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Journal articles on the topic 'Allocation des ressources radios'

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1

Chung, J. M., C. H. Kim, J. H. Park, J. Shin, and D. Kim. "Optimal channel allocation for cognitive radios." Electronics Letters 46, no. 11 (2010): 802. http://dx.doi.org/10.1049/el.2010.0829.

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2

Prabhjot Kaur, Moin Uddin, and Arun Khosla. "Adaptive Bandwidth Allocation Scheme for Cognitive Radios." International Journal of Advancements in Computing Technology 2, no. 2 (2010): 35–41. http://dx.doi.org/10.4156/ijact.vol2.issue2.3.

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3

Yang, Zhou, Wenqian Jiang, and Gang Li. "Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization." Wireless Communications and Mobile Computing 2018 (July 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/1327030.

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Green cognitive radios are promising in future wireless communications due to high energy efficiency. Energy efficiency maximization problems are formulated in delay-insensitive green cognitive radio and delay-sensitive green cognitive radio. The optimal resource allocation strategies for delay-insensitive green cognitive radio and delay-sensitive green cognitive radio are designed to maximize the energy efficiency of the secondary user. The peak interference power and the average/peak transmit power constraints are considered. Two algorithms based on the proposed resource allocation strategie
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4

Kim, Seung-Jun, Nasim Yahya Soltani, and Georgios B. Giannakis. "Resource Allocation for OFDMA Cognitive Radios Under Channel Uncertainty." IEEE Transactions on Wireless Communications 12, no. 7 (2013): 3578–87. http://dx.doi.org/10.1109/twc.2013.062413.121892.

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5

Bazerque, Juan-Andrés, and Georgios B. Giannakis. "Distributed Scheduling and Resource Allocation for Cognitive OFDMA Radios." Mobile Networks and Applications 13, no. 5 (2008): 452–62. http://dx.doi.org/10.1007/s11036-008-0083-z.

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6

Balassa, Bela. "Politiques agricoles et allocation internationale des ressources." Économie rurale 189, no. 1 (1989): 22–28. http://dx.doi.org/10.3406/ecoru.1989.3948.

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7

Luo, Rong-hua, and Zhen Yang. "Stackelberg Game-based Distributed Power Allocation Algorithm in Cognitive Radios." Journal of Electronics & Information Technology 32, no. 12 (2011): 2964–69. http://dx.doi.org/10.3724/sp.j.1146.2010.00374.

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8

Azmat, Freeha, Yunfei Chen, and Nigel Stocks. "Bio-inspired collaborative spectrum sensing and allocation for cognitive radios." IET Communications 9, no. 16 (2015): 1949–59. http://dx.doi.org/10.1049/iet-com.2014.0769.

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9

Liu, Yicong, Muhammad Iqbal, Muhammad Naeem, Alagan Anpalagan, and N. N. Qadri. "Resource Allocation in Hospital Networks Based on Green Cognitive Radios." Wireless Personal Communications 85, no. 3 (2015): 1487–507. http://dx.doi.org/10.1007/s11277-015-2852-x.

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10

Jian Tang, R. Hincapie, Guoliang Xue, Weiyi Zhang, and R. Bustamante. "Fair Bandwidth Allocation in Wireless Mesh Networks With Cognitive Radios." IEEE Transactions on Vehicular Technology 59, no. 3 (2010): 1487–96. http://dx.doi.org/10.1109/tvt.2009.2038478.

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11

Hung, Nguyen Manh. "L’efficacité économique du mode d’allocation des ressources naturelles." Articles 51, no. 3 (2009): 405–19. http://dx.doi.org/10.7202/800630ar.

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Abstract In this paper, the decentralized economic allocation of a common property resource is fully characterized in a dynamic framework. Because of the externalities involved, this mode of allocation is shown to be inefficient. The extent and the causes of this inefficiency are discussed, with special emphasis on a kind of externality herein called the "appropriability". Finally, economic policies involving taxation, subsidy and user charge which purpose to bring about the social optimum are pointed out.
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12

NGUYEN, Thanh-Tung, and Insoo KOO. "Distortion-Aware Dynamic Channel Allocation for Multimedia Users in Cognitive Radios." IEICE Transactions on Communications E97.B, no. 12 (2014): 2790–99. http://dx.doi.org/10.1587/transcom.e97.b.2790.

