45,265 research outputs found
Cognitive code-division links with blind primary-system identification
Abstract—We consider the problem of cognitive code-division channelization (simultaneous power and code-channel allocation) for secondary transmission links co-existing with an unknown primary code-division multiple-access (CDMA) system. We first develop a blind primary-user identification scheme to detect the binary code sequences (signatures) utilized by primary users. To create a secondary link we propose two alternative procedures –one of moderate and one of low computational complexity – that optimize the secondary transmitting power and binary codechannel assignment in accordance with the detected primary code channels to avoid “harmful ” interference. At the same time, the optimization procedures guarantee that the signalto-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear secondary receiver is no less than a certain threshold to meet secondary transmission quality of service (QoS) requirements. The extension of the channelization problem to multiple secondary links is also investigated. Simulation studies presented herein illustrate the theoretical developments. Index Terms—Blind user identification, code-channel allocation, code-division multiple-access, cognitive radio, dynamic spectrum access, power allocation, signal-to-interference-plusnoise ratio. I
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
Waveform Design for Secure SISO Transmissions and Multicasting
Wireless physical-layer security is an emerging field of research aiming at
preventing eavesdropping in an open wireless medium. In this paper, we propose
a novel waveform design approach to minimize the likelihood that a message
transmitted between trusted single-antenna nodes is intercepted by an
eavesdropper. In particular, with knowledge first of the eavesdropper's channel
state information (CSI), we find the optimum waveform and transmit energy that
minimize the signal-to-interference-plus-noise ratio (SINR) at the output of
the eavesdropper's maximum-SINR linear filter, while at the same time provide
the intended receiver with a required pre-specified SINR at the output of its
own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is
unavailable, we design a waveform that maximizes the amount of energy available
for generating disturbance to eavesdroppers, termed artificial noise (AN),
while the SINR of the intended receiver is maintained at the pre-specified
level. The extensions of the secure waveform design problem to multiple
intended receivers are also investigated and semidefinite relaxation (SDR) -an
approximation technique based on convex optimization- is utilized to solve the
arising NP-hard design problems. Extensive simulation studies confirm our
analytical performance predictions and illustrate the benefits of the designed
waveforms on securing single-input single-output (SISO) transmissions and
multicasting
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
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