71 research outputs found
Satellite-MEC Integration for 6G Internet of Things: Minimal Structures, Advances, and Prospects
The sixth-generation (6G) network is envisioned to shift its focus from the
service requirements of human beings' to those of Internet-of-Things (IoT)
devices'. Satellite communications are indispensable in 6G to support IoT
devices operating in rural or disastrous areas. However, satellite networks
face the inherent challenges of low data rate and large latency, which may not
support computation-intensive and delay-sensitive IoT applications. Mobile Edge
Computing (MEC) is a burgeoning paradigm by extending cloud computing
capabilities to the network edge. By utilizing MEC technologies, the
resource-limited IoT devices can access abundant computation resources with low
latency, which enables the highly demanding applications while meeting strict
delay requirements. Therefore, an integration of satellite communications and
MEC technologies is necessary to better enable 6G IoT. In this survey, we
provide a holistic overview of satellite-MEC integration. We first discuss the
main challenges of the integrated satellite-MEC network and propose three
minimal integrating structures. For each minimal structure, we summarize the
current advances in terms of their research topics, after which we discuss the
lessons learned and future directions of the minimal structure. Finally, we
outline potential research issues to envision a more intelligent, more secure,
and greener integrated satellite-MEC network
Caching at the Edge with Fountain Codes
We address the use of linear randon fountain codes caching schemes in a
heterogeneous satellite network. We consider a system composed of multiple hubs
and a geostationary Earth orbit satellite. Coded content is memorized in hubs'
caches in order to serve immediately the user requests and reduce the usage of
the satellite backhaul link. We derive the analytical expression of the average
backhaul rate, as well as a tight upper bound to it with a simple expression.
Furthermore, we derive the optimal caching strategy which minimizes the average
backhaul rate and compare the performance of the linear random fountain code
scheme to that of a scheme using maximum distance separable codes. Our
simulation results indicate that the performance obtained using fountain codes
is similar to that of maximum distance separable codes
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
A High Altitude Platform Station (HAPS) is a network node that operates in
the stratosphere at an of altitude around 20 km and is instrumental for
providing communication services. Precipitated by technological innovations in
the areas of autonomous avionics, array antennas, solar panel efficiency
levels, and battery energy densities, and fueled by flourishing industry
ecosystems, the HAPS has emerged as an indispensable component of
next-generations of wireless networks. In this article, we provide a vision and
framework for the HAPS networks of the future supported by a comprehensive and
state-of-the-art literature review. We highlight the unrealized potential of
HAPS systems and elaborate on their unique ability to serve metropolitan areas.
The latest advancements and promising technologies in the HAPS energy and
payload systems are discussed. The integration of the emerging Reconfigurable
Smart Surface (RSS) technology in the communications payload of HAPS systems
for providing a cost-effective deployment is proposed. A detailed overview of
the radio resource management in HAPS systems is presented along with
synergistic physical layer techniques, including Faster-Than-Nyquist (FTN)
signaling. Numerous aspects of handoff management in HAPS systems are
described. The notable contributions of Artificial Intelligence (AI) in HAPS,
including machine learning in the design, topology management, handoff, and
resource allocation aspects are emphasized. The extensive overview of the
literature we provide is crucial for substantiating our vision that depicts the
expected deployment opportunities and challenges in the next 10 years
(next-generation networks), as well as in the subsequent 10 years
(next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial
A Survey on Non-Geostationary Satellite Systems: The Communication Perspective
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 support a wide range of digital technologies from various industries. NGSO communication systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary orbit (GSO) satellites, which can potentially enable latency-critical applications to be provided through satellites. NGSO promises a substantial boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilization. The promised improvements of NGSO systems have motivated this paper to provide a comprehensive survey of the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. Beyond this, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are also discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Finally, we outline a set of innovative research directions and new opportunities for future NGSO research
Content Caching and Delivery in Heterogeneous Vehicular Networks
Connected and automated vehicles (CAVs), which enable information exchange and content delivery in real time, are expected to revolutionize current transportation systems for better driving safety, traffic efficiency, and environmental sustainability. However, the emerging CAV applications such as content delivery pose stringent requirements on latency, throughput, reliability, and global connectivity. The current wireless networks face significant challenges to satisfy the requirements due to scarce radio spectrum resources, inflexibility to dynamic traffic demands, and geographic-constrained fixed infrastructure deployment. To empower multifarious CAV content delivery, heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial networks formed by unmanned aerial vehicles (UAVs) and space networks constituting of low Earth orbit (LEO) satellites, can guarantee reliable, flexible, cost-effective, and globally seamless service provisioning. In addition, edge caching is a promising solution to facilitate content delivery by caching popular files in the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. The main technical issues are: 1) to fully reveal the potential of HetVNets for content delivery performance enhancement, content caching scheme design in HetVNets should jointly consider network characteristics, vehicle mobility patterns, content popularity, and APs’ caching capacities; 2) to fully exploit the controllable mobility and agility of UAVs to support dynamic vehicular content demands, the caching scheme and trajectory design for UAVs should be jointly optimized, which has not been well addressed due to their intricate inter-coupling relationships; and 3) for caching-based content delivery in HetVNets, a cooperative content delivery scheme should be designed to enable the cooperation among different network segments with ingenious utilization of heterogeneous network resources.
