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Journal articles on the topic 'Radio opportuniste'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

A., Dr Prakash. "Review of Opportunistic Spectrum Access Approach in Cognitive Radio Networks." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 222–31. http://dx.doi.org/10.5373/jardcs/v12sp7/20202101.

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2

Smith, Andrew D. "Automated Screening for Future Osteoporotic Fractures on Abdominal CT: Opportunistic or an Outstanding Opportunity?" Radiology 297, no. 1 (October 2020): 73–74. http://dx.doi.org/10.1148/radiol.2020202900.

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3

Senthil Kumar, B., and Dr S. K. Srivatsa. "Opportunistic Channel Access Algorithm Based on Hidden Semi Markov Model for Cognitive Radio Networks." Bonfring International Journal of Research in Communication Engineering 4, no. 2 (November 30, 2014): 17–21. http://dx.doi.org/10.9756/bijrce.8098.

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4

Dohler, Mischa, Seyed A. Ghorashi, Mohamed Ghozzi, Marylin Arndt, Fatin Said, and A. Hamid Aghvami. "Opportunistic scheduling using cognitive radio." Comptes Rendus Physique 7, no. 7 (September 2006): 805–15. http://dx.doi.org/10.1016/j.crhy.2006.07.004.

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5

Silva Cabral, Yngrid Keila, Joab De Araújo Silva, and Marcelo Portela Sousa. "Uma proposta para a melhoria da descoberta de vizinhos em Redes Cognitivas." Revista Principia - Divulgação Científica e Tecnológica do IFPB 1, no. 38 (February 15, 2018): 69. http://dx.doi.org/10.18265/1517-03062015v1n38p69-76.

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Several wireless services make use of the spectrum for communication, from space radio communication to a simple wireless computer network. However, the considerable amount of services that make use of spectrum in the last years, has been responsible for its limited availability and inefficiency.. Cognitive networks or cognitive radio networks, are a technology that offer the efficient use of spectrum through opportunistic access to frequency bands. Throughout the cognitive radios scenario, this work will present a variation of the JENNA neighbors discovery algorithm. In addition to that, this research will propose a sensing model for a more efficient approach.

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6

Susanti, Neneng, Rima Rahmayanti, Rizal Ramdan Padmakusumah, and Achmad Drajat Aji Sujai R. "Influence Investment Opportunity Set, Leverage and Market Risk of Dividend Payout Ratio." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 13, 2020): 3521–28. http://dx.doi.org/10.37200/ijpr/v24i2/pr200672.

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7

Kaarthik, K., P. T. Sivagurunathan, and S. Sivaranjani. "A REVIEW ON SPECTRUM SENSING METHODS FOR COGNITIVE RADIO NETWORKS." JOURNAL OF ADVANCES IN CHEMISTRY 12, no. 18 (November 16, 2016): 5053–57. http://dx.doi.org/10.24297/jac.v12i18.5380.

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In Wireless Communication, Radio Spectrum is doing a vital role; for the future need it should use efficient. The existing system, it is not possible to use it efficiently where the allocation of spectrum is done based on fixed spectrum access (FSA) policy. Several surveys prove that it show the way to inefficient use of spectrum. An innovative technique is needed for spectrum utilization effectively. Using Dynamic spectrum access (DSA) policy, available spectrum can be exploited. Cognitive radio arises to be an attractive solution which introduces opportunistic usage of the frequency bands that are not commonly occupied by licensed users. Cognitive radios promote open spectrum allocation which is a clear departure from habitual command and control allocation process for radio spectrum usage. In short, it permits the formation of â€œinfrastructure-lessâ€ joint network clusters which is called Cognitive Radio Networks (CRN). Conversely the spectrum sensing techniques are needed to detect free spectrum. In this paper, different spectrum sensing techniques are analyzed.

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8

Mishra, Mangala Prasad, Sunil Kumar Singh, and Deo Prakash Vidyarthi. "Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network." International Journal of Mathematical, Engineering and Management Sciences 5, no. 5 (October 1, 2020): 995–1012. http://dx.doi.org/10.33889/ijmems.2020.5.5.076.

