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Journal articles on the topic 'Allocation des ressources radio'

Author: Grafiati

Published: 25 January 2025

Last updated: 31 July 2025

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1

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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2

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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3

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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4

Bello, N., and F. O. Edeko. "Designing a Spectrum Allocation Chart for Nigeria." Nigerian Journal of Environmental Sciences and Technology 5, no. 2 (2021): 320–28. http://dx.doi.org/10.36263/nijest.2021.02.0277.

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The regulation of the radio spectrum today by government agencies addresses the issue of interference between radio stations by allocating, allotting and registering bands of radio-frequencies. The framework of management is done at the international, regional and national level. With this paper, we present a study of the radio spectrum allocation policies in some leading countries and Nigeria. However, narrowing the study down to the detailed design of spectrum allocation charts. The study used the information in the national table of frequency allocation (NTFA) to design the spectrum allocat

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5

FREYENS, BENOÎT PIERRE, and CHRIS JONES. "Efficient Allocation of Radio Spectrum." Journal of Public Economic Theory 16, no. 1 (2013): 1–23. http://dx.doi.org/10.1111/jpet.12045.

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6

Kloeck, Clemens, Holger Jaekel, and Friedrich Jondral. "Multi-Agent Radio Resource Allocation." Mobile Networks and Applications 11, no. 6 (2006): 813–24. http://dx.doi.org/10.1007/s11036-006-0051-4.

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7

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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8

Li, Yongcheng, Hai Shen, and Manxi Wang. "Spectrum Allocation of Cognitive Radio Based on Autonomy Evolutionary Algorithm." Cybernetics and Information Technologies 16, no. 4 (2016): 87–97. http://dx.doi.org/10.1515/cait-2016-0070.

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Abstract Reasonable and effective allocation of cognitive radio spectrum resource according to user’s requirements is the key task of cognitive radio network. Cognitive radio spectrum allocation problem can be viewed as an optimization problem. This paper analyzes the application of bio-inspired intelligent algorithm in cognitive radio network spectrum allocation, and based on graph theory model of spectrum allocation, proposesaspectrum allocation algorithm based on autonomously evolutionary scheme. Three objective functions: Max-Min-Reward, Max-Sum- Reward and Max-Proportional-Fair are employ

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9

Zhang, Pei, Long Xiang Yang, and Xu Liu. "Subcarrier Allocation in Cognitive Radio Systems." Applied Mechanics and Materials 195-196 (August 2012): 154–58. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.154.

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In CR systems, resource allocation is very important, which can be operated by different rules, according to the different purposes. In this paper, we focus on subcarrier allocation in CR systems, where primary and CR users co-exist in adjacent bands, while keeping the total interference introduced to the PU band below a certain threshold and the total power allocated to the CR users under a constraint. First, we investigate Max-Rate subcarrier allocation algorithm (MaxR-SAA) which can achieve maximum transmit rate of the CR system. Then, focusing on the fact that the less interference introdu

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10

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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11

Xie, Yu Peng, Xue Zhi Tan, Yu Tao Liu, Lin Ma, and Hai Yan Wu. "A Spectrum Allocation Algorithm Based on Graph Theory." Applied Mechanics and Materials 157-158 (February 2012): 1065–70. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1065.

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In the cognitive radio system allocation algorithm based on graph theory is always research focus at home and abroad. Cognitive radio users choose main users’ spectrum which don’t use spectrum, but the available spectrum is affected on main users’ work state, location, and coverage, with the changing nature of space and time. Therefore, graph theory model analyzes spectrum allocation in the cognitive radio system, which revises constraint conditions and distribution targets of spectrum allocation. This paper summarizes the principle of existing allocation algorithm on graph theory model on cog

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12

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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13

Siebert, Patrick, Mustapha Mouloua, Kendra Burns, et al. "The Effects of Telematics on Driver Distraction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 22 (2002): 1868–70. http://dx.doi.org/10.1177/154193120204602219.

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This study used both cellular phones and analogue radio to measure driver distraction and workload in a low fidelity driving simulator. Thirty-four participants performed a simulated driving task while using either a cell phone or a radio in conjunction with a secondary task assessing their spare attentional capacity. The results showed that more lane deviations were made during the cell phone and radio tuning use than both of the pre-allocation and Post-allocation phases. The secondary task errors were also higher during both the cell phone and radio tuning allocation phase than the pre-alloc

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14

Grandhi, S. A., R. D. Yates, and D. J. Goodman. "Resource allocation for cellular radio systems." IEEE Transactions on Vehicular Technology 46, no. 3 (1997): 581–87. http://dx.doi.org/10.1109/25.618182.

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15

Dettmer, R. "Up the revolution [radio spectrum allocation]." IEE Review 51, no. 5 (2005): 44–47. http://dx.doi.org/10.1049/ir:20050506.

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16

Chang, Kun-Nyeong, and Sehun Kim. "Channel allocation in cellular radio networks." Computers & Operations Research 24, no. 9 (1997): 849–60. http://dx.doi.org/10.1016/s0305-0548(96)00098-6.

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17

Ma, Lu, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp, and Irfan Ghauri. "A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet." Wireless Communications and Mobile Computing 2018