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|a 9783030257484
|9 978-3-030-25748-4
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|a 10.1007/978-3-030-25748-4
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|a Cognitive Radio-Oriented Wireless Networks
|h [electronic resource] :
|b 14th EAI International Conference, CrownCom 2019, Poznan, Poland, June 11-12, 2019, Proceedings /
|c edited by Adrian Kliks, Paweł Kryszkiewicz, Faouzi Bader, Dionysia Triantafyllopoulou, Carlos E. Caicedo, Aydin Sezgin, Nikos Dimitriou, Michał Sybis.
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|a 1st ed. 2019.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2019.
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|a XI, 410 p. 243 illus., 166 illus. in color.
|b online resource.
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|a text
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|a Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,
|x 1867-8211 ;
|v 291
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|a PoMeS: Profit-Maximizing Sensor Selection for Crowd-Sensed Spectrum Discovery -- Tenant-Aware Slice Admission Control using Neural Networks-Based Policy Agent Main Track -- Novel Filter Bank Based Cooperative Spectrum Sensing under RF Impairments and Channel Fading Beyond 5G Cognitive Radios -- Performance Evaluation of Windowing Based Energy Detector in Multipath and Multi-Signal Scenarios -- Transmitter Classification With Supervised Deep Learning -- Preferential Radar Method for Dynamic Assignment of Wi-Fi Channels -- Tactical radio operator's combat readiness as context information for dynamic spectrum management within military mobile ad hoc networks -- Dynamic Placement Algorithm for MultipleClasses of Mobile Base Stations in Public SafetyNetworks -- Spectrum analysis using semantic models for context -- Margin-based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks -- On the Feasibility of a Secondary Service Transmission Over an Existent Satellite Infrastructure -- Cooperative Delay-Constrained Cognitive Radio Networks: Throughput Maximization With Full-Duplex Capability Impact -- Radio Environment Maps for Military Cognitive Networks: Density of Sensor Network vs. Map Quality -- An Out-of-Sample Extension for Wireless Multipoint Channel Charting -- Machine Learning based RATs Selection supporting Multi-Connectivity for Reliability -- Spectrum-agile Cognitive Interference Avoidance through Deep Reinforcement Learning -- Localization techniques for 5G Radio Environment Maps -- A Hybrid Chain based Incentive Mechanism for Resource Leasing in NDN -- Reinforcement Learning-based Radio Access Network Slicing for a 5G System with Support for Cellular V2X -- Assessment of Spectrum Management Approaches to Private Industrial Networks -- Unlocking the Potential of QoS-Aware Pricing under the Licensed Shared Access Regime -- Location dependent spectrum valuation of private LTE and 5G networks in Europe -- Spectrum allocation options for point-to-multipoint services in 5G -- Wireless Network Virtualization with Long-Term Device-to-Device Communication -- Assessing the Feasibility of the Citizens Broadband Radio Service Concept for the Private Industrial Internet of Things Networks -- Wrkshop on Open Radio Platforms for 5G Research and Beyond -- Enhanced Resource Management for Web Based Thin Clients Using Cross-Platform Progressive Offline Capabilities -- Stabilized Distributed Layered Grant-Free NOMA for mMTC -- 5G CrowdCell with mm-Wave SDR Based Backhaul -- Performance Analysis of Full Duplex Wireless Multi-Hop Networks.
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|a This book constitutes the refereed proceedings of the 14th International Conference on Cognitive Radio-Oriented Wireless Networks, CROWNCOM 2019, held in Poznan, Poland, in June 2019. The 30 revised full papers were selected from 48 submissions and present a large scope of research topic also covering IoT in 5G and how cognitive mechanisms shall help leveraging access for numerous devices; mmWave and how specific propagation and operation in these bands bring new sharing mechanisms ; how resource allocation amongst bands (including offload mechanisms) shall be solved. The key focus will be on how rich data analysis can improve the delivery of above defined services.
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|a Computer communication systems.
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|a Data structures (Computer science).
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|a Machine learning.
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|a Computer Communication Networks.
|0 http://scigraph.springernature.com/things/product-market-codes/I13022
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|a Data Structures and Information Theory.
|0 http://scigraph.springernature.com/things/product-market-codes/I15009
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|a Machine Learning.
|0 http://scigraph.springernature.com/things/product-market-codes/I21010
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1 |
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|a Kliks, Adrian.
|e editor.
|0 (orcid)0000-0001-6766-7836
|1 https://orcid.org/0000-0001-6766-7836
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Kryszkiewicz, Paweł.
|e editor.
|0 (orcid)0000-0001-9054-9416
|1 https://orcid.org/0000-0001-9054-9416
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Bader, Faouzi.
|e editor.
|4 edt
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700 |
1 |
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|a Triantafyllopoulou, Dionysia.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Caicedo, Carlos E.
|e editor.
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700 |
1 |
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|a Sezgin, Aydin.
|e editor.
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700 |
1 |
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|a Dimitriou, Nikos.
|e editor.
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700 |
1 |
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|a Sybis, Michał.
|e editor.
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|i Printed edition:
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776 |
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|i Printed edition:
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830 |
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|a Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,
|x 1867-8211 ;
|v 291
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856 |
4 |
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|u https://doi.org/10.1007/978-3-030-25748-4
|z Full Text via HEAL-Link
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|a Computer Science (Springer-11645)
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