835 research outputs found
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A review of relay network on UAVS for enhanced connectivity
One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented
IRS-assisted UAV Communications: A Comprehensive Review
Intelligent reflecting surface (IRS) can smartly adjust the wavefronts in
terms of phase, frequency, amplitude and polarization via passive reflections
and without any need of radio frequency (RF) chains. It is envisaged as an
emerging technology which can change wireless communication to improve both
energy and spectrum efficiencies with low energy consumption and low cost. It
can intelligently configure the wireless channels through a massive number of
cost effective passive reflecting elements to improve the system performance.
Similarly, unmanned aerial vehicle (UAV) communication has gained a viable
attention due to flexible deployment, high mobility and ease of integration
with several technologies. However, UAV communication is prone to security
issues and obstructions in real-time applications. Recently, it is foreseen
that UAV and IRS both can integrate together to attain unparalleled
capabilities in difficult scenarios. Both technologies can ensure improved
performance through proactively altering the wireless propagation using smart
signal reflections and maneuver control in three dimensional (3D) space. IRS
can be integrated in both aerial and terrene environments to reap the benefits
of smart reflections. This study briefly discusses UAV communication, IRS and
focuses on IRS-assisted UAC communications. It surveys the existing literature
on this emerging research topic and highlights several promising technologies
which can be implemented in IRS-assisted UAV communication. This study also
presents several application scenarios and open research challenges. This study
goes one step further to elaborate research opportunities to design and
optimize wireless systems with low energy footprint and at low cost. Finally,
we shed some light on future research aspects for IRS-assisted UAV
communication
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Sixth-generation (6G) mobile communication networks are expected to have
dense infrastructures, large-dimensional channels, cost-effective hardware,
diversified positioning methods, and enhanced intelligence. Such trends bring
both new challenges and opportunities for the practical design of 6G. On one
hand, acquiring channel state information (CSI) in real time for all wireless
links becomes quite challenging in 6G. On the other hand, there would be
numerous data sources in 6G containing high-quality location-tagged channel
data, making it possible to better learn the local wireless environment. By
exploiting such new opportunities and for tackling the CSI acquisition
challenge, there is a promising paradigm shift from the conventional
environment-unaware communications to the new environment-aware communications
based on the novel approach of channel knowledge map (CKM). This article aims
to provide a comprehensive tutorial overview on environment-aware
communications enabled by CKM to fully harness its benefits for 6G. First, the
basic concept of CKM is presented, and a comparison of CKM with various
existing channel inference techniques is discussed. Next, the main techniques
for CKM construction are discussed, including both the model-free and
model-assisted approaches. Furthermore, a general framework is presented for
the utilization of CKM to achieve environment-aware communications, followed by
some typical CKM-aided communication scenarios. Finally, important open
problems in CKM research are highlighted and potential solutions are discussed
to inspire future work
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