Wireless communication systems offer strong potential for accurate localization, and deep-learning-based fingerprinting has shown good adaptability in complex propagation environments. However, existing fingerprints are limited to static location features, and current neural network designs do not fully utilize their structural properties. To improve this, we introduce a dynamic triple-beam fingerprint (TBF) for massive multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems and develop a localization and orientation awareness network (LOA-Net) designed to align with its sparse structural characteristics for joint localization and orientation estimation. We analyze the advantages of TBF for localization and orientation awareness through channel modeling. Localization is formulated as a regression task and enhanced with a masking mechanism, while orientation estimation is modeled as a multi-class classification problem using the estimated coordinates as prior information. Simulations in standard 3GPP scenarios demonstrate the high localization accuracy and the potential for effective orientation awareness.

In this paper, we investigate precoder design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication (SATCOM). We first establish a beam based channel model that characterizes the spatial single path property of the LEO SATCOM channel, and reveal its sparsity in the beam domain. Based on this channel model, we prove that the design of the space domain precoder can be simplified into that of a lower-dimensional beam domain precoder. More importantly, we introduce a sparsity constraint on the beam domain precoder and propose a novel sparse precoder, achieving a flexible trade-off between transmission performance and computational complexity. Simulation results demonstrate the satisfactory performance of the proposed sparse precoder with low complexity.

Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system to fully exploit its maximum spatial diversity for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.

In this paper, we investigate the signal detection for user-centric network (UCN) massive multi-input multi-output (mMIMO) system. We consider that the users are divided into multiple user groups (UGs). For each UG, leveraging the interference sparsity, we reveal that the performance of the minimum-mean-square-error (MMSE) detector can be guaranteed in the network mMIMO system by using the matched filtering (MF) outputs of the intra-group and interfering users. Then, with the base station (BS) connection sparsity, we reveal that the detection performance of each UG is primarily determined by a limited number of associated BSs. To facilitate practical application, we propose a straightforward user grouping method and outline the process for determining interfering users and associated BSs for each UG in UCN mMIMO systems. Then, we propose a user-centric detection method that decouples the detection process for each UG into two stages. In the first stage, local MF is performed at each associated BSs using local information. In the second stage, group-wise interference cancellation (IC) is carried out to obtain detection results at the primary serving BS (PSBS) of each UG, with information exchanged from auxiliary serving BSs (ASBSs). Simulation results confirm the effectiveness and computational efficiency of our proposed user-centric detection for the UCN mMIMO system.

Distributed multiple-input-multiple-output radar in electronic countermeasure environments faces a critical tradeoff between surveillance performance and electromagnetic exposure. This article jointly optimizes node positions and transmit states to balance the signal-to-interference-plus-noise ratio against the probability of interception by self-protection jammers. We formulate this as a 0-1 mixed-integer biobjective optimization problem and solve it using an improved particle swarm optimization algorithm. Simulation results demonstrate the superior tradeoff performance compared to benchmark methods.

Integrating multisource Earth observation data and reconstructing subsurface thermohaline structures from remote sensing at global and basin scales will provide a better understanding of oceans. However, previous methods relied on layer-by-layer modeling, which required separate reconstruction of subsurface temperature and salinity fields at different depths, resulting in many models, inefficiency, and weak vertical connections between thermohaline data at different depths. A fast deep neural network-based reconstruction can reduce models and enhance the overall consistency of thermohaline data, which is significant for the reconstruction of ocean environmental variables. This study proposes an improved Swin Transformer approach, i.e. SwinOcean3D, to perform one-shot reconstruction of three-dimensional (3-D) thermohaline structures (upper 1000 m) in different scales of Global (1 degrees x 1 degrees) and Indian Oceans (0.25 degrees x 0.25 degrees) by integrating multisource remote sensing and observation-based ocean products. SwinOcean3D combines the Swin Transformer, U-net, and dual-residual blocks to enhance the representation capability of the global scale, local detailed, and vertical features of ocean thermohaline structures. The significant advantages of SwinOcean3D in the reconstruction of multiscale 3-D thermohaline structures outperform other classical approaches. Furthermore, interpretability experiments suggest that SwinOcean3D can effectively capture the evolution of 3-D thermohaline structures from multisource observations.

The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-location channel measurements and constructs a structured channel charting with location-indexed PCFs. Furthermore, we propose a low-complexity algorithm to acquire beam domain statistical CSI (sCSI) using the PCFs in the channel charting. The resulting sCSI can be directly employed as prior information for channel estimation. Simulation results show that the proposed method delivers sCSI performance comparable to traditional online probing techniques, and the generated sCSI can serve as reliable prior knowledge to significantly enhance the accuracy of channel estimation. These results validate the proposed PCF as a powerful and versatile tool for channel acquisition and system design of the next-generation mobile communication.

