoretic analysis of the NGLQ method, we prove that its quadrature error exhibits a super-exponential decay. Furthermore, we derive a spatial sampling threshold, termed computational degrees of freedom (cDoF), which reveals a π/2 oversampling penalty over the physical DoF for 1D arrays, compounding to a 68% computational redundancy for 2D separable grids. To address the ill-conditioning of the eigenvalue decomposition (EVD) problem inherent to the Nystrom discretization, we invoke the multidimensional Szego-Widom asymptotic expansion. This analysis yields a physically grounded semi-analytical expression for the effective DoF (eDoF) of 2D rectangular apertures, capturing the anisotropic boundary truncation effects to guide partial EVD and reduce computational complexity. Numerical evaluations confirm the tightness of the cDoF threshold under worst-case end-fire conditions. Moreover, simulations utilizing closed-form kernels for isotropic scattering verify that the derived eDoF acts as an accurate asymptotic approximation. Finally, by deploying the exact non-uniform discrete Fourier transform to eliminate interpolation error floors, we demonstrate spectral convergence down to the machine-precision level for non-isotropic scattering environments.

A Foundation Model for Cross-Band CSI Reconstruction

Acquiring dense high-frequency channel state information (CSI) is costly in multi-band low-altitude wireless systems because pilot resources are limited and channel dimensions change with the carrier frequency, bandwidth, and antenna array size. We address cross-band CSI reconstruction, which recovers dense target-band CSI from dense source-band CSI and sparse, noisy target-band pilots. We propose a foundation model that represents every band in a common power-angle-delay spectrum and uses radio-frequency metadata as auxiliary conditioning for an encoder-decoder. The model uses pilot-guided cross-attention to fuse source-band structure with target-band pilot, allowing one model to handle heterogeneous band pairs. It is trained by pilot-densification pretraining followed by supervised cross-band fine-tuning. On ray-tracing, 3GPP, and DeepMIMO datasets, the model lowers average normalized mean-square error by 6.1 dB over the state-of-the-art pair-specific baseline. It also transfers to two unseen pairs without paired fine-tuning, achieving 7.5 and 7.1 dB gains over fully supervised baselines, and remains effective when target pilots are noisy.

Low-Altitude Channel Multipath Prediction via Panoramic Perception and Vision-Language Model

Unmanned aerial vehicle (UAV) communication is expected to support a wide range of low-altitude applications in 6G mobile networks. However, traditional statistical channel models provide limited accuracy in specific environments, while deterministic methods such as ray tracing usually rely on accurate three-dimensional environment models and involve high computational complexity. Existing multimodal channel prediction approaches mainly focus on large-scale metrics such as path loss, and remain insufficient for modeling small-scale parameters. To address these limitations, this paper proposes PanoLAMP, a Panoramic perception and vision-language model-based Low-Altitude Multipath Prediction framework. It adopts a pretrained vision-language model as the backbone and captures the propagation environment features through panoramic RGB-D observations collected at both the transmitter and receiver to predict the delay, power, azimuth angle, and zenith angle offset relative to the line-of-sight path. Experiments are conducted on a synthetic dataset containing 18,949 UAV-vehicle links across seven UAV altitudes. Experimental results show that the proposed method consistently outperforms representative baselines in both multipath parameters and statistical metrics, and demonstrates stronger generalization across different flight heights.

BDFlow-3DRM: Height-Coherent 3D Radio Map Construction via Bi-Dynamical Flow Matching

Three-dimensional (3D) radio map (RM) is a key enabler for environment-aware communications in low-altitude wireless scenarios by providing site-specific channel priors indexed by spatial locations. However, existing 3D RM construction methods lack effective modeling of the height dimension, which limits their generalization to unseen height configurations and degrades construction coherence across height layers. In this paper, we propose BDFlow-3DRM, a bi-dynamical flow matching framework for 3D RM construction. Specifically, the 3D RM construction problem is formulated as a deterministic probability flow in a semantic latent space. BDFlow-3DRM learns continuous height-aware representations from flexible transceiver height inputs, enhancing geometric awareness. Meanwhile, its bi-dynamical design explicitly models bidirectional dependencies across neighboring height layers, so that RMs at different height layers can be constructed jointly. Extensive experiments on multiple datasets, covering diverse simplified and realistic urban scenarios, validate the effectiveness of BDFlow-3DRM. Compared with diffusion-based baselines, it reduces the normalized mean square error (NMSE) by 28.6% and attains a 180-fold reduction in inference complexity. More importantly, with only 20 training receiver-height layers, BDFlow-3DRM maintains accurate prediction over a wide continuous receiver-height range from 1 to 120 m under variable transmitter heights, highlighting its practical potential for large-scale 3D RM construction.

