318 research outputs found
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
Joint Traffic-Aware UAV Placement and Predictive Routing for Aerial Networks
Aerial networks, composed of Unmanned Aerial Vehicles (UAVs) acting as Wi-Fi
access points or cellular base stations, are emerging as an interesting
solution to provide on-demand wireless connectivity to users, when there is no
network infrastructure available, or to enhance the network capacity. This
article proposes a traffic-aware topology control solution for aerial networks
that holistically combines the placement of UAVs with a predictive and
centralized routing protocol. The synergy created by the combination of the UAV
placement and routing solutions allows the aerial network to seamlessly update
its topology according to the users' traffic demand, whilst minimizing the
disruption caused by the movement of the UAVs. As a result, the Quality of
Service (QoS) provided to the users is improved. The components of the proposed
solution are described and evaluated individually in this article by means of
simulation and an experimental testbed. The results show that all the
components improve the QoS provided to the users when compared to the
corresponding baseline solutions
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
Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach
Nowadays there is a growing research interest on the possibility of enriching
small flying robots with autonomous sensing and online navigation capabilities.
This will enable a large number of applications spanning from remote
surveillance to logistics, smarter cities and emergency aid in hazardous
environments. In this context, an emerging problem is to track unauthorized
small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in
large UAV networks. In contrast with current solutions mainly based on static
and on-ground radars, this paper proposes the idea of a dynamic radar network
of UAVs for real-time and high-accuracy tracking of malicious targets. To this
end, we describe a solution for real-time navigation of UAVs to track a dynamic
target using heterogeneously sensed information. Such information is shared by
the UAVs with their neighbors via multi-hops, allowing tracking the target by a
local Bayesian estimator running at each agent. Since not all the paths are
equal in terms of information gathering point-of-view, the UAVs plan their own
trajectory by minimizing the posterior covariance matrix of the target state
under UAV kinematic and anti-collision constraints. Our results show how a
dynamic network of radars attains better localization results compared to a
fixed configuration and how the on-board sensor technology impacts the accuracy
in tracking a target with different radar cross sections, especially in non
line-of-sight (NLOS) situations
- …