Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek

Beam prediction is an effective approach to reduce training overhead in massive multiple-input multiple-output (MIMO) systems. However, existing beam prediction models still exhibit limited generalization ability in diverse scenarios, which remains a critical challenge. In this paper, we propose MLM-BP, a beam prediction framework based on the multi-modal large model released by DeepSeek, with full consideration of multi-modal environmental information. Specifically, the distribution of scatterers that impact the optimal beam is captured by the sensing devices. Then positions are tokenized to generate text-based representations, and multi-view images are processed by an image encoder, which is fine-tuned with low-rank adaptation (LoRA), to extract environmental embeddings. Finally, these embeddings are fed into the large model, and an output projection module is designed to determine the optimal beam index. Simulation results show that MLM-BP achieves 98.1% Top-1 accuracy on the simulation dataset. Additionally, it demonstrates few-shot generalization on a real-world dataset, achieving 72.7% Top-1 accuracy and 92.4% Top-3 accuracy with only 30% of the dataset, outperforming the existing small models by over 15%.

STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising

Remote sensing image (RSI) denoising is an important topic in the field of remote sensing. Despite the impressive denoising performance of RSI denoising methods, most current deep learning-based approaches function as black boxes and lack integration with physical information models, leading to limited interpretability. Additionally, many methods may struggle with insufficient attention to non-local self-similarity in RSI and require tedious tuning of regularization parameters to achieve optimal performance, particularly in conventional iterative optimization approaches. In this paper, we first propose a novel RSI denoising method named sparse tensor-aided representation network (STAR-Net), which leverages a low-rank prior to effectively capture the non-local self-similarity within RSI. Furthermore, we extend STAR-Net to a sparse variant called STAR-Net-S to deal with the interference caused by non-Gaussian noise in original RSI for the purpose of improving robustness. Different from conventional iterative optimization, we develop an alternating direction method of multipliers (ADMM)-guided deep unrolling network, in which all regularization parameters can be automatically learned, thus inheriting the advantages of both model-based and deep learning-based approaches and successfully addressing the above-mentioned shortcomings. Comprehensive experiments on synthetic and real-world datasets demonstrate that STAR-Net and STAR-Net-S outperform state-of-the-art RSI denoising methods.

A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis

Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

A Unified Deterministic Channel Model for Multi-Type RIS with Reflective, Transmissive, and Polarization Operations

Reconfigurable Intelligent Surface (RIS) technologies have been considered as a promising enabler for 6G, enabling advantageous control of electromagnetic (EM) propagation. RIS can be categorized into multiple types based on their reflective/transmissive modes and polarization control capabilities, all of which are expected to be widely deployed in practical environments. A reliable RIS channel model is essential for the design and development of RIS communication systems. While deterministic modeling approaches such as ray-tracing (RT) offer significant benefits, a unified model that accommodates all RIS types is still lacking. This paper addresses this gap by developing a high-precision deterministic channel model based on RT, supporting multiple RIS types: reflective, transmissive, hybrid, and three polarization operation modes. To achieve this, a unified EM response model for the aforementioned RIS types is developed. The reflection and transmission coefficients of RIS elements are derived using a tensor-based equivalent impedance approach, followed by calculating the scattered fields of the RIS to establish an EM response model. The performance of different RIS types is compared through simulations in typical scenarios. During this process, passive and lossless constraints on the reflection and transmission coefficients are incorporated to ensure fairness in the performance evaluation. Simulation results validate the framework's accuracy in characterizing the RIS channel, and specific cases tailored for dual-polarization independent control and polarization rotating RISs are highlighted as insights for their future deployment. This work can be helpful for the evaluation and optimization of RIS-enabled wireless communication systems.

Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction

Traditional Chinese medicine, as an essential component of traditional medicine, contains active ingredients that serve as a crucial source for modern drug development, holding immense therapeutic potential and development value. A multi-layered and complex network is formed from Chinese medicine to diseases and used to predict the potential associations between Chinese medicine ingredients and diseases. This study proposes an ingredient-disease association prediction model (Node2Vec-DGI-EL) based on hierarchical graph representation learning. First, the model uses the Node2Vec algorithm to extract node embedding vectors from the network as the initial features of the nodes. Next, the network nodes are deeply represented and learned using the DGI algorithm to enhance the model's expressive power. To improve prediction accuracy and robustness, an ensemble learning method is incorporated to achieve more accurate ingredient-disease association predictions. The effectiveness of the model is then evaluated through a series of theoretical verifications. The results demonstrated that the proposed model significantly outperformed existing methods, achieving an AUC of 0.9987 and an AUPR of 0.9545, thereby indicating superior predictive capability. Ablation experiments further revealed the contribution and importance of each module. Additionally, case studies explored potential associations, such as triptonide with hypertensive retinopathy and methyl ursolate with colorectal cancer. Molecular docking experiments validated these findings, showing the triptonide-PGR interaction and the methyl ursolate-NFE2L2 interaction can bind stable. In conclusion, the Node2Vec-DGI-EL model focuses on TCM datasets and effectively predicts ingredient-disease associations, overcoming the reliance on node semantic information.

Robust Orthogonal NMF with Label Propagation for Image Clustering

Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF methods are highly sensitive to noise corruption and are unable to effectively leverage limited supervised information. To overcome these drawbacks, we propose a unified non-convex framework with label propagation called robust orthogonal nonnegative matrix factorization (RONMF). This method not only considers the graph Laplacian and label propagation as regularization terms but also introduces a more effective non-convex structure to measure the reconstruction error and imposes orthogonal constraints on the basis matrix to reduce the noise corruption, thereby achieving higher robustness. To solve RONMF, we develop an alternating direction method of multipliers (ADMM)-based optimization algorithm. In particular, all subproblems have closed-form solutions, which ensures its efficiency. Experimental evaluations on eight public image datasets demonstrate that the proposed RONMF outperforms state-of-the-art NMF methods across various standard metrics and shows excellent robustness. The code will be available at https://github.com/slinda-liu.

High-Resolution Multipath Angle Estimation Based on Power-Angle-Delay Profile for Directional Scanning Sounding

Directional scanning sounding (DSS) has become widely adopted for high-frequency channel measurements because it effectively compensates for severe path loss. However, the resolution of existing multipath component (MPC) angle estimation methods is constrained by the DSS angle sampling interval. Therefore, this communication proposes a high-resolution MPC angle estimation method based on power-angle-delay profile (PADP) for DSS. By exploiting the mapping relationship between the power difference of adjacent angles in the PADP and MPC offset angle, the resolution of MPC angle estimation is refined, significantly enhancing the accuracy of MPC angle and amplitude estimation without increasing measurement complexity. Numerical simulation results demonstrate that the proposed method reduces the mean squared estimation errors of angle and amplitude by one order of magnitude compared to traditional omnidirectional synthesis methods. Furthermore, the estimation errors approach the Cram\'er-Rao Lower Bounds (CRLBs) derived for wideband DSS, thereby validating its superior performance in MPC angle and amplitude estimation. Finally, experiments conducted in an indoor scenario at 37.5 GHz validate the excellent performance of the proposed method in practical applications.

Research and Experimental Validation for 3GPP ISAC Channel Modeling Standardization

Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an extended GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted in both indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, UAVs, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.

A Survey of New Mid-Band/FR3 for 6G: Channel Measurement, Characterization and Modeling in Outdoor Environment

The new mid-band (6-24 GHz) has attracted significant attention from both academia and industry, which is the spectrum with continuous bandwidth that combines the coverage benefits of low frequency with the capacity advantages of high frequency. Since outdoor environments represent the primary application scenario for mobile communications, this paper presents the first comprehensive review and summary of multi-scenario and multi-frequency channel characteristics based on extensive outdoor new mid-band channel measurement data, including UMa, UMi, and O2I. Specifically, a survey of the progress of the channel characteristics is presented, such as path loss, delay spread, angular spread, channel sparsity, capacity and near-field spatial non-stationary characteristics. Then, considering that satellite communication will be an important component of future communication systems, we examine the impact of clutter loss in air-ground communications. Our analysis of the frequency dependence of mid-band clutter loss suggests that its impact is not significant. Additionally, given that penetration loss is frequency-dependent, we summarize its variation within the FR3 band. Based on experimental results, comparisons with the standard model reveal that while the 3GPP TR 38.901 model remains a useful reference for penetration loss in wood and glass, it shows significant deviations for concrete and glass, indicating the need for further refinement. In summary, the findings of this survey provide both empirical data and theoretical support for the deployment of mid-band in future communication systems, as well as guidance for optimizing mid-band base station deployment in the outdoor environment. This survey offers the reference for improving standard models and advancing channel modeling.