848 research outputs found
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
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 Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends
This paper examines the security vulnerabilities and threats imposed by the
inherent open nature of wireless communications and to devise efficient defense
mechanisms for improving the wireless network security. We first summarize the
security requirements of wireless networks, including their authenticity,
confidentiality, integrity and availability issues. Next, a comprehensive
overview of security attacks encountered in wireless networks is presented in
view of the network protocol architecture, where the potential security threats
are discussed at each protocol layer. We also provide a survey of the existing
security protocols and algorithms that are adopted in the existing wireless
network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term
evolution (LTE) systems. Then, we discuss the state-of-the-art in
physical-layer security, which is an emerging technique of securing the open
communications environment against eavesdropping attacks at the physical layer.
We also introduce the family of various jamming attacks and their
counter-measures, including the constant jammer, intermittent jammer, reactive
jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the
integration of physical-layer security into existing authentication and
cryptography mechanisms for further securing wireless networks. Finally, some
technical challenges which remain unresolved at the time of writing are
summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201
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