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The growing demand of radio spectrum to facilitate the primary/secondary users in a cellular network is a challenging task. Many channel allocation models, applying cognition, have been proposed to increase the radio spectrum utilization. The proposed model peruses three types of users: primary users (PUs), opportunistic primary users (OPUs), and secondary users (SUs) that use the radio resources in collocated primary base stations. Out of these users, the opportunistic primary users and secondary users may request for handover as per their requirements. The objective of the model is to enhance the radio spectrum utilization by the opportunistic utilization of radio resources by OPUs and by enabling cognitive radio base stations to collect free channel information dynamically. The cognitive radio base station maintains the centralized free channel at collocated primary base stations to facilitate the SUs opportunistically. The proposed channel allocation technique maintains the Quality of Experience (QoE) of the users as well. The performance analysis of the model is done by simulation which diversifies the importance of the proposed model in the view of minimum blocked services.

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9

Bourdena, Athina, Evangelos Pallis, Georgios Kormentzas, and George Mastorakis. "Efficient radio resource management algorithms in opportunistic cognitive radio networks." Transactions on Emerging Telecommunications Technologies 25, no. 8 (July 29, 2013): 785–97. http://dx.doi.org/10.1002/ett.2687.

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10

Bansal, Tarun, Dong Li, and Prasun Sinha. "Opportunistic Channel Sharingin Cognitive Radio Networks." IEEE Transactions on Mobile Computing 13, no. 4 (April 2014): 852–65. http://dx.doi.org/10.1109/tmc.2013.59.

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11

Mumtaz, Shahid, Paulo Marques, Atilio Gameiro, and Jonathan Rodriguez. "Ad-hoc behavior in opportunistic radio." Journal of Communications and Networks 11, no. 2 (April 2009): 187–95. http://dx.doi.org/10.1109/jcn.2009.6391393.

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12

Hendee, William R. "An Opportunity for Radiology." Radiology 238, no. 2 (February 2006): 389–94. http://dx.doi.org/10.1148/radiol.2382051177.

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13

Preet, Tanu Preet Singh, Prof R. K. Singh, Jaspreet Kaur, and Vishal Sharma. "OPPORTUNISTIC APPROACH TO EXPLOIT WIRELESS SPECTRUM BY USE OF COGNITIVE RADIO." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 108–15. http://dx.doi.org/10.24297/ijct.v2i3b.2700.

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Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. In this paper, intrinsic properties and research on software defined cognitive radio (SDCR) are presented. Firstly brief introduction of Cognitive Radio is given along with its architecture.Â Then spectrum management of Cognitive Radio ad hoc networks (CRAHNs) and their main features like spectrum sensing, spectrum decision, spectrum selection and spectrum mobility are defined. At the end, Software Defined Cognitive Radio (SDCR), its hardware and software platform, along with research topics on SDCR are defined

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14

El-Sherif, A. A., A. K. Sadek, and K. J. R. Liu. "Opportunistic Multiple Access for Cognitive Radio Networks." IEEE Journal on Selected Areas in Communications 29, no. 4 (April 2011): 704–15. http://dx.doi.org/10.1109/jsac.2011.110404.

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15

Habachi, O., and Y. Hayel. "Optimal opportunistic sensing in cognitive radio networks." IET Communications 6, no. 8 (2012): 797. http://dx.doi.org/10.1049/iet-com.2010.0537.

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16

Li, Yang, and Aria Nosratinia. "Hybrid Opportunistic Scheduling in Cognitive Radio Networks." IEEE Transactions on Wireless Communications 11, no. 1 (January 2012): 328–37. http://dx.doi.org/10.1109/twc.2011.110811.110722.

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17

Krestin, Gabriel P. "Commoditization in Radiology: Threat or Opportunity?" Radiology 256, no. 2 (August 2010): 338–42. http://dx.doi.org/10.1148/radiol.10100144.

