Related papers: UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with P…
Radio maps (RMs), which provide location-based pathloss estimations, are fundamental to enabling proactive, environment-aware communication in 6G networks. However, existing deep learning-based methods for RM construction often model…
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-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…
Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both…
Machine learning tools are finding interesting applications in millimeter wave (mmWave) and massive MIMO systems. This is mainly thanks to their powerful capabilities in learning unknown models and tackling hard optimization problems. To…
Future 6G networks will host massive numbers of embodied intelligent agents, which require real-time channel awareness over continuous-space for autonomous decision-making. By pre-obtaining location-specific channel state information (CSI),…
The integration of artificial intelligence into next-generation wireless networks necessitates the accurate construction of radio maps (RMs) as a foundational prerequisite for electromagnetic digital twins. A RM provides the digital…
Research on sixth-generation (6G) integrated sensing and communication (ISAC) increasingly depends on multimodal datasets. These datasets need to jointly characterize wireless propagation, onboard sensing, and platform mobility. Existing…
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using…
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene geometries is now relatively straightforward, determining…
Machine learning (ML) has greatly advanced data-driven channel modeling and resource optimization in wireless communication systems. However, most existing ML-based methods rely on large, accurately labeled datasets with location…
The upper 6 GHz (U6G) band with XL-MIMO is a key enabler for sixth-generation wireless systems, yet intelligent radiomap prediction for such systems remains challenging. Existing datasets support only small-scale arrays (up to 8x8) with…
Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems…
The convergence of artificial intelligence (AI) and sixth-generation (6G) wireless technologies is driving an urgent need for large-scale, high-fidelity, and reproducible radio frequency (RF) datasets. Existing resources, such as…
In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel…
Channel state information (CSI) acquisition is a challenging problem in massive multiple-input multiple-output (MIMO) networks. Radio maps provide a promising solution for radio resource management by reducing online CSI acquisition.…
Massive multiple-input multiple-output (MIMO) systems offer significant potential to enhance wireless communication performance, yet accurate and timely channel state information (CSI) acquisition remains a key challenge. Existing works on…
Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize…
In the sixth generation (6G) wireless communication networks, the device density, antenna number, and the complexity of communication scenarios will significantly increase, which brings great challenges for system design and network…
Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF…