Related papers: System-Level Metrics for Non-Terrestrial Networks …
Satellite networks are expected to support global connectivity and services via future integrated 6G space-terrestrial networks (STNs), as well as private non-geostationary satellite orbit (NGSO) constellations. In the past few years, many…
Graph Neural Networks (GNNs) have excelled in predicting graph properties in various applications ranging from identifying trends in social networks to drug discovery and malware detection. With the abundance of new architectures and…
Non-terrestrial networks (NTNs), including low Earth orbit (LEO) satellites, are expected to play a pivotal role in achieving global coverage for Internet-of-Things (IoT) applications in sixth-generation (6G) systems. Although specific…
With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit…
The landscape of wireless communication systems is evolving rapidly, with a pivotal role envisioned for dynamic network structures and self-organizing networks in upcoming technologies like the 6G mobile communications standard. This…
In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests. In this paper, we propose a novel nonlinear solution through the utilization of deformable geometric…
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The…
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Generally, for a…
With the surging demand for ultra-reliable, low-latency, and ubiquitous connectivity in Sixth-Generation (6G) networks, Non-Terrestrial Networks (NTNs) emerge as a key complement to terrestrial networks by offering flexible access and…
The unprecedented development of non-terrestrial networks (NTN) utilizes the low-altitude airspace for commercial and social flying activities. The integration of NTN and terrestrial networks leads to the emergence of low-altitude economy…
The integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN), including 5G Advanced/6G and the Internet of Things (IoT) technologies, using Low Earth Orbit (LEO) satellites, high-altitude platforms (HAPS), and Unmanned…
The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non-terrestrial connectivity solutions and to…
Beam management is central in the operation of dense 5G cellular networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence…
In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive…
Non-terrestrial network (NTN) is envisioned as a critical component of Sixth Generation (6G) networks by enabling ubiquitous services and enhancing network resilience. However, the inherent mobility and high-altitude operation of NTN pose…
Due to the high altitudes and large beam sizes of satellites, the curvature of the Earth's surface can impact system-level performance. To consider this, 3GPP introduces the UV-plane beam mapping for system-level simulations of…
Non-terrestrial networks (NTNs) have become appealing resolutions for seamless coverage in the next-generation wireless transmission, where a large number of Internet of Things (IoT) devices diversely distributed can be efficiently served.…
This article proposes a generative neural network architecture for spatially consistent air-to-ground channel modeling. The approach considers the trajectories of uncrewed aerial vehicles along typical urban paths, capturing spatial…
Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial…
Neural networks are trained by optimizing multi-dimensional sets of fitting parameters on non-convex loss landscapes. Low-loss regions of the landscapes correspond to the parameter sets that perform well on the training data. A key issue in…