arXiv subjects

Yuelong Qiu

Publications and source records attributed to Yuelong Qiu.

6 recordsLinked to original sources

arXiv2026

WiWorld-RealData: A Real-World Multi-Modal Dataset for 6G Wireless World Models

As sixth-generation wireless systems evolve from reliable connectivity toward environment intelligence, wireless world models aim to learn how physical environments and user states affect wireless propagation, requiring real-world data with explicit correspondences between channel responses and environment observations. However, existing channel-environment datasets are predominantly simulation-based or designed for specific communication tasks, limiting their support for general environment-channel relationship learning. To address this gap, we construct WiWorld-RealData, a real-world multi-band channel and multi-modal environment sensing dataset for 6G wireless world model research. It provides synchronized channel impulse responses measured at 3.7 and 6.775 GHz together with multi-view and panoramic images, light detection and ranging point clouds, millimeter-wave radar observations, and global navigation satellite system trajectories. Unified timestamps, sample identifiers, and metadata establish sample-level correspondences across these heterogeneous modalities. The overall measurement campaign produced approximately 10 TB of data, while the current public release provides aligned channel-environment samples from a representative continuous outdoor route. A path-loss prediction case study further validates the dataset using a continuous test route segment, achieving a mean absolute error of 2.02 dB and a root mean square error of 2.69 dB under few-shot adaptation. WiWorld-RealData supports cross-band propagation analysis, environment-aware channel modeling, wireless digital twins, and channel foundation model research. The dataset is available at https://scc.bupt.edu.cn/dataset-manage/datasets/44 and https://doi.org/10.57760/sciencedb.40663.

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arXiv2026

Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface

As 6G advances, ubiquitous connectivity and higher capacity requirements of the air interface pose substantial challenges for accurate and real-time wireless channel acquisition in diverse environments. Conventional statistical channel modeling relies on offline measurement data from limited environments, struggling to support online applications facing diverse environments. To this end, the digital twin channel (DTC) has emerged as a novel paradigm that constructs a digital replica of the physical environment through high-fidelity sensing and predicts corresponding channel in real time utilizing artificial intelligence (AI) models. As the engine of DTC, existing AI models struggle to simultaneously achieve strong environmental generalization in real-world and end-to-end channel prediction for real time tasks. Therefore, this paper proposes a channel large model (ChannelLM)-driven DTC architecture comprising three modules: low-complexity and high-accuracy environment reconstruction based on dynamic object detection and multimodal alignment of image and point cloud data, physically interpretable environment feature extraction, and a ChannelLM core to mapping these features into generalized environment representations for multi-task channel prediction. Simulation results demonstrate that, in unseen test environments, compared with small-scale AI models, ChannelLM reduces prediction errors by 4.23 dB in channel state information prediction while achieving an end-to-end inference latency of 70 milliseconds in the real world.

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arXiv2025

Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness

To meet the robust and high-speed communication requirements of the sixth-generation (6G) mobile communication system in complex scenarios, sensing- and artificial intelligence (AI)-based digital twin channel (DTC) techniques become a promising approach to reduce system overhead. In this paper, we propose an environment-specific channel subspace basis (ECB)-aided partial-to-whole channel state information (CSI) prediction method (ECB-P2WCP) for realizing DTC-enabled low-overhead channel prediction. Specifically, we introduce a wireless environment knowledge (WEK) construction method that extracts ECB from the digital twin environment via subspace estimation. This ECB characterizes the static statistical properties of the electromagnetic environment and serves as environment information prior to the prediction task. Then, we fuse ECB with real-time estimated local CSI to predict the entire spatial-frequency domain channel for both the present and future time instances. Hence, an ECB-based partial-to-whole CSI prediction network (ECB-P2WNet) is designed to achieve a robust channel prediction scheme in various complex scenarios. Simulation results indicate that incorporating ECB provides significant benefits under low signal-to-noise ratio and pilot ratio conditions, achieving a reduction of up to 50\% in pilot overhead. Additionally, the proposed method maintains robustness against multi-user interference, tolerating 3-meter localization errors with only a 0.5 dB normalized mean square error increase, and predicts CSI for the next channel coherent time within 1.3 milliseconds.

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arXiv2025

AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM

Accurate and efficient acquisition of wireless channel state information (CSI) is crucial to enhance the communication performance of wireless systems. However, with the continuous densification of wireless links, increased channel dimensions, and the use of higher-frequency bands, channel estimation in the sixth generation (6G) and beyond wireless networks faces new challenges, such as insufficient orthogonal pilot sequences, inadequate signal-to-noise ratio (SNR) for channel training, and more sophisticated channel statistical distributions in complex environment. These challenges pose significant difficulties for classical channel estimation algorithms like least squares (LS) and maximum a posteriori (MAP). To address this problem, we propose a novel environment-aware channel estimation framework with location-specific prior channel distribution enabled by the new concept of channel knowledge map (CKM). To this end, we propose a new type of CKM called channel score function map (CSFM), which learns the channel probability density function (PDF) using artificial intelligence (AI) techniques. To fully exploit the prior information in CSFM, we propose a plug-and-play (PnP) based algorithm to decouple the regularized MAP channel estimation problem, thereby reducing the complexity of the optimization process. Besides, we employ Tweedie's formula to establish a connection between the channel score function, defined as the logarithmic gradient of the channel PDF, and the channel denoiser. This allows the use of the high-precision, environment-aware channel denoiser from the CSFM to approximate the channel score function, thus enabling efficient processing of the decoupled channel statistical components. Simulation results show that the proposed CSFM-PnP based channel estimation technique significantly outperforms the conventional techniques in the aforementioned challenging scenarios.

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arXiv2025

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.

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arXiv2024

CKMImageNet: A Comprehensive Dataset to Enable Channel Knowledge Map Construction via Computer Vision

Environment-aware communication and sensing is one of the promising paradigm shifts towards 6G, which fully leverages prior information of the local wireless environment to optimize network performance. One of the key enablers for environment-aware communication and sensing is channel knowledge map (CKM), which provides location-specific channel knowledge that is crucial for channel state information (CSI) acquisition. To support the efficient construction of CKM, large-scale location-specific channel data is essential. However, most existing channel datasets do not have the location information nor visual representations of channel data, making them inadequate for exploring the intrinsic relationship between the channel knowledge and the local environment, nor for applying advanced artificial intelligence (AI) algorithms such as computer vision (CV) for CKM construction. To address such issues, in this paper, a large-scale dataset named CKMImageNet is established, which can provide both location-tagged numerical channel data and visual images, providing a holistic view of the channel and environment. Built using commercial ray tracing software, CKMImageNet captures electromagnetic wave propagation in different scenarios, revealing the relationships between location, environment and channel knowledge. By integrating detailed channel data and the corresponding image, CKMImageNet not only supports the verification of various communication and sensing algorithms, but also enables CKM construction with CV algorithms.

cs.IT↗