519 research outputs found
Survey of Spectrum Sharing for Inter-Technology Coexistence
Increasing capacity demands in emerging wireless technologies are expected to
be met by network densification and spectrum bands open to multiple
technologies. These will, in turn, increase the level of interference and also
result in more complex inter-technology interactions, which will need to be
managed through spectrum sharing mechanisms. Consequently, novel spectrum
sharing mechanisms should be designed to allow spectrum access for multiple
technologies, while efficiently utilizing the spectrum resources overall.
Importantly, it is not trivial to design such efficient mechanisms, not only
due to technical aspects, but also due to regulatory and business model
constraints. In this survey we address spectrum sharing mechanisms for wireless
inter-technology coexistence by means of a technology circle that incorporates
in a unified, system-level view the technical and non-technical aspects. We
thus systematically explore the spectrum sharing design space consisting of
parameters at different layers. Using this framework, we present a literature
review on inter-technology coexistence with a focus on wireless technologies
with equal spectrum access rights, i.e. (i) primary/primary, (ii)
secondary/secondary, and (iii) technologies operating in a spectrum commons.
Moreover, we reflect on our literature review to identify possible spectrum
sharing design solutions and performance evaluation approaches useful for
future coexistence cases. Finally, we discuss spectrum sharing design
challenges and suggest future research directions
LTE and Wi-Fi Coexistence in Unlicensed Spectrum with Application to Smart Grid: A Review
Long Term Evolution (LTE) is expanding its utilization in unlicensed band by
deploying LTE Unlicensed (LTEU) and Licensed Assisted Access LTE (LTE-LAA)
technology. Smart Grid can take the advantages of unlicensed bands for
achieving two-way communication between smart meters and utility data centers
by using LTE-U/LTE-LAA. However, both schemes must co-exist with the incumbent
Wi-Fi system. In this paper, several co-existence schemes of Wi-Fi and LTE
technology is comprehensively reviewed. The challenges of deploying LTE and
Wi-Fi in the same band are clearly addressed based on the papers reviewed.
Solution procedures and techniques to resolve the challenging issues are
discussed in a short manner. The performance of various network architectures
such as listenbefore- talk (LBT) based LTE, carrier sense multiple access with
collision avoidance (CSMA/CA) based Wi-Fi is briefly compared. Finally, an
attempt is made to implement these proposed LTEWi- Fi models in smart grid
technology.Comment: submitted in 2018 IEEE PES T&
An analysis on decentralized adaptive MAC protocols for Cognitive Radio networks
The scarcity of bandwidth in the radio spectrum has become more vital since the demand for more and more wireless applications has increased. Most of the spectrum bands have been allocated although many studies have shown that these bands are significantly underutilized most of the time. The problem of unavailability of spectrum and inefficiency in its utilization has been smartly addressed by the Cognitive Radio (CR) Technology which is an opportunistic network that senses the environment, observes the network changes, and then using knowledge gained from the prior interaction with the network, makes intelligent decisions by dynamically adapting their transmission characteristics. In this paper some of the decentralized adaptive MAC protocols for CR networks have been critically analyzed and a novel adaptive MAC protocol for CR networks, DNG-MAC which is decentralized and non-global in nature, has been proposed. The results show the DNG-MAC out performs other CR MAC protocols in terms of time and energy efficiency
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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