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18

Verma, Gaurav, and O. P. Sahu. "Opportunistic Selection of Threshold in Cognitive Radio Networks." Wireless Personal Communications 92, no. 2 (August 8, 2016): 711–26. http://dx.doi.org/10.1007/s11277-016-3573-5.

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19

Ye, Zhihui, Yicheng Shen, and Qi Feng. "Scheme for opportunistic spectrum access in cognitive radio." IET Communications 7, no. 11 (July 23, 2013): 1061–69. http://dx.doi.org/10.1049/iet-com.2012.0560.

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20

Lee, Seunghyun, Rui Zhang, and Kaibin Huang. "Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks." IEEE Transactions on Wireless Communications 12, no. 9 (September 2013): 4788–99. http://dx.doi.org/10.1109/twc.2013.072613.130323.

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21

Zafar, Ammar, Mohamed-Slim Alouini, Yunfei Chen, and Redha M. Radaydeh. "Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access." Journal of Computer Networks and Communications 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/294581.

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Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources.

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22

Hawa, Mohammed, Ahmad AlAmmouri, Ala Alhiary, and Nidal Alhamad. "Distributed opportunistic spectrum sharing in cognitive radio networks." International Journal of Communication Systems 30, no. 7 (May 19, 2016): e3147. http://dx.doi.org/10.1002/dac.3147.

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23

Asadi, Arash, and Vincenzo Mancuso. "DRONEE: Dual-radio opportunistic networking for energy efficiency." Computer Communications 50 (September 2014): 41–52. http://dx.doi.org/10.1016/j.comcom.2014.02.014.

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24

Marinho, José, and Edmundo Monteiro. "CORHYS: Hybrid signaling for opportunistic distributed cognitive radio." Computer Networks 81 (April 2015): 19–42. http://dx.doi.org/10.1016/j.comnet.2015.01.019.

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25

Alnabelsi, Sharhabeel H., Haythem A. Bany Salameh, and Zaid M. Albataineh. "Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3854. http://dx.doi.org/10.11591/ijece.v10i4.pp3854-3861.

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Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

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26

Liu, Anfeng, Wei Chen, and Xiao Liu. "Delay optimal opportunistic pipeline routing scheme for cognitive radio sensor networks." International Journal of Distributed Sensor Networks 14, no. 4 (April 2018): 155014771877253. http://dx.doi.org/10.1177/1550147718772532.

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In order to solve the problem of spectrum scarcity in wireless sensor networks, cognitive radio technology can be introduced into wireless sensor networks, giving rising to cognitive radio sensor networks. Delay-sensitive data applications in cognitive radio sensor networks require efficient real-time communication. Opportunistic pipeline routing is a potential technology to reduce the delay, which can use nodes outside the main forwarding path forward data opportunistically when the transmission fails. However, the energy efficiency of cognitive radio sensor networks with opportunistic pipeline routing is low, and the data transmission delay can be further optimized. In view of this situation, we propose the delay optimal opportunistic pipeline routing scheme named Variable Duty Cycle for Opportunistic Pipeline Routing (VDCOPR). In the Variable Duty Cycle for Opportunistic Pipeline Routing scheme, the nodes employ high duty cycle in the area far from the sink, and low duty cycle in the area near to the sink, which can achieve the balance of energy consumption and reduce the data transmission delay while not affecting network lifetime. The theoretical analysis and experimental results show that, compared with previous opportunistic pipeline routing, energy consumption of network is relatively balanced and the data transmission delay can be reduced by 36.6% in the Variable Duty Cycle for Opportunistic Pipeline Routing scheme.

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27

Arshid, Kaleem, Iftikhar Hussain, Muhammad Khawar Bashir, Shahid Naseem, Allah Ditta, Natash Ali Mian, Misha Zahid, and Israr Ali Khan. "Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks." Applied Sciences 10, no. 5 (March 2, 2020): 1674. http://dx.doi.org/10.3390/app10051674.

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Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.

