Diffusion Models-Aided Uplink Channel Estimation for RIS-Assisted Systems

This letter proposes a channel estimation method for reconfigurable intelligent surface (RIS)-assisted systems through a novel diffusion model (DM) framework. We reformulate the channel estimation problem as a denoising process, which aligns with the reverse process of the DM. To overcome the inherent randomness in the reverse process of conventional DM approaches, we adopt a deterministic sampling strategy with a step alignment mechanism that ensures the accuracy of channel estimation while adapting to different signal-to-noise ratio (SNR). Furthermore, to reduce the number of parameters of the U-Net, we meticulously design a lightweight network that achieves comparable performance, thereby enhancing the practicality of our proposed method. Extensive simulations demonstrate superior performance over a wide range of SNRs compared to baselines. For instance, the proposed method achieves performance improvements of up to 13.5 dB in normalized mean square error (NMSE) at SNR = 0 dB. Notably, the proposed lightweight network exhibits almost no performance loss compared to the original U-Net, while requiring only 6.59\% of its parameters.

Fluid Antenna-Empowered Receive Spatial Modulation

Fluid antenna (FA), as an emerging antenna technology, fully exploits spatial diversity. This paper integrates FA with the receive spatial modulation (RSM) scheme and proposes a novel FA-empowered RSM (FA-RSM) system. In this system, the transmitter is equipped with an FA that simultaneously activates multiple ports to transmit precoded signals. We address three key challenges in the FA-RSM system: port selection, theoretical analysis, and detection. First, for port selection, an optimal algorithm from a capacity maximization perspective are proposed, followed by two low-complexity alternatives. Second, for theoretical analysis, performance evaluation metrics are provided for port selection, which demonstrate that increasing the number of activated ports enhances system performance. Third, regarding detection, two low-complexity detectors are proposed. Simulation results confirm that the FA-RSM system significantly outperforms the conventional RSM system. The proposed low-complexity port selection algorithms facilitate minimal performance degradation. Moreover, while activating additional ports improves performance, the gain gradually saturates due to inherent spatial correlation, highlighting the importance of effective port selection in reducing system complexity and cost. Finally, both proposed detectors achieve near-optimal detection performance with low computational complexity, emphasizing the receiver-friendly nature of the FA-RSM system.

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?

Vision-language models (VLMs) exhibit strong zero-shot generalization on natural images and show early promise in interpretable medical image analysis. However, existing benchmarks do not systematically evaluate whether these models truly reason like human clinicians or merely imitate superficial patterns. To address this gap, we propose DrVD-Bench, the first multimodal benchmark for clinical visual reasoning. DrVD-Bench consists of three modules: Visual Evidence Comprehension, Reasoning Trajectory Assessment, and Report Generation Evaluation, comprising a total of 7,789 image-question pairs. Our benchmark covers 20 task types, 17 diagnostic categories, and five imaging modalities-CT, MRI, ultrasound, radiography, and pathology. DrVD-Bench is explicitly structured to reflect the clinical reasoning workflow from modality recognition to lesion identification and diagnosis. We benchmark 19 VLMs, including general-purpose and medical-specific, open-source and proprietary models, and observe that performance drops sharply as reasoning complexity increases. While some models begin to exhibit traces of human-like reasoning, they often still rely on shortcut correlations rather than grounded visual understanding. DrVD-Bench offers a rigorous and structured evaluation framework to guide the development of clinically trustworthy VLMs.

Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation

Movable antenna (MA) has shown significant potential for improving the performance of integrated sensing and communication (ISAC) systems. In this paper, we model an MA-aided ISAC system operating in a communication full-duplex mono-static sensing framework. The self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. We develop an optimization problem to maximize the weighted sum of downlink and uplink communication rates alongside the mutual information relevant to the sensing task. To address this highly non-convex problem, we employ the fractional programming (FP) method and propose an alternating optimization (AO)-based algorithm that jointly optimizes the beamforming, user power allocation, and antenna positions at the transceivers. Given the sensitivity of the AO-based algorithm to the initial antenna positions, a PSO-based algorithm is proposed to explore superior sub-optimal antenna positions within the feasible region. Numerical results indicate that the proposed algorithms enable the MA system to effectively leverage the antenna position flexibility for accurate beamforming in a complex ISAC scenario. This enhances the system's self-interference cancellation (SIC) capabilities and markedly improves its overall performance and reliability compared to conventional fixed-position antenna designs.

AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model

Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Building on our prior work in communication multi-modal alignment and telecom large language model (LLM), we propose a scalable, task-aware artificial intelligence-air interface multi-modal universal model (AI2MMUM), which flexibility and effectively perform various physical layer tasks according to subtle task instructions. The LLM backbone provides robust contextual comprehension and generalization capabilities, while a fine-tuning approach is adopted to incorporate domain-specific knowledge. To enhance task adaptability, task instructions consist of fixed task keywords and learnable, implicit prefix prompts. Frozen radio modality encoders extract universal representations and adapter layers subsequently bridge radio and language modalities. Moreover, lightweight task-specific heads are designed to directly output task objectives. Comprehensive evaluations demonstrate that AI2MMUM achieves SOTA performance across five representative physical environment/wireless channel-based downstream tasks using the WAIR-D and DeepMIMO datasets.

Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm...

Downlink OFDM-FAMA in 5G-NR Systems

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system's achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.

Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning

Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSSL), which utilizes a diffusion model (DM) to generate synthetic data, bridging the gap between heterogeneous local data distributions and the global data distribution. In DDSA-FSSL, clients address the challenge of the scarcity of labeled data by employing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. This process allows clients to generate more comprehensive synthetic datasets aligned with the global distribution. Extensive experiments conducted on multiple datasets and varying non-IID distributions demonstrate the effectiveness of DDSA-FSSL, e.g., it improves accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

Fluid Antenna Grouping-Based Index Modulation: Transceiver Design for MIMO Systems

The fluid antenna (FA)-enabled multiple-input multiple-output (MIMO) system based on index modulation (IM), referred to as FA-IM, significantly enhances spectral efficiency (SE) compared to the conventional FA-assisted MIMO system. This paper proposes an innovative FA grouping-based IM (FAG-IM) system to improve performance in mitigating the high spatial correlation between multiple activated ports. A block grouping scheme is employed based on the spatial correlation model and the distribution structure of the ports. Then, a closed-form expression for the average bit error probability (ABEP) upper bound of the FAG-IM system is derived. In order to reduce the receiver complexity of the proposed system, the message passing mechanism is first incorporated into the FAG-IM system. Subsequently, within the approximate message passing (AMP) framework, an efficient structured AMP (S-AMP) detector is devised by leveraging the structural characteristics of the transmission signal vector. Simulation results confirm that the proposed FAG-IM system significantly outperforms the existing FA-IM system in the presence of spatial correlation. The derived ABEP curve aligns well with the numerical results, providing an efficient theoretical tool for evaluating the system performance. Additionally, simulation results demonstrate that the proposed low-complexity S-AMP detector not only reduces the time complexity to a linear scale but also substantially improves bit error rate (BER) performance compared to the minimum mean square error (MMSE) detector, thus facilitating the practical implementation of the FAG-IM system.

Decentralized Hybrid Precoding for Massive MU-MIMO ISAC

Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramer-Rao bound (CRB) as an objective function.Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on ThomlinsonHarashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.