532 research outputs found
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
Characterization of Wireless Channel Semantics: A New Paradigm
Recently, deep learning enabled semantic communications have been developed
to understand transmission content from semantic level, which realize effective
and accurate information transfer. Aiming to the vision of sixth generation
(6G) networks, wireless devices are expected to have native perception and
intelligent capabilities, which associate wireless channel with surrounding
environments from physical propagation dimension to semantic information
dimension. Inspired by these, we aim to provide a new paradigm on wireless
channel from semantic level. A channel semantic model and its characterization
framework are proposed in this paper. Specifically, a channel semantic model
composes of status semantics, behavior semantics and event semantics. Based on
actual channel measurement at 28 GHz, as well as multi-mode data, example
results of channel semantic characterization are provided and analyzed, which
exhibits reasonable and interpretable semantic information
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
Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization
In this paper, we propose a missing spectrum data recovery technique for
cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It
is shown that the spectrum measurements collected from secondary users (SUs)
can be factorized as product of a channel gain matrix times an activation
matrix. Then, an NMF method with piecewise constant activation coefficients is
introduced to analyze the measurements and estimate the missing spectrum data.
The proposed optimization problem is solved by a Majorization-Minimization
technique. The numerical simulation verifies that the proposed technique is
able to accurately estimate the missing spectrum data in the presence of noise
and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
The fifth generation (5G) mobile networks are envisaged to enable a plethora
of breakthrough advancements in wireless technologies, providing support of a
diverse set of services over a single platform. While the deployment of 5G
systems is scaling up globally, it is time to look ahead for beyond 5G systems.
This is driven by the emerging societal trends, calling for fully automated
systems and intelligent services supported by extended reality and haptics
communications. To accommodate the stringent requirements of their prospective
applications, which are data-driven and defined by extremely low-latency,
ultra-reliable, fast and seamless wireless connectivity, research initiatives
are currently focusing on a progressive roadmap towards the sixth generation
(6G) networks. In this article, we shed light on some of the major enabling
technologies for 6G, which are expected to revolutionize the fundamental
architectures of cellular networks and provide multiple homogeneous artificial
intelligence-empowered services, including distributed communications, control,
computing, sensing, and energy, from its core to its end nodes. Particularly,
this paper aims to answer several 6G framework related questions: What are the
driving forces for the development of 6G? How will the enabling technologies of
6G differ from those in 5G? What kind of applications and interactions will
they support which would not be supported by 5G? We address these questions by
presenting a profound study of the 6G vision and outlining five of its
disruptive technologies, i.e., terahertz communications, programmable
metasurfaces, drone-based communications, backscatter communications and
tactile internet, as well as their potential applications. Then, by leveraging
the state-of-the-art literature surveyed for each technology, we discuss their
requirements, key challenges, and open research problems
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