661 research outputs found
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 Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Machine Learning for Unmanned Aerial System (UAS) Networking
Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale.
With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring.
This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS
Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking
The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out
Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting
Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has
aroused. Specifically, UAVs can be used in cellular networks as aerial users
for delivery, surveillance, rescue search, or as an aerial base station (aBS)
for communication with ground users in remote uncovered areas or in dense
environments requiring prompt high capacity. Aiming to satisfy the high
requirements of wireless aerial networks, several multiple access techniques
have been investigated. In particular, space-division multiple access(SDMA) and
power-domain non-orthogonal multiple access (NOMA) present promising
multiplexing gains for aerial downlink and uplink. Nevertheless, these gains
are limited as they depend on the conditions of the environment. Hence, a
generalized scheme has been recently proposed, called rate-splitting multiple
access (RSMA), which is capable of achieving better spectral efficiency gains
compared to SDMA and NOMA. In this paper, we present a comprehensive survey of
key multiple access technologies adopted for aerial networks, where aBSs are
deployed to serve ground users. Since there have been only sporadic results
reported on the use of RSMA in aerial systems, we aim to extend the discussion
on this topic by modelling and analyzing the weighted sum-rate performance of a
two-user downlink network served by an RSMA-based aBS. Finally, related open
issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa
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