1,137 research outputs found
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
Recent technological advancements in space, air and ground components have
made possible a new network paradigm called "space-air-ground integrated
network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs.
However, due to UAVs' high dynamics and complexity, the real-world deployment
of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the
space and terrestrial components, UAVs are expected to meet performance
requirements with high flexibility and dynamics using limited resources.
Therefore, employing UAVs in various usage scenarios requires well-designed
planning in algorithmic approaches. In this paper, we provide a comprehensive
review of recent learning-based algorithmic approaches. We consider possible
reward functions and discuss the state-of-the-art algorithms for optimizing the
reward functions, including Q-learning, deep Q-learning, multi-armed bandit
(MAB), particle swarm optimization (PSO) and satisfaction-based learning
algorithms. Unlike other survey papers, we focus on the methodological
perspective of the optimization problem, which can be applicable to various
UAV-assisted missions on a SAGIN using these algorithms. We simulate users and
environments according to real-world scenarios and compare the learning-based
and PSO-based methods in terms of throughput, load, fairness, computation time,
etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional
(3D) variations of these algorithms to reflect different deployment cases. Our
simulation suggests that the D satisfaction-based learning algorithm
outperforms the other approaches for various metrics in most cases. We discuss
some open challenges at the end and our findings aim to provide design
guidelines for algorithm selections while optimizing the deployment of
UAV-assisted SAGINs.Comment: Submitted to the IEEE Internet of Things Journal in June 202
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
Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks
This paper addresses the efficient management of Mobile Access Points (MAPs),
which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a
two-level hierarchical architecture, which dynamically reconfigures the network
while considering Integrated Access-Backhaul (IAB) constraints. The high-layer
decision process determines the number of MAPs through consensus, and we
develop a joint optimization process to account for co-dependence in network
self-management. In the low-layer, MAPs manage their placement using a
double-attention based Deep Reinforcement Learning (DRL) model that encourages
cooperation without retraining. To improve generalization and reduce
complexity, we propose a federated mechanism for training and sharing one
placement model for every MAP in the low-layer. Additionally, we jointly
optimize the placement and backhaul connectivity of MAPs using a
multi-objective reward function, considering the impact of varying MAP
placement on wireless backhaul connectivity.Comment: 2023 IEEE International Symposium on Personal, Indoor and Mobile
Radio Communications (PIMRC
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
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
- …