20,277 research outputs found
SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization
Satellite systems face a significant challenge in effectively utilizing
limited communication resources to meet the demands of ground network traffic,
characterized by asymmetrical spatial distribution and time-varying
characteristics. Moreover, the coverage range and signal transmission distance
of low Earth orbit (LEO) satellites are restricted by notable propagation
attenuation, molecular absorption, and space losses in sub-terahertz (THz)
frequencies. This paper introduces a novel approach to maximize LEO satellite
coverage by leveraging reconfigurable intelligent surfaces (RISs) within 6G
sub-THz networks. The optimization objectives encompass enhancing the
end-to-end data rate, optimizing satellite-remote user equipment (RUE)
associations, data packet routing within satellite constellations, RIS phase
shift, and ground base station (GBS) transmit power (i.e., active beamforming).
The formulated joint optimization problem poses significant challenges owing to
its time-varying environment, non-convex characteristics, and NP-hard
complexity. To address these challenges, we propose a block coordinate descent
(BCD) algorithm that integrates balanced K-means clustering, multi-agent
proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and
whale optimization (WOA) techniques. The performance of the proposed approach
is demonstrated through comprehensive simulation results, exhibiting its
superiority over existing baseline methods in the literature
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning
This study introduces a resource allocation framework for integrated
satellite-terrestrial networks to address these challenges. The framework
leverages local cache pool deployments and non-orthogonal multiple access
(NOMA) to reduce time delays and improve energy efficiency. Our proposed
approach utilizes a multi-agent enabled deep deterministic policy gradient
algorithm (MADDPG) to optimize user association, cache design, and transmission
power control, resulting in enhanced energy efficiency. The approach comprises
two phases: User Association and Power Control, where users are treated as
agents, and Cache Optimization, where the satellite (Bs) is considered the
agent. Through extensive simulations, we demonstrate that our approach
surpasses conventional single-agent deep reinforcement learning algorithms in
addressing cache design and resource allocation challenges in integrated
terrestrial-satellite networks. Specifically, our proposed approach achieves
significantly higher energy efficiency and reduced time delays compared to
existing methods.Comment: 16, 1
A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges
Maritime activities represent a major domain of economic growth with several
emerging maritime Internet of Things use cases, such as smart ports, autonomous
navigation, and ocean monitoring systems. The major enabler for this exciting
ecosystem is the provision of broadband, low-delay, and reliable wireless
coverage to the ever-increasing number of vessels, buoys, platforms, sensors,
and actuators. Towards this end, the integration of unmanned aerial vehicles
(UAVs) in maritime communications introduces an aerial dimension to wireless
connectivity going above and beyond current deployments, which are mainly
relying on shore-based base stations with limited coverage and satellite links
with high latency. Considering the potential of UAV-aided wireless
communications, this survey presents the state-of-the-art in UAV-aided maritime
communications, which, in general, are based on both conventional optimization
and machine-learning-aided approaches. More specifically, relevant UAV-based
network architectures are discussed together with the role of their building
blocks. Then, physical-layer, resource management, and cloud/edge computing and
caching UAV-aided solutions in maritime environments are discussed and grouped
based on their performance targets. Moreover, as UAVs are characterized by
flexible deployment with high re-positioning capabilities, studies on UAV
trajectory optimization for maritime applications are thoroughly discussed. In
addition, aiming at shedding light on the current status of real-world
deployments, experimental studies on UAV-aided maritime communications are
presented and implementation details are given. Finally, several important open
issues in the area of UAV-aided maritime communications are given, related to
the integration of sixth generation (6G) advancements
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
High throughput satellites (HTS), with their digital payload technology, are
expected to play a key role as enablers of the upcoming 6G networks. HTS are
mainly designed to provide higher data rates and capacities. Fueled by
technological advancements including beamforming, advanced modulation
techniques, reconfigurable phased array technologies, and electronically
steerable antennas, HTS have emerged as a fundamental component for future
network generation. This paper offers a comprehensive state-of-the-art of HTS
systems, with a focus on standardization, patents, channel multiple access
techniques, routing, load balancing, and the role of software-defined
networking (SDN). In addition, we provide a vision for next-satellite systems
that we named as extremely-HTS (EHTS) toward autonomous satellites supported by
the main requirements and key technologies expected for these systems. The EHTS
system will be designed such that it maximizes spectrum reuse and data rates,
and flexibly steers the capacity to satisfy user demand. We introduce a novel
architecture for future regenerative payloads while summarizing the challenges
imposed by this architecture
NB-IoT via LEO satellites: An efficient resource allocation strategy for uplink data transmission
In this paper, we focus on the use of Low-Eart Orbit (LEO) satellites
providing the Narrowband Internet of Things (NB-IoT) connectivity to the
on-ground user equipment (UEs). Conventional resource allocation algorithms for
the NBIoT systems are particularly designed for terrestrial infrastructures,
where devices are under the coverage of a specific base station and the whole
system varies very slowly in time. The existing methods in the literature
cannot be applied over LEO satellite-based NB-IoT systems for several reasons.
First, with the movement of the LEO satellite, the corresponding channel
parameters for each user will quickly change over time. Delaying the scheduling
of a certain user would result in a resource allocation based on outdated
parameters. Second, the differential Doppler shift, which is a typical
impairment in communications over LEO, directly depends on the relative
distance among users. Scheduling at the same radio frame users that overcome a
certain distance would violate the differential Doppler limit supported by the
NB-IoT standard. Third, the propagation delay over a LEO satellite channel is
around 4-16 times higher compared to a terrestrial system, imposing the need
for message exchange minimization between the users and the base station. In
this work, we propose a novel uplink resource allocation strategy that jointly
incorporates the new design considerations previously mentioned together with
the distinct channel conditions, satellite coverage times and data demands of
various users on Earth. The novel methodology proposed in this paper can act as
a framework for future works in the field.Comment: Tis work has been submitted to the IEEE IoT Journal for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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