Related papers: TransfoREM: Transformer aided 3D Radio Environment…
A Radio Environment Map (REM) is a powerful tool in enhancing the experience of radio-enabled agents but building such a REM can be a laborious undertaking, especially in three dimensions. This project shows how such a REM of an indoor…
Spectrum sensing and the generation of 3D Radio Environment Maps (REMs) are essential for enabling spectrum sharing within cognitive radio networks. While Uncrewed Aerial Vehicles (UAVs) offer high-mobility 3D sensing, REM accuracy is…
Precise aerial radio environment characterization is vital for low-altitude planning. However, existing datasets and estimation methods lack the high-resolution granularity required for complex aerial spaces. Additionally, current schemes…
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new…
The enabling of safe cellular controlled unmanned aerial vehicle (UAV) beyond visual line of sight is expected to open important future opportunities in the area of transportation, goods delivery, and system monitoring. A key challenge in…
Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is…
Radio Environmental Maps (REMs) are a powerful tool for enhancing the performance of various communication and networked agents. However, generating REMs is a laborious undertaking, especially in complex 3-Dimensional (3D) environments,…
Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation…
With the emergence of wireless applications in three-dimensional environments, such as the low-altitude airspace and 3D heterogeneous networks, radio map estimation is increasingly required to characterize signal propagation across both…
Low-altitude wireless networks (LAWN) are rapidly expanding with the growing deployment of unmanned aerial vehicles (UAVs) for logistics, surveillance, and emergency response. Reliable connectivity remains a critical yet challenging task…
This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground…
The expansion of the low-altitude economy is contingent on reliable cellular connectivity for unmanned aerial vehicles (UAVs). A key challenge in pre-flight planning is predicting communication link quality along proposed and pre-defined…
Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progress, most REM construction methods remain passive, relying on…
The usage of beamforming in Unmanned Aerial Vehicles (UAVs) has the potential of significantly improving the air-to-ground link quality. This paper presents the outcome of experimental trial of such a UAV-based beamforming system over live…
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet still in the context of wireless networks, drones have also…
Several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measurement campaigns, inaccurate statistical models, or…
Cellular-connected unmanned aerial vehicles (UAVs) represent a promising technology for extending the coverage of 5G and 6G networks in a cost-effective manner. Additionally, Massive multiple-input multiple-output (MIMO) serves as an…
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…
Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission…
Enabling technologies of 5G and beyond wireless communication networks, such as millimeter-wave communication, beamforming, and multiple-input multiple-output (MIMO) antenna systems, are becoming increasingly dependent on accurate…