Related papers: Space-Air-Ground Integrated Multi-domain Network R…
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs).…
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The future…
Virtualization technologies are the foundation of modern ICT infrastructure, enabling service providers to create dedicated virtual networks (VNs) that can support a wide range of smart city applications. These VNs continuously generate…
In post-disaster space-air-ground integrated networks (SAGINs), terrestrial infrastructure is often impaired, and unmanned aerial vehicles (UAVs) must rapidly restore connectivity for mission-critical ground terminals in cluttered…
In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management…
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands,…
In this letter, we investigate the problem of joint content caching and routing in satellite-terrestrial edge computing networks (STECNs) to improve caching service for geographically distributed users. To handle the challenges arising from…
Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to…
Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and…
In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's reflection for enhancing the network's performance. To aim at…
The space-air-ground integrated network (SAGIN) can enable global range and seamless coverage in the future network. SAGINs consist of three spatial layer network nodes: 1) satellites on the space layer, 2) aerial vehicles on the aerial…
In this paper, a deep reinforcement learning (DRL) method is proposed to address the problem of UAV navigation in an unknown environment. However, DRL algorithms are limited by the data efficiency problem as they typically require a huge…
The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts…
Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the…
After the industrialization of 5G cellular communications, 6G has increasingly become a research hotspot in the academia. Space-Air-Ground Integrated Network (SAGIN) is a key supporting technology for 6G because of its advantages such as…
Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G), particularly in the context of connecting the unconnected and ultraconnecting the connected.…
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and…
Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation…
Due to the rapid growth of heterogeneous wireless networks (HWNs), where devices with diverse communication technologies coexist, there is increasing demand for efficient and adaptive multi-hop routing with multiple data flows. Traditional…
Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles. Whereas recent innovations bring the promise of larger link capacity, their…