In this thesis, we design the content caching and delivery schemes in the caching-enabled HetVNet to address the three technical issues. First, we study the content caching in HetVNets with fixed terrestrial APs including cellular base stations (CBSs), Wi-Fi roadside units (RSUs), and TV white space (TVWS) stations. To characterize the intermittent network connection caused by limited network coverage and high vehicle mobility, we establish an on-off model with service interruptions to describe the vehicular content delivery process. Content coding then is leveraged to resist the impact of unstable network connections and enhance caching efficiency. By jointly considering file characteristics and network conditions, the content placement is formulated as an integer linear programming (ILP) problem. Adopting the idea of the student admission model, the ILP problem is then transformed into a many-to-one matching problem between content files and HetVNet APs and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with a low complexity. Second, UAV-aided caching is considered to assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching, content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem. Specifically, we first devise a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content caching policy, we design a time-based graph decomposition method to jointly optimize content delivery and UAV trajectory, with which we then leverage the particle swarm optimization algorithm to optimize the content caching. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make online decisions. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely inferences. Extensive trace-driven experiments are conducted to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated vehicular networks (SAGVNs), where vehicular content requests can be cooperatively served by multiple APs in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay compared to the benchmark schemes.
In summary, we have investigated the content caching in terrestrial networks with fixed APs, joint caching and trajectory optimization in the AGVN, and caching-assisted cooperative content delivery in the SAGVN. The proposed schemes and theoretical results should provide useful guidelines for future research in the caching scheme design and efficient utilization of network resources in caching-enabled heterogeneous wireless networks
A Survey on Applications of Cache-Aided NOMA
Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the future network requirements (5G and beyond systems), such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of the cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize the recent related literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well
Near-Space Communications: the Last Piece of 6G Space-Air-Ground-Sea Integrated Network Puzzle
This article presents a comprehensive study on the emerging near-space
communications (NS-COM) within the context of space-air-ground-sea integrated
network (SAGSIN). Specifically, we firstly explore the recent technical
developments of NS-COM, followed by the discussions about motivations behind
integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM,
a comparative analysis between the NS-COM network and other counterparts in
SAGSIN is conducted, covering aspects of deployment, coverage, channel
characteristics and unique problems of NS-COM network. Afterwards, the
technical aspects of NS-COM, including channel modeling, random access, channel
estimation, array-based beam management and joint network optimization, are
examined in detail. Furthermore, we explore the potential applications of
NS-COM, such as structural expansion in SAGSIN communication, civil aviation
communication, remote and urgent communication, weather monitoring and carbon
neutrality. Finally, some promising research avenues are identified, including
stratospheric satellite (StratoSat) -to-ground direct links for mobile
terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic
MIMO, federated learning in NS-COM networks, maritime communication,
electromagnetic spectrum sensing and adversarial game, integrated sensing and
communications, StratoSat-based radar detection and imaging, NS-COM assisted
enhanced global navigation system, NS-COM assisted intelligent unmanned system
and free space optical (FSO) communication. Overall, this paper highlights that
the NS-COM plays an indispensable role in the SAGSIN puzzle, providing
substantial performance and coverage enhancement to the traditional SAGSIN
architecture.Comment: 28 pages, 8 figures, 2 table
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th
Generation (6G) networks, providing ubiquitous, continuous, and scalable
services. Satellites emerge as the primary enabler for NTN, leveraging their
extensive coverage, stable orbits, scalability, and adherence to international
regulations. However, satellite-based NTN presents unique challenges, including
long propagation delay, high Doppler shift, frequent handovers, spectrum
sharing complexities, and intricate beam and resource allocation, among others.
The integration of NTNs into existing terrestrial networks in 6G introduces a
range of novel challenges, including task offloading, network routing, network
slicing, and many more. To tackle all these obstacles, this paper proposes
Artificial Intelligence (AI) as a promising solution, harnessing its ability to
capture intricate correlations among diverse network parameters. We begin by
providing a comprehensive background on NTN and AI, highlighting the potential
of AI techniques in addressing various NTN challenges. Next, we present an
overview of existing works, emphasizing AI as an enabling tool for
satellite-based NTN, and explore potential research directions. Furthermore, we
discuss ongoing research efforts that aim to enable AI in satellite-based NTN
through software-defined implementations, while also discussing the associated
challenges. Finally, we conclude by providing insights and recommendations for
enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve
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