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13

Lopez-Ramos, Luis M., Antonio G. Marques, and Javier Ramos. "Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios." IEEE Transactions on Wireless Communications 13, no. 11 (2014): 5954–67. http://dx.doi.org/10.1109/twc.2014.2347273.

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14

Alabbasi, Abdulrahman, Zouheir Rezki, and Basem Shihada. "Energy Efficient Resource Allocation for Cognitive Radios: A Generalized Sensing Analysis." IEEE Transactions on Wireless Communications 14, no. 5 (2015): 2455–69. http://dx.doi.org/10.1109/twc.2014.2387161.

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15

Ghosh, Amiotosh, and Walaa Hamouda. "Cross-Layer Antenna Selection and Channel Allocation for MIMO Cognitive Radios." IEEE Transactions on Wireless Communications 10, no. 11 (2011): 3666–74. http://dx.doi.org/10.1109/twc.2011.080611.100912.

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16

Audibert, Gérard, Hélène Gebel, and Michel Hasselmann. "Allocation de ressources médicales rares : enjeux éthiques et citoyens." Revue française d'éthique appliquée N° 12, no. 1 (2022): 171–82. http://dx.doi.org/10.3917/rfeap.012.0171.

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17

Yudaninggar, Kartika Sari. "Reformulasi Regulasi Radio Komunitas di Indonesia." POPULIKA 7, no. 1 (2019): 94–107. http://dx.doi.org/10.37631/populika.v7i1.30.

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Community Radio (CR) in Indonesia has emerged as a media that able to disseminate information to audience. Since this function can not be fullfiled by both private and public radios, CR is important for audience especially for those who lives in remote or even blank spot area. However, Government regulation on CR is feared to deprive the existence of CR. The regulation ranging from limitation of frequency allocation, criteria on coverage area and limited emmitance strength compare to public and private radios. This imply that the Government has never supported the existence of Community Radio.
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18

Yu, Heejung. "Optimal primary pilot power allocation and secondary channel sensing in cognitive radios." IET Communications 10, no. 5 (2016): 487–94. http://dx.doi.org/10.1049/iet-com.2015.0646.

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19

Illanko, Kandasamy, Muhammad Naeem, Alagan Anpalagan, and Dimitrios Androutsos. "Energy-Efficient Frequency and Power Allocation for Cognitive Radios in Television Systems." IEEE Systems Journal 10, no. 1 (2016): 313–24. http://dx.doi.org/10.1109/jsyst.2015.2393834.

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20

Gamel, Claude. "Comment financer l'allocation universelle? La stratégie de Van Parijs (1995) en question." Recherches économiques de Louvain 70, no. 3 (2004): 287–315. http://dx.doi.org/10.1017/s0770451800010769.

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RésuméDans l'ouvrage que Van Parijs a consacré en 1995 aux fondements philosophiques et économiques du projet d'allocation universelle, le chapitre IV est dédié au problème du financement « soutenable » d'une telle mesure. Les « rentes d'emploi » y sont présentées comme un troisième type de ressources susceptibles d'élargir l'assiette fiscale du financement, à côté des « ressources externes » et des « ressources internes » des individus. Les difficultés d'exploitation de telles rentes légitiment l'exploration d'une stratégie alternative de financement fondée sur la flexibilité « walrassienne »
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21

Nkengne, Patrick, and Léonie Marin. "L’allocation des ressources enseignantes en Afrique subsaharienne francophone : pour une meilleure équité des systèmes éducatifs." Éducation et francophonie 45, no. 3 (2018): 35–60. http://dx.doi.org/10.7202/1046416ar.

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Les pays d’Afrique subsaharienne souffrent d’un manque de personnel enseignant, et la forte augmentation de la population d’âge scolaire montre que le problème persistera. Il devient donc crucial pour ces pays d’optimiser l’utilisation de leurs ressources enseignantes en poste en s’assurant qu’elles sont équitablement allouées aux écoles. Or, jusqu’à quel point la répartition des enseignantes et des enseignants entre les écoles d’Afrique subsaharienne est-elle équitable? Comment peut-on améliorer cette allocation? Pour répondre à ces questions, cette étude s’appuie sur deux indicateurs, soit l
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22

Sauner-Leroy, Jacques-Bernard. "Allocation de ressources, avantage concurrentiel et performance des petites et moyennes entreprises de l’industrie manufacturière française." Revue internationale P.M.E. 15, no. 1 (2012): 65–85. http://dx.doi.org/10.7202/1008801ar.

