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.

Directional Measurements and Analysis for FR3 Low-Altitude Channels in a Campus Environment

In this paper, we present detailed low-altitude channel measurements at the FR3 band in an outdoor campus environment. Using a time-domain channel sounder system, we conduct two types of measurements: path loss measurements by moving the transmitter (Tx) at one-meter intervals along a 26-point rooftop path, and directional power angular spectrum measurements through antenna scanning at half-power beam width intervals. The path loss analysis across different Rx shows that the close-in model outperforms conventional 3GPP models and height-corrected variants, with path loss exponents close to free space values indicating line-of-sight dominance. The power angular spectrum measurements show that propagation behavior varies significantly with environmental conditions. Closer Rx exhibit stronger sensitivity to ground reflections during downward Tx tilting, while obstructed links display uniform angular characteristics due to dominant scattering effects, and corridor environments produce asymmetric power distributions. These results indicate that low-altitude propagation is characterized by complex interactions between Tx height and ground scattering mechanisms, providing fundamental insights for channel modeling in emerging mid-band communication systems.

Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting

Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from limited accuracy or require strong scene priors. Recent neural radiance field (NeRF-based) methods improve reconstruction fidelity through differentiable volumetric rendering, but their reliance on computationally expensive multilayer perceptron (MLP) queries hinders real-time deployment. To overcome these challenges, we introduce Gaussian splatting (GS) to the wireless domain, leveraging its efficiency in modeling optical radiance fields to enable compact and accurate WRF reconstruction. Specifically, we propose SwiftWRF, a deformable 2D Gaussian splatting framework that synthesizes WRF spectra at arbitrary positions under single-sided transceiver mobility. SwiftWRF employs CUDA-accelerated rasterization to render spectra at over 100000 fps and uses a lightweight MLP to model the deformation of 2D Gaussians, effectively capturing mobility-induced WRF variations. In addition to novel spectrum synthesis, the efficacy of SwiftWRF is further underscored in its applications in angle-of-arrival (AoA) and received signal strength indicator (RSSI) prediction. Experiments conducted on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF spectra up to 500x faster than existing state-of-the-art methods, while significantly enhancing its signal quality. Code and datasets will be released.

Low-Latency Terrestrial Interference Detection for Satellite-to-Device Communications

Direct satellite-to-device communication is a promising future direction due to its lower latency and enhanced efficiency. However, intermittent and unpredictable terrestrial interference significantly affects system reliability and performance. Continuously employing sophisticated interference mitigation techniques is practically inefficient. Motivated by the periodic idle intervals characteristic of burst-mode satellite transmissions, this paper investigates online interference detection frameworks specifically tailored for satellite-to-device scenarios. We first rigorously formulate interference detection as a binary hypothesis testing problem, leveraging differences between Rayleigh (no interference) and Rice (interference present) distributions. Then, we propose a cumulative sum (CUSUM)-based online detector for scenarios with known interference directions, explicitly characterizing the trade-off between detection latency and false alarm rate, and establish its asymptotic optimality. For practical scenarios involving unknown interference direction, we further propose a generalized likelihood ratio (GLR)-based detection method, jointly estimating interference direction via the Root-MUSIC algorithm. Numerical results validate our theoretical findings and demonstrate that our proposed methods achieve high detection accuracy with remarkably low latency, highlighting their practical applicability in future satellite-to-device communication systems.

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization

This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detector and conduct an asymptotic analysis of the detection statistic under the large-sample regime. We examine how the signal-to-noise ratios (SNRs) of the target paths and direct paths influence the detection performance. Then, we propose two joint transmit beamforming designs based on the analyses. In the first design, the asymptotic detection probability is maximized while satisfying the signal-to-interference-plus-noise ratio requirement for each communication user under the total transmit power constraint. Given the non-convex nature of the problem, we develop an alternating optimization algorithm based on the quadratic transform and semi-definite relaxation. The second design adopts a heuristic approach that aims to maximize the target energy, subject to a minimum SNR threshold on the direct path, and offers lower computational complexity. Numerical results validate the asymptotic analysis and demonstrate the superiority of the proposed beamforming designs in balancing passive detection performance and communication quality. This work highlights the promise of target detection using unknown communication data signals in multi-static ISAC systems.

Channel Knowledge Maps for 6G Wireless Networks: Construction, Applications, and Future Challenges

The advent of 6G wireless networks promises unprecedented connectivity, supporting ultra-high data rates, low latency, and massive device connectivity. However, these ambitious goals introduce significant challenges, particularly in channel estimation due to complex and dynamic propagation environments. This paper explores the concept of channel knowledge maps (CKMs) as a solution to these challenges. CKMs enable environment-aware communications by providing location-specific channel information, reducing reliance on real-time pilot measurements. We categorize CKM construction techniques into measurement-based, model-based, and hybrid methods, and examine their key applications in integrated sensing and communication systems, beamforming, trajectory optimization of unmanned aerial vehicles, base station placement, and resource allocation. Furthermore, we discuss open challenges and propose future research directions to enhance the robustness, accuracy, and scalability of CKM-based systems in the evolving 6G landscape.

Deep Learning-Based Extended Target Tracking in ISAC Systems

In this paper, we explore the feasibility of using communication signals for extended target (ET) tracking in an integrated sensing and communication (ISAC) system. The ET is characterized by its center range, azimuth, orientation, and contour shape, for which conventional scatterer-based tracking algorithms are hardly feasible due to the limited scatterer resolution in ISAC. To address this challenge, we propose ISACTrackNet, a deep learning-based tracking model that directly estimates ET kinematic and contour parameters from noisy received echoes. The model consists of three modules: Denoising module for clutter and self-interference suppression, Encoder module for instantaneous state estimation, and KalmanNet module for prediction refinement within a constant-velocity state-space model. Simulation results show that ISACTrackNet achieves near-optimal accuracy in position and angle estimation compared to radar-based tracking methods, even under limited measurement resolution and partial occlusions, but orientation and contour shape estimation remains slightly suboptimal. These results clearly demonstrate the feasibility of using communication-only signals for reliable ET tracking.