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28

Gur, David. "Lung Cancer Screening: Radiology's Opportunity Here and Now." Radiology 238, no. 2 (February 2006): 395–97. http://dx.doi.org/10.1148/radiol.2382050706.

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29

Li, Mingming, Jiaru Lin, Fazhong Liu, Dongxu Wang, and Li Guo. "Cognitive MIMO Radio." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 2 (April 2011): 58–79. http://dx.doi.org/10.4018/jcini.2011040104.

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The authors consider a cognitive radio network in which a set of cognitive users make opportunistic spectrum access to one primary channel by time-division multiplexing technologies. Multiple Input Multiple Output techniques (MIMO) are similarly considered to enhance the stable throughput for cognitive links while they should guarantee co-channel interference constraints to the primary link. Here, two different cases are considered: one is that cognitive radio network is distributed; the other is centrally-controlled that cognitive radio network has a cognitive base station. In the first case, how to choose one fixed cognitive user and power control for each transmission antenna at the cognitive base station are considered to maximize the cognitive link’s stable throughput. In the second case, a scheme to choose a group of cognitive users and a Zero-Forcing method to pre-white co-channel interference to the primary user, are also proposed in order to maximize cognitive base station’s sum-rate. The algorithm can be employed to realize opportunistic spectrum transmission over the wireless fading channels.

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30

Islam, Muhammad Arshad, and Marcel Waldvogel. "Analysis of Cognitive Radio Enabled Flooding in Opportunistic Networks." International Journal of Communications, Network and System Sciences 07, no. 07 (2014): 212–22. http://dx.doi.org/10.4236/ijcns.2014.77023.

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31

Xu, Li, He Fang, and Zhiwei Lin. "Evolutionarily stable opportunistic spectrum access in cognitive radio networks." IET Communications 10, no. 17 (November 24, 2016): 2290–99. http://dx.doi.org/10.1049/iet-com.2016.0049.

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32

Kwon, Sehoon, Bosung Kim, and Byeong-hee Roh. "Preemptive Opportunistic MAC Protocol in Distributed Cognitive Radio Networks." IEEE Communications Letters 18, no. 7 (July 2014): 1155–58. http://dx.doi.org/10.1109/lcomm.2014.2327963.

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33

D'Oro, Salvatore, Panayotis Mertikopoulos, Aris L. Moustakas, and Sergio Palazzo. "Interference-Based Pricing for Opportunistic Multicarrier Cognitive Radio Systems." IEEE Transactions on Wireless Communications 14, no. 12 (December 2015): 6536–49. http://dx.doi.org/10.1109/twc.2015.2456063.

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34

Tsinos, Christos G., and Kostas Berberidis. "Blind Opportunistic Interference Alignment in MIMO Cognitive Radio Systems." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3, no. 4 (December 2013): 626–39. http://dx.doi.org/10.1109/jetcas.2013.2284611.

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35

Urgaonkar, R., and M. J. Neely. "Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks." IEEE Transactions on Mobile Computing 8, no. 6 (June 2009): 766–77. http://dx.doi.org/10.1109/tmc.2009.38.

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36

Pirmoradian, Mahdi, Olayinka Adigun, and Christos Politis. "Entropy-based opportunistic spectrum access for cognitive radio networks." Transactions on Emerging Telecommunications Technologies 28, no. 1 (October 8, 2014): e2886. http://dx.doi.org/10.1002/ett.2886.

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37

Shatila, Hazem, Mohamed Khedr, and Jeffrey H. Reed. "Opportunistic channel allocation decision making in cognitive radio communications." International Journal of Communication Systems 27, no. 2 (April 13, 2012): 216–32. http://dx.doi.org/10.1002/dac.2350.

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38

El Tanab, Manal, Walaa Hamouda, and Yasmine Fahmy. "Distributed opportunistic scheduling for MIMO underlay cognitive radio networks." Wireless Communications and Mobile Computing 16, no. 15 (June 13, 2016): 2212–24. http://dx.doi.org/10.1002/wcm.2677.