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L’analyse des liens entre la possession de un ou plusieurs avantages concurrentiels et la performance des firmes est un domaine qui a déjà été assez largement abordé dans la littérature de management stratégique. Certains de ces travaux ont cherché plus précisément à mettre en évidence des modes de combinaisons de ressources conduisant à la réalisation de niveaux de performance élevés. Toutefois, leurs champs d’investigation sont demeurés le plus souvent restreints à des domaines d’activité spécifiques, de sorte que les résultats auxquels ils aboutissent ne peuvent être généralisés. Partant de
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23

Huang, Xiaoge, Baltasar Beferull-Lozano, and Carmen Botella. "Quasi-Nash Equilibria for Non-Convex Distributed Power Allocation Games in Cognitive Radios." IEEE Transactions on Wireless Communications 12, no. 7 (2013): 3326–37. http://dx.doi.org/10.1109/twc.2013.060413.121158.

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24

Dong In Kim, Long Bao Le, and Ekram Hossain. "Joint rate and power allocation for cognitive radios in dynamic spectrum access environment." IEEE Transactions on Wireless Communications 7, no. 12 (2008): 5517–27. http://dx.doi.org/10.1109/t-wc.2008.071465.

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25

Kaur, Prabhjot, Arun Khosla, and Khosla Uddin. "Markovian Queuing Model for Dynamic Spectrum Allocation in Centralized Architecture for Cognitive Radios." International Journal of Engineering and Technology 3, no. 1 (2011): 96–101. http://dx.doi.org/10.7763/ijet.2011.v3.206.

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26

Jayati, Ari Endang, Wahyu Minarti, and Sri Heranurweni. "Analisa Teknis Penetapan Kanal Frekuensi Radio Untuk Lembaga Penyiaran Radio Komunitas Wilayah Kabupaten Batang." Jurnal ELTIKOM 5, no. 2 (2021): 73–80. http://dx.doi.org/10.31961/eltikom.v5i2.361.

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The radio frequency spectrum constitutes a limited and strategic natural resource with high economic value, so it must be managed effectively and efficiently to obtain optimal benefits by observing national and international legal principles. Radio Community Broadcasting Institution uses limited frequency allocation in three channels, namely, in the frequency channels 202 (107.7 MHz), 203 (107.8 MHz), and 204 (107.9 MHz), with limited transmit power and area coverage. The problem in this research is the frequency overlap with other community radios in an area. The issue raised is whether it is
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27

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 (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 th
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28

Destexhe, Alain. "Crise économique et financement de la santé." Politique africaine 28, no. 1 (1987): 53–64. http://dx.doi.org/10.3406/polaf.1987.5131.

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La santé a une dimension économique et la crise actuelle appelle une révision des stratégies sanitaires, dans le sens d’une meilleure allocation des ressources. On se trouve devant une alternative cruelle : ou des services gratuits accessibles à tous mais démunis de tout, ou des services partiellement payants susceptibles de fournir un minimum de prestations.
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29

Zhou, Fuhui, Zan Li, Jiangbo Si, Boyang Liu, and Haiyan Huang. "Optimal power allocation for multiple input single output cognitive radios with antenna selection strategies." IET Communications 9, no. 10 (2015): 1337–45. http://dx.doi.org/10.1049/iet-com.2014.0939.

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30

Guo, Songtao, Chuangyin Dang, and Xiaofeng Liao. "Distributed resource allocation with fairness for cognitive radios in wireless mobile ad hoc networks." Wireless Networks 17, no. 6 (2011): 1493–512. http://dx.doi.org/10.1007/s11276-011-0360-9.

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31

Eghbali, Amir, Håkan Johansson, Per Löwenborg, and Heinz G. Göckler. "Dynamic Frequency-Band Reallocation and Allocation: from Satellite-Based Communication Systems to Cognitive Radios." Journal of Signal Processing Systems 62, no. 2 (2009): 187–203. http://dx.doi.org/10.1007/s11265-009-0348-1.

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32

Shiung, David, Ya-Yin Yang, and Chu-Sing Yang. "Transmit Power Allocation for Cognitive Radios Under Rate Protection Constraints: A Signal Coverage Approach." IEEE Transactions on Vehicular Technology 62, no. 8 (2013): 3685–99. http://dx.doi.org/10.1109/tvt.2013.2257907.

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33

M, Anusha, Srikanth Vemuru, and T. Gunasekhar. "Transmission protocols in Cognitive Radio Mesh Networks." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (2015): 1446. http://dx.doi.org/10.11591/ijece.v5i6.pp1446-1451.

