CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing with greatly enhanced performance, by offering location-specific channel prior information for future wireless networks. One fundamental problem for CKM-enabled wireless systems lies in how to construct high-quality and complete CKM for all locations of interest, based on only limited and noisy on-site channel knowledge data. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited and noisy observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with learned priors using generative artificial intelligence (AI), we propose CKMDiff, a conditional diffusion model that can be applied to perform various tasks for CKM constructions such as denoising, inpainting, and super-resolution, without having to know the physical environment maps or transceiver locations. Furthermore, we propose an environment-aware data augmentation mechanism to enhance the model's ability to learn implicit relations between electromagnetic propagation patterns and spatial-geometric features. Extensive numerical results are provided based on the CKMImageNet and RadioMapSeer datasets, which demonstrate that the proposed CKMDiff achieves state-of-the-art performance, outperforming various benchmark methods.

Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments

Large language models (LLMs) are being widely applied across various fields, but as tasks become more complex, evaluating their responses is increasingly challenging. Compared to human evaluators, the use of LLMs to support performance evaluation offers a more efficient alternative. However, most studies focus mainly on aligning LLMs' judgments with human preferences, overlooking the existence of biases and mistakes in human judgment. Furthermore, how to select suitable LLM judgments given multiple potential LLM responses remains underexplored. To address these two aforementioned issues, we propose a three-stage meta-judge selection pipeline: 1) developing a comprehensive rubric with GPT-4 and human experts, 2) using three advanced LLM agents to score judgments, and 3) applying a threshold to filter out low-scoring judgments. Compared to methods using a single LLM as both judge and meta-judge, our pipeline introduces multi-agent collaboration and a more comprehensive rubric. Experimental results on the JudgeBench dataset show about 15.55\% improvement compared to raw judgments and about 8.37\% improvement over the single-agent baseline. Our work demonstrates the potential of LLMs as meta-judges and lays the foundation for future research on constructing preference datasets for LLM-as-a-judge reinforcement learning.

Supporting Urban Low-Altitude Economy: Channel Gain Map Inference Based on 3D Conditional GAN

The advancement of advanced air mobility (AAM) in recent years has given rise to the concept of low-altitude economy (LAE). However, the diverse flight activities associated with the emerging LAE applications in urban scenarios confront complex physical environments, which urgently necessitates ubiquitous and reliable communication to guarantee the operation safety of the low-altitude aircraft. As one of promising technologies for the sixth generation (6G) mobile networks, channel knowledge map (CKM) enables the environment-aware communication by constructing a site-specific dataset, thereby providing a priori on-site information for the aircraft to obtain the channel state information (CSI) at arbitrary locations with much reduced online overhead. Diverse base station (BS) deployments in the three-dimensional (3D) urban low-altitude environment require efficient 3D CKM construction to capture spatial channel characteristics with less overhead. Towards this end, this paper proposes a 3D channel gain map (CGM) inference method based on a 3D conditional generative adversarial network (3D-CGAN). Specifically, we first analyze the potential deployment types of BSs in urban low-altitude scenario, and investigate the CGM representation with the corresponding 3D channel gain model. The framework of the proposed 3D-CGAN is then discussed, which is trained by a dataset consisting of existing CGMs. Consequently, the trained 3D-CGAN is capable of inferring the corresponding CGM only based on the BS coordinate without additional measurement. The simulation results demonstrate that the CGMs inferred by the proposed 3D-CGAN outperform those of the benchmark schemes, which can accurately reflect the radio propagation condition in 3D environment.

CKMImageNet: A Dataset for AI-Based Channel Knowledge Map Towards Environment-Aware Communication and Sensing

With the increasing demand for real-time channel state information (CSI) in sixth-generation (6G) mobile communication networks, channel knowledge map (CKM) emerges as a promising technique, offering a site-specific database that enables environment-awareness and significantly enhances communication and sensing performance by leveraging a priori wireless channel knowledge. However, efficient construction and utilization of CKMs require high-quality, massive, and location-specific channel knowledge data that accurately reflects the real-world environments. Inspired by the great success of ImageNet dataset in advancing computer vision and image understanding in artificial intelligence (AI) community, we introduce CKMImageNet, a dataset developed to bridge AI and environment-aware wireless communications and sensing by integrating location-specific channel knowledge data, high-fidelity environmental maps, and their visual representations. CKMImageNet supports a wide range of AI-driven approaches for CKM construction with spatially consistent and location-specific channel knowledge data, including both supervised and unsupervised, as well as discriminative and generative AI methods.

Ultrasound-Guided Robotic Blood Drawing and In Vivo Studies on Submillimetre Vessels of Rats

Billions of vascular access procedures are performed annually worldwide, serving as a crucial first step in various clinical diagnostic and therapeutic procedures. For pediatric or elderly individuals, whose vessels are small in size (typically 2 to 3 mm in diameter for adults and less than 1 mm in children), vascular access can be highly challenging. This study presents an image-guided robotic system aimed at enhancing the accuracy of difficult vascular access procedures. The system integrates a 6-DoF robotic arm with a 3-DoF end-effector, ensuring precise navigation and needle insertion. Multi-modal imaging and sensing technologies have been utilized to endow the medical robot with precision and safety, while ultrasound imaging guidance is specifically evaluated in this study. To evaluate in vivo vascular access in submillimeter vessels, we conducted ultrasound-guided robotic blood drawing on the tail veins (with a diameter of 0.7 plus or minus 0.2 mm) of 40 rats. The results demonstrate that the system achieved a first-attempt success rate of 95 percent. The high first-attempt success rate in intravenous vascular access, even with small blood vessels, demonstrates the system's effectiveness in performing these procedures. This capability reduces the risk of failed attempts, minimizes patient discomfort, and enhances clinical efficiency.

Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation

The imputation of the Multivariate time series (MTS) is particularly challenging since the MTS typically contains irregular patterns of missing values due to various factors such as instrument failures, interference from irrelevant data, and privacy regulations. Existing statistical methods and deep learning methods have shown promising results in time series imputation. In this paper, we propose a Temporal Gaussian Copula Model (TGC) for three-order MTS imputation. The key idea is to leverage the Gaussian Copula to explore the cross-variable and temporal relationships based on the latent Gaussian representation. Subsequently, we employ an Expectation-Maximization (EM) algorithm to improve robustness in managing data with varying missing rates. Comprehensive experiments were conducted on three real-world MTS datasets. The results demonstrate that our TGC substantially outperforms the state-of-the-art imputation methods. Additionally, the TGC model exhibits stronger robustness to the varying missing ratios in the test dataset. Our code is available at https://github.com/MVL-Lab/TGC-MTS.

EMO: Edge Model Overlays to Scale Model Size in Federated Learning

Federated Learning (FL) trains machine learning models on edge devices with distributed data. However, the computational and memory limitations of these devices restrict the training of large models using FL. Split Federated Learning (SFL) addresses this challenge by distributing the model across the device and server, but it introduces a tightly coupled data flow, leading to computational bottlenecks and high communication costs. We propose EMO as a solution to enable the training of large models in FL while mitigating the challenges of SFL. EMO introduces Edge Model Overlay(s) between the device and server, enabling the creation of a larger ensemble model without modifying the FL workflow. The key innovation in EMO is Augmented Federated Learning (AFL), which builds an ensemble model by connecting the original (smaller) FL model with model(s) trained in the overlay(s) to facilitate horizontal or vertical scaling. This is accomplished through three key modules: a hierarchical activation replay cache to decouple AFL from FL, a convergence-aware communication controller to optimize communication overhead, and an ensemble inference module. Evaluations on a real-world prototype show that EMO improves accuracy by up to 17.77% compared to FL, and reduces communication costs by up to 7.17x and decreases training time by up to 6.9x compared to SFL.

Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization

Selective retrieval improves retrieval-augmented generation (RAG) by reducing distractions from low-quality retrievals and improving efficiency. However, existing approaches under-utilize the inherent knowledge of large language models (LLMs), leading to suboptimal retrieval decisions and degraded generation performance. To bridge this gap, we propose Self-Routing RAG (SR-RAG), a novel framework that binds selective retrieval with knowledge verbalization. SR-RAG enables an LLM to dynamically decide between external retrieval and verbalizing its own parametric knowledge. To this end, we design a multi-task objective that jointly optimizes an LLM on knowledge source selection, knowledge verbalization, and response generation. We further introduce dynamic knowledge source inference via nearest neighbor search to improve the accuracy of knowledge source decision under domain shifts. Fine-tuning three LLMs with SR-RAG significantly improves both their response accuracy and inference latency. Compared to the strongest selective retrieval baseline, SR-RAG reduces retrievals by 29% while improving the performance by 5.1%.

Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-specific areas like telecommunications, which demands specialized expertise and adaptability to evolving standards. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain. The framework leverages a KG to capture structured, domain-specific information about network protocols, standards, and other telecom-related entities, comprehensively representing their relationships. By integrating KG with RAG, LLMs can dynamically access and utilize the most relevant and up-to-date knowledge during response generation. This hybrid approach bridges the gap between structured knowledge representation and the generative capabilities of LLMs, significantly enhancing accuracy, adaptability, and domain-specific comprehension. Our results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision. The proposed KG-RAG model attained an accuracy of 88% for question answering tasks on a frequently used telecom-specific dataset, compared to 82% for the RAG-only and 48% for the LLM-only approaches.

Sampling Innovation-Based Adaptive Compressive Sensing

Scene-aware Adaptive Compressive Sensing (ACS) has attracted significant interest due to its promising capability for efficient and high-fidelity acquisition of scene images. ACS typically prescribes adaptive sampling allocation (ASA) based on previous samples in the absence of ground truth. However, when confronting unknown scenes, existing ACS methods often lack accurate judgment and robust feedback mechanisms for ASA, thus limiting the high-fidelity sensing of the scene. In this paper, we introduce a Sampling Innovation-Based ACS (SIB-ACS) method that can effectively identify and allocate sampling to challenging image reconstruction areas, culminating in high-fidelity image reconstruction. An innovation criterion is proposed to judge ASA by predicting the decrease in image reconstruction error attributable to sampling increments, thereby directing more samples towards regions where the reconstruction error diminishes significantly. A sampling innovation-guided multi-stage adaptive sampling (AS) framework is proposed, which iteratively refines the ASA through a multi-stage feedback process. For image reconstruction, we propose a Principal Component Compressed Domain Network (PCCD-Net), which efficiently and faithfully reconstructs images under AS scenarios. Extensive experiments demonstrate that the proposed SIB-ACS method significantly outperforms the state-of-the-art methods in terms of image reconstruction fidelity and visual effects. Codes are available at https://github.com/giant-pandada/SIB-ACS_CVPR2025.