In this paper, we propose a low complexity turbo receiver for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems. By leveraging the beam based channel model (BBCM), we reveal the interference sparsity of HF skywave massive MIMO systems. Exploiting the interference sparsity, we provide a condition of extracting sufficient observation for signal detection. Motivated by this condition, we construct the interference user terminal (UT) set (IUS) and extract the observation vector from the received signal after matched filtering (MF) for each UT. Then, a low-dimensional interference sparsity-aware detector (ISD) is separately designed for each UT, and the interference sparsity-aware turbo receiver (ISTR) is subsequently formulated using ISDs. Further, we develop an efficient implementation of the ISTR, involving approximate computations of the ISD and the signal reconstructed by ISD. Simulation results confirm that the proposed ISTR achieves excellent performance with relatively low complexity.

The paradigm shift from environment-unaware communication to intelligent environment-aware communication is expected to facilitate the acquisition of channel state information for future wireless communications. Channel Fingerprint (CF), as an emerging enabling technology for environment-aware communication, provides channel-related knowledge for potential locations within the target communication area. However, due to the limited availability of practical devices for sensing environmental information and measuring channel-related knowledge, most of the acquired environmental information and CF are coarse-grained, insufficient to guide the design of wireless transmissions. To address this, this paper proposes a deep conditional generative learning approach, namely a customized conditional generative diffusion model (CDiff). The proposed CDiff simultaneously refines environmental information and CF, reconstructing a fine-grained CF that incorporates environmental information, referred to as EnvCF, from its coarse-grained counterpart. Experimental results show that the proposed approach significantly improves the performance of EnvCF construction compared to the baselines.

With advancements in wireless communication and localization technologies, cellular networks are evolving towards integrated sensing and communication (ISAC) capabilities. To address the challenges of sensing-assisted communication, we introduce the digital twin of channel (DToC). Specifically, locations of user terminals (UTs) and their statistical channel state information (sCSI) are treated as physical objects and virtual counterparts in the concept of digital twin (DT), respectively. In this work, we establish a probabilistic model that characterizes sCSI as a location-conditioned distribution. To enable precise sCSI generation, we enhance the latent diffusion model (LDM) and propose an improved latent diffusion model (ILDM) with deterministic sampling. We further propose an accelerated LDM method to speed up the generation process by skipping certain sampling steps. Simulation results demonstrate that the proposed ILDM achieves high accuracy in generating sCSI, while the accelerated LDM delivers significant speedups with minor performance degradation. Our results also validate that the DToC framework can effectively generate sCSI without pilot overhead.

Reasonable and rigorous beam position design is an important task of a spaceborne radar system to realize moving target detection. However, due to the high-speed movement of the satellite platform, the relative position between the radar and the detection area will change over the observation time, which may change the spaceborne radar beam point position and degrade the radar detection performance. Based on this observation, this article analyzes the impact of satellite platform motion on radar beam position and proposes a novel beam position design method of time-dynamic allocation that considers the influence of satellite platform motion. This algorithm updates the elevation and azimuth angles of the radar beam scanning based on the relative position between the satellite and the designed beam position center at a time. And it dynamically allocates detection times for different beam positions according to the target detection performance, thereby mitigating the signal-to-noise ratio (SNR) differences caused by the satellite motion. It can effectively improve the utilization rate of the time resources of a spaceborne radar system. Simulation results verify the effectiveness of the proposed algorithm.

Hyperspectral (HS) cameras have great potential in extracting spectral, textual, and temporal information from objects. Many existing works leverage HS data for object tracking, as it provides unique spectral features that can help address challenges like background clutter (BC) or camouflage. However, most of these methods overlook the rich temporal information available in video sequences, and many spectral-visual fusion approaches fail to extract contextual information from a global perspective, causing an insufficient understanding of an entire object by the model. To address the above issues, a spectral-temporal tracking Transformer based on a frequency-domain fusion strategy (S3T-FFS) is proposed. First, a spectral-temporal token is proposed to capture an object's spectral information that remains unchanged in video clips, providing additional tracking cues. Second, to extract spectral semantic information, we propose a frequency-domain fusion strategy (FFS), including a frequency attention network (FAN) and a Kolmogorov-Arnold network-based convolutional unit (CuKAN), to provide spectral information for the tracking model. Specifically, FAN is designed for the simultaneous extraction and fusion of spectral features. This synchronous modeling approach decouples low-frequency and high-frequency information, adjusting their balance through frequency-domain prior knowledge and self-adaptive weights. This allows the fusion network to focus more on global information. To further extract global patterns from the low-frequency features and improve the network's interpretability, we introduce CuKAN to extract nonlinear relationships within the decoupled frequency components, and its learnable activation function helps the model learn global patterns, thus avoiding the local overfitting in convolutional neural networks. Extensive experiments on multiple large-scale datasets illustrate the effectiveness of our proposed methods.