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A Cognitive Radio (CR) is a radio that can adjust its transmission limit based on available spectrum in its operational surroundings. Cognitive Radio Network (CRN) is made up of both the licensed users and unlicensed users with CR enable and disabled radios. CR’S supports to access dynamic spectrum and supports secondary user to access underutilized spectrum efficiently, which was allocated to primary users. In CRN’S most of the research was done on spectrum allocation, spectrum sensing and spectrum sharing. In this literature, we present various Medium Access (MAC) protocols of CRN’S. This st
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34

Silva, Francisco S. S., Andson M. Balieiro, Francisco F. de Mendoça, Kelvin L. Dias, and Paulo Guarda. "A Conformance Testing Methodology and System for Cognitive Radios." Wireless Communications and Mobile Computing 2021 (January 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/8869104.

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The fifth generation (5G) of mobile networks has started its operation in some countries and is aimed at meeting demands beyond the current system capabilities such as the huge amount of connected devices from IoT applications (e.g., smart cities), explosive growth of high-speed mobile data traffic (e.g., ultrahigh definition video streaming), and ultrareliable and low latency communication (e.g., autonomous vehicle). To attend to these needs, the electromagnetic spectrum must be made available, but the static spectrum allocation policy has caused a spectrum shortage and impaired the employmen
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35

Etner, Johanna. "Allocation optimale des ressources et préférences à la Selden-Kreps et Porteus." Revue économique 49, no. 4 (1998): 967–87. http://dx.doi.org/10.3406/reco.1998.410026.

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36

Etner, Johanna. "Allocation optimale des ressources et préférences à la Selden-Kreps et Porteus." Revue économique 49, no. 4 (1998): 967. http://dx.doi.org/10.2307/3502604.

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37

Lajoie, Andrée. "La macro-allocation des ressources et le droit aux services de santé." Revue de droit. Université de Sherbrooke 20, no. 2 (1990): 231–48. http://dx.doi.org/10.17118/11143/13554.

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38

Lajoie, Andrée. "LA MACRO-ALLOCATION DES RESSOURCES ET LE DROIT AUX SERVICES DE SANTE." Revue de droit de l'Université de Sherbrooke 20, no. 2 (1990): 231. http://dx.doi.org/10.7202/1108594ar.

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39

Etner, Johanna. "Allocation optimale des ressources et préférences à la Selden-Kreps et Porteus." Revue économique 49, no. 4 (1998): 967–87. http://dx.doi.org/10.3917/reco.p1998.49n4.0967.

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40

Bakht, Khush, Furqan Jameel, Zain Ali, et al. "Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks." Wireless Communications and Mobile Computing 2019 (November 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/2472783.

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The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment soluti
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41

Musavian, Leila, and Tho Le-Ngoc. "QoS-based power allocation for cognitive radios with AMC and ARQ in Nakagami-mfading channels." Transactions on Emerging Telecommunications Technologies 27, no. 2 (2014): 266–77. http://dx.doi.org/10.1002/ett.2853.

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42

Zhang, Er Qing, Si Xing Yin, Liang Yin, and Shu Fang Li. "Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks with Imperfect Spectrum Sensing." Advanced Materials Research 846-847 (November 2013): 635–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.635.

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With the rapid development of wireless network technologies and proliferation of related services such as multimedia applications, demands for wireless spectrum resources keep rising. Cognitive radio (CR) is a novel approach for better utilization of the scarce, already packed but highly underutilized radio spectrum. Meanwhile, exclusive functionalities such as spectrum sensing make energy efficiency (EE) a crucial issue in Cognitive Radios (CRs). In this paper, we focus on the energy-efficient power allocation for OFDM-based CRs with imperfect spectrum sensing. The EE maximization for seconda
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43

Bergeron, Richard. "La restructuration de l’habitat au Burkina-Faso : un moyen pour la révolution." I. Politiques d’État et stratégies de survie : l’enjeu du logement, no. 17 (December 18, 2015): 43–53. http://dx.doi.org/10.7202/1034367ar.