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39

Asaduzzaman, Hyung Yun Kong, and Insoo Koo. "Opportunistic relaying based spectrum leasing for cognitive radio networks." Journal of Communications and Networks 13, no. 1 (February 2011): 50–55. http://dx.doi.org/10.1109/jcn.2011.6157251.

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40

Zhai, Linbo. "Opportunistic spectrum access for TDMA-based cognitive radio networks." Optik 125, no. 9 (May 2014): 2081–85. http://dx.doi.org/10.1016/j.ijleo.2013.10.125.

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41

Santhoshkumar, M., Dony J. Muttath, and K. Premkumar. "Throughput Optimal Opportunistic Channel Switching in Cognitive Radio Networks." IEEE Wireless Communications Letters 10, no. 9 (September 2021): 2046–50. http://dx.doi.org/10.1109/lwc.2021.3091635.

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42

Voronovich, Alexander G., and Valery U. Zavorotny. "Determination of surface reflectivity using radio signals of opportunity." Waves in Random and Complex Media 27, no. 3 (November 7, 2016): 395–402. http://dx.doi.org/10.1080/17455030.2016.1253902.

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43

Nikookar, Homayoun, and Patrick Oonincx. "An Introduction to Radio Locationing with Signals of Opportunity." Journal of Communication, Navigation, Sensing and Services (CONASENSE) 1, no. 3 (2016): 197–206. http://dx.doi.org/10.13052/jconasense2246-2120.131.

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44

Nikookar, Homayoun, and Patrick Oonincx. "An Introduction to Radio Locationing with Signals of Opportunity." Journal of Communication, Navigation, Sensing and Services (CONASENSE) 2016, no. 1 (2016): 1–10. http://dx.doi.org/10.13052/jconasense2246-2120.2016.001.

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45

GLENDENNING, NORMAN K. "FAST PULSARS, STRANGE STARS: AN OPPORTUNITY IN RADIO ASTRONOMY." Modern Physics Letters A 05, no. 27 (October 30, 1990): 2197–207. http://dx.doi.org/10.1142/s021773239000250x.

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The world's data on radio pulsars are not expected to represent the underlying pulsar population because of a search bias against detection of short periods, especially below 1 ms. Yet pulsars in increasing numbers with periods right down to this limit have been discovered, suggesting that there may be even shorter ones. If pulsars with periods below 1/2 ms were found, the conclusion that the confined hadronic phase of nucleons and nuclei is only metastable would be almost inescapable. The plausible ground state in that event is the deconfined phase of (3-flavor) strange-quark-matter. From the QCD energy scale this is as likely a ground state as the confined phase. We show that strange matter as the ground state is not ruled out by any known fact, and most especially not by the fact that the universe is in the confined phase.

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46

Byun, Sang-Seon. "TCP over scarce transmission opportunity in cognitive radio networks." Computer Networks 103 (July 2016): 101–14. http://dx.doi.org/10.1016/j.comnet.2016.03.026.

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47

Smith, Andrew D. "Screening of Bone Density at CT: An Overlooked Opportunity." Radiology 291, no. 2 (May 2019): 368–69. http://dx.doi.org/10.1148/radiol.2019190434.

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48

Hendee, William R. "An Opportunity for Radiology: Recommendations from the Educational Summit." Radiology 241, no. 1 (October 2006): 5–10. http://dx.doi.org/10.1148/radiol.2411060628.

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49

Suter, B., B. Shoulders, M. Maclean, and J. Balyckyi. "Machine verification radiographs: an opportunity for role extension?" Radiography 6, no. 4 (November 2000): 245–51. http://dx.doi.org/10.1053/radi.2000.0275.

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50

ZHAI, Linbo, and Gang XIE. "A Slot-Based Opportunistic Spectrum Access for Cognitive Radio Networks." IEICE Transactions on Communications E94-B, no. 11 (2011): 3183–85. http://dx.doi.org/10.1587/transcom.e94.b.3183.

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