LAMBDA: A Low-Altitude Multimodal Base Dataset for UAV Sensing and Communication

Research on low-altitude integrated sensing and communication (ISAC) requires aligned multimodal data that jointly describe wireless propagation, visual appearance, unmanned aerial vehicle (UAV) motion, light detection and ranging (LiDAR) perception, and radar sensing under common trajectories and timestamps. To address this need, a low-altitude multimodal base dataset, named LAMBDA, is introduced. LAMBDA is characterized by high fidelity, modality diversity, scenario richness, and configuration flexibility. It is generated through a high-fidelity digital-twin pipeline with detailed scene geometry, refined material assignment, and electromagnetic modeling of UAVs. LAMBDA provides synchronized RGB images, depth maps, LiDAR point clouds, inertial measurement unit states, UAV poses, channel state information (CSI), and radar-synthesis resources across matched low-altitude operating conditions, shared coordinate systems, and synchronized frame indices. The dataset covers urban, suburban, and campus scenes, multi-UAV/multi-base-station settings, nighttime conditions, and sunny, rainy, snowy, and foggy weather variations. Its CSI and radar resources support user-defined antenna-array sizes, bandwidths, subcarrier spacings, chirp parameters, and plane-wave or spherical-wavefront channel synthesis. The reliability and usability of LAMBDA are assessed through quality control, weather and multimodal visualization, and two UAV ISAC-related use cases: RGB-aided beam prediction and RGB-LiDAR-based UAV localization.

Invertible Diffusion for Low-Memory Channel Gain Map Construction in Wireless Communication Networks

Channel gain maps (CGMs) enable propagation-aware services in edge-intelligent wireless communication networks, while diffusion-based CGM construction is memory intensive for on-device training or adaptation. This letter proposes InvDiff-CGM, an invertible diffusion framework that constructs CGMs from sparse measurements and environmental priors. By adopting invertible architectures in both the diffusion process and the U-Net noise estimator, InvDiff-CGM achieves near-constant training memory consumption. A prior-informed multi-scale injector further integrates environmental priors with sparse measurements to improve physical consistency and detail preservation. Experiments on RadioMap3DSeer show about an 85\% reduction in peak training memory and a PSNR of 38.02~dB, outperforming representative recent baselines. This validates the practicality of InvDiff-CGM for high-fidelity CGM construction under edge resource constraints.

Robust Multi-Stream Massive MIMO Satellite Systems Based on Statistical CSI

This paper investigates multi-stream downlink precoding for massive multiple-input multiple-output low-Earthorbit satellite (SAT) communication systems. We adopt a delay and Doppler precompensation approach to achieve coherent transmission. Under this setting, we formulate a signal transmission model that incorporates the near-independent properties of inter-SAT interference and compensation errors. We then demonstrate that moving beyond single-stream transmission requires both multi-SAT cooperation and multi-antenna UTs. Based on this configuration and the established signal transmission model, we derive the first- and second-order statistical channel characteristics and utilize them to design locally optimal precoding algorithms for both total power constraint (TPC) and per-antenna power constraint (PAPC) conditions, which rely only on statistical channel state information (sCSI). In particular, the designed PAPC algorithm achieves linear complexity with respect to the number of antennas on the cooperative SATs. To reduce the computational complexity of the locally optimal precoder under TPC, we propose a low-complexity and robust precoding scheme optimized for both minimum mean squared error and sum-rate maximization objectives. Using majorization theory, we also provide a rigorous theoretical analysis of the optimal precoding structure under TPC. Moreover, the Lanczos algorithm is adopted to further reduce the complexity of the proposed robust designs. Simulation results show that when each SAT is equipped with a sufficiently large number of antennas, the proposed sCSI-based designs achieve performance comparable to that of instantaneous CSI-based designs.

A Unified Codebook Design for Curvature-Reconfigurable Apertures: Seamless Near to Far Field Coverage

Beam training for extremely large-scale arrays with curvature-reconfigurable apertures (CuRAs) faces the critical challenge of severe, geometry-dependent angle-range coupling. While most existing designs compartmentalize near field and far field scenarios, we propose a unified, distance-adaptive hierarchical codebook framework for 1-D and 2-D CuRAs that seamlessly bridges both propagation regimes. Under a spherical-wave model, we first characterize the beamforming-gain correlation in a polar angular domain, deriving an angle-dependent angular sampling rule to capture the varying curvature. To achieve full-range coverage, we introduce a direction-dependent effective Rayleigh distance (ERD) as a soft boundary to gate the range sampling. Crucially, by sampling uniformly in the reciprocal-range domain, the proposed codebook provides precise, dense focusing within the ERD and automatically degenerates into sparse, angle-only steering beyond it. This mechanism eliminates the need for hard mode-switching between near- and far-field operations. Simulation results demonstrate that our unified design consistently outperforms representative baselines in spectral efficiency and alignment accuracy, offering a comprehensive solution for full-range CuRA communications.

Maritime Communication in Evaporation Duct Environment with Ship Trajectory Optimization

In maritime wireless networks, the evaporation duct effect has been known as a preferable condition for long-range transmissions. However, how to effectively utilize the duct effect for efficient communication design is still open for investigation. In this paper, we consider a typical scenario of ship-to-shore data transmission, where a ship collects data from multiple oceanographic buoys, sails from one to another, and transmits the collected data back to a terrestrial base station during its voyage. A novel framework, which exploits priori information of the channel gain map in the presence of evaporation duct, is proposed to minimize the data transmission time and the sailing time by optimizing the ship's trajectory. To this end, a multi-objective optimization problem is formulated and is further solved by a dynamic population PSO-integrated NSGA-II algorithm. Through simulations, it is demonstrated that, compared to the benchmark scheme which ignores useful information of the evaporation duct, the proposed scheme can effectively reduce both the data transmission time and the sailing time.