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Un des moyens d’implanter la révolution au Burkina-Faso (ancienne Haute-Volta) a consisté à restructurer les secteurs d’habitat spontané de la capitale, Ouagadougou, où loge 60 % de la population. Pour résoudre les problèmes d’habitat des couches populaires, le régime burkinabé a misé sur une approche collective au logement, visant à faire émerger une conscience collective de l’habitat. Mais des blocages ont empêché sa stratégie de se réaliser : blocages au point de vue de l’interprétation des objectifs par les résidants, blocages liés à l’ampleur des ressources humaines, techniques et logisti
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44

Gougeon, Patrick. "Assurance et diversification." L'Actualité économique 63, no. 2-3 (2009): 187–99. http://dx.doi.org/10.7202/601417ar.

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Résumé Le domaine du risk management se limite traditionnellement aux risques purs, par opposition aux risques spéculatifs qui proviennent des aléas du marché. L’objectif de cet article est de mettre en évidence la perte d’efficience qui peut résulter d’une gestion cloisonnée et indépendante de ces deux catégories de risques. Nous proposons un modèle global, inspiré de l’approche portefeuille, au sein duquel différents types d’actions influençant le niveau de risque de la firme sont envisagées simultanément : allocation des ressources entre plusieurs activités, choix d’une structure financière
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45

Yadav, Savita, Pradeep Kumar Shah, Sowmiya Kumar, and Anjali Singh. "Enhancing resource allocation for power sharing in cognitive radio communication networks using ensemble moth-flame optimized dynamic recurrent neural networks." Multidisciplinary Science Journal 6 (July 12, 2024): 2024ss0307. http://dx.doi.org/10.31893/multiscience.2024ss0307.

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Wireless data transmission networks are radio communication networks (RCN). These networks allow radios as well as phones to communicate, exchange data and calls across radio waves. Broadcasting, emergency services and mobile telecommunications are among the numerous industries that use RCN's flexibility and ease. Our proposed method, Ensemble Moth-Flame Optimized Dynamic Recurrent Neural Network (EMFO-DRNN), addresses the challenges inherent in DNN-based systems, maximizing power distribution, minimizing interference and optimizing spectrum utilization. This solution enables adaptive channel
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46

Jagannath, Jithin. "Framework for Federated Learning and Edge Deployment of Real-Time Reinforcement Learning Decision Engine on Software Defined Radio." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 304. http://dx.doi.org/10.1609/aaaiss.v3i1.31218.

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Machine learning promises to empower dynamic resource allocation requirements of Next Generation (NextG) wireless networks including 6G and tactical networks. Recently, we have seen the impact machine learning can make on various aspects of wireless networks. Yet, in most cases, the progress has been limited to simulations and/or relies on large processing units to run the decision engines as opposed to deploying it on the radio at the edge. While relying on simulations for rapid and efficient training of deep reinforcement learning (DRL) may be necessary, it is key to mitigate the sim-real ga
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47

Tlouyamma, Joseph, and Mthulisi Velempini. "Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks." Wireless Personal Communications 119, no. 4 (2021): 3161–78. http://dx.doi.org/10.1007/s11277-021-08392-5.

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AbstractA major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel all
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48

M., K. Kaushik, Yoganandam Y., and K. Sahoo S. "Sensing and Sharing Schemes for Spectral Efficiency of Cognitive Radios." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2934–41. https://doi.org/10.11591/ijece.v8i5.pp2934-2941.

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Increase in data traffic, number of users and their requirements laid to a necessity of more bandwidth. Cognitive radio is one of the emerging technology which addresses the spectrum scarcity issue. In this work we study the advantage of having collaboration between cognitive enabled small cell network and primary macrocell. Different from the existing works at spectrum sensing stage we are applying enhanced spectrum sensing to avoid probability of false alarms and missed detections which has impact on spectral efficiency. Later power control optimization for secondary users known as Hybrid sp
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49

MOKHEFI, Amine, and Mohamed AMRANI. "Accroître l'efficacité du marché financier algérien et révision de la politique financière Une option pour sortir de l’économie d’endettement." Dirassat Journal Economic Issue 7, no. 3 (2016): 423–33. http://dx.doi.org/10.34118/djei.v7i3.515.

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Le marché financier algérien a pris beaucoup d’ampleur au cours de ces dernières années. Bien que certains facteurs limitent la portée de son évolution, le marché est devenu un moyen de financement qui s’affirme comme le canal par excellence pour une meilleure allocation des ressources.
 Cette recherche a pour objet de présenter un reflet fidèle de l’évolution du marché financier algérien à travers ses compartiments, ses acteurs et aussi les différentes réformes qui ont été émises. L’accent est mis sur les limites qui continuent à influer sur son développement.
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50

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 (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 bandwidt
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