Due to the flexible mobility and high-quality of line-of-sight (LoS) channels, uncrewed aerial vehicle (UAV) has begun to play an important role in wireless communications. However, the broadcasting nature of wireless communications and the limited payload of UAVs render the spectrum vulnerable to malicious jamming attacks. To guarantee the performance of UAV communications, this paper focuses on reactive jamming, and sets up an actively exposed deception band to attract partial power of jamming. Specifically, we first model the anti-jamming process with a Stackelberg game model, under the assumption that the rational behavior of jamming is known. Then, we analyze the theoretical optimal strategies of the jamming as well as the users in UAV communication to reach the equilibrium of the above game model. Finally, we design a collaborative multi-agent jamming deception method to achieve anti-jamming in the absence of environmental and jamming information. This method is based on the centralized evaluation network at the UAV and decentralized policy network at each user. Simulation results show that the anti-jamming performance of the proposed method can approach the theoretical upper bound and significantly outperform other benchmark methods.

Meteorological clutter is composed of gas molecules and aerosols. It can scatter and absorb electromagnetic waves emitted by the spaceborne radar, especially under the extreme weather conditions, influencing the target detection performance in a spaceborne radar system with a wide searching mode. In this article, a multichannel signal modeling method of meteorological clutter is established with the consideration of the distribution region, particle size, and velocity components of meteorological particles, as well as the attenuation of radar signals, where the meteorological clutter including the cloud, rain, and snow particles are considered under different climate environments. Based on the traditional spaceborne multichannel radar model, the effects of different meteorological conditions on the adaptive suppression and target detection performances in a spaceborne multichannel radar system are analyzed. The simulation results may provide a reference for the work of spaceborne multichannel radar in the harsh climate scene.

This paper investigates the design of the on-off division multiple access (ODMA) transmission scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) systems with soft-output (SO) polar codes. First, a three-segment pilot-uncoupled coding scheme is introduced under the ODMA framework, which reduces the coding rate of the data segment without increasing the transmission overhead, improving the overall system performance. Building upon this architecture, a hierarchical pattern detection framework is developed. Specifically, a coarse-grained candidate set of transmission patterns is first identified through correlation operations. Based on this, a message-passing (MP)-based pattern detection algorithm is developed to iteratively estimate the posterior probabilities of transmission patterns, followed by the maximum a posteriori (MAP) estimation to obtain the precise pattern detection result. Furthermore, a joint pattern detection and data decoding algorithm based on the bit-wise SO information of polar decoder is investigated, where the posterior probability information provided by the polar decoder is exploited to refine the pattern detection and contribute to an improved accuracy. In addition, by leveraging bit-wise SO information of the successive cancellation list polar decoder, an MP-based iterative decoding algorithm is developed to significantly enhance the decoding performance. The proposed scheme simultaneously exploits the transmission gain of uncoupled-ODMA framework, the coding gain of polar codes in the short-blocklength regime, and the iterative decoding gain enabled by SO information, while the computational complexity is significantly reduced through the hierarchical detection framework. Simulation results demonstrate that the proposed scheme achieves strong robustness ...

Massive multiple-input multiple-output (MIMO) offers significant advantages in spectral and energy efficiencies, positioning it as a cornerstone technology of fifth-generation (5G) wireless communication systems and a promising solution for the burgeoning data demands anticipated in sixth-generation (6G) networks. Meanwhile, the rapid evolution of artificial intelligence (AI) has ushered in a new era dominated by large AI models (LAMs), particularly large generative foundation models (LGFMs), which have achieved impressive success in computer vision (CV), natural language processing (NLP), and autonomous driving. As a pioneering force, these models are driving the paradigm shift in AI towards large generative AI (LaGenAI). Among them, the generative diffusion model (GDM), as one of state-of-theart families of generative models, demonstrates an exceptional capability to learn implicit prior knowledge and robust generalization capabilities, thereby enhancing its versatility and effectiveness across diverse applications. In this paper, we delve into the potential applications of GDM in massive MIMO communications. Specifically, we first provide an overview of massive MIMO communication, the framework of LGFMs, and the working mechanism of GDM. Following this, we discuss recent research advancements in the field and present a case study of near-field channel estimation based on GDM, demonstrating its promising potential for facilitating efficient ultra-dimensional channel statement information (CSI) acquisition in the context of massive MIMO communications. Finally, we highlight several pressing challenges in future mobile communications and identify promising research directions surrounding GDM.

In a spaceborne bistatic radar system, where the transmitter and receiver are physically distinct, the background clutter usually exhibits severe spatial-temporal two-dimensional range dependence. Then the suppression residues from the range ambiguous clutter components may appear outside the main-lobe clutter region and the additional filtering notches may be formed, ultimately reducing the clean area for moving target detection. This paper presents a method aimed at this problem, which suppresses range ambiguous clutter through inter-pulse random phase coding. This method can effectively alleviate the influence of range ambiguous clutter via signal mismatching along Doppler dimension, and thus enhances the clutter suppression robustness at the expense of a certain reduction in target signal-to-clutter-plus-noise ratio. Simulated results are provided to evaluate the feasibility and performance of this method in different spaceborne radar configuration cases.

Recent advances in semantic segmentation of multi-modal remote sensing images have significantly improved the accuracy of tree cover mapping, supporting applications in urban planning, forest monitoring, and ecological assessment. Integrating data from multiple modalities-such as optical imagery, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)-has shown superior performance over single-modality methods. However, these data are often acquired days or even months apart, during which various changes may occur, such as vegetation disturbances (e.g., logging, and wildfires) and variations in imaging quality. Such temporal misalignments introduce cross-modal uncertainty, especially in high-resolution imagery, which can severely degrade segmentation accuracy. To address this challenge, we propose MURTreeFormer, a novel multi-modal segmentation framework that mitigates and leverages aleatoric uncertainty for robust tree cover mapping. MURTreeFormer treats one modality as primary and others as auxiliary, explicitly modeling patch-level uncertainty in the auxiliary modalities via a probabilistic latent representation. Uncertain patches are identified and reconstructed from the primary modality's distribution through a VAE-based resampling mechanism, producing enhanced auxiliary features for fusion. In the decoder, a gradient magnitude attention (GMA) module and a lightweight refinement head (RH) are further integrated to guide attention toward tree-like structures and to preserve fine-grained spatial details. Extensive experiments on multi-modal datasets from Shanghai and Zurich demonstrate that MURTreeFormer significantly improves segmentation performance and effectively reduces the impact of temporally induced aleatoric uncertainty.

In this paper, we investigate a movable antenna (MA)-assisted uncrewed aerial vehicle (UAV) swarm communication system. Unlike conventional fixed-position antenna (FPA) systems, each UAV is equipped with an MA array distributed on two hemispherical surfaces at the head and tail, significantly expanding the spatial degrees of freedom (DoFs) in three-dimensional (3-D) seamless coverage. A far-field line-of-sight (LoS) channel model is adopted to characterize the UAV-to-UAV (U2U) communication links, incorporating both antenna positioning and radiation patterns. We formulate an achievable sum rate maximization problem by jointly optimizing the antenna position vectors (APVs) and transmit/receive beamforming vectors, subject to constraints on maximum transmit power, limited antenna moving region, and minimum inter-antenna spacing. To tackle this non-convex and highly coupled problem, we propose a two-loop iterative optimization algorithm that effectively combines the Spider Wasp Optimizer (SWO) for APV optimization and alternative optimization (AO) for beamforming design. Extensive simulation results demonstrate that the proposed MA-assisted scheme outperforms traditional FPA systems and other benchmark algorithms under various settings. The performance gains are attributed to the efficient optimization of antenna positions within the hemispherical moving region for interference suppression and coverage enhancement.

Hyperspectral imaging delivers high-resolution spectral-spatial information to support molecular tissue characterization, but its clinical utility is far from being fully realized. Existing segmentation techniques are constrained by fixed or suboptimal band selection strategies and insufficient frequency-domain modeling, which limit their ability to fully exploit discriminative spectral cues and subtle tissue structures. To address these challenges, we propose AMBS-SF2Net, a unified framework that enhances spectral representation and hierarchical frequency modeling for accurate and efficient segmentation. Specifically, the Adaptive Mask-based Band Selection (AMBS) module dynamically identifies informative spectral channels, the Adaptive Spectral-Frequency Integration (ASFI) module fuses multi-scale spatial edges and frequency-aware spectral features, and the Multi-Axis Frequency Enhanced (MAFE) module captures complementary spectral and spatial frequency patterns along different tensor dimensions. To rigorously evaluate the method’s generalizability, we conduct extensive experiments on datasets spanning distinct imaging scales, comprising a microscopic cholangiocarcinoma pathology dataset and two macroscopic tissue datasets of pig abdominal organs and human placenta. Results demonstrate that AMBS-SF2Net significantly outperforms state-of-the-art methods, exhibiting superior robustness across varying spectral resolutions and spatial modalities, thereby validating its strong potential for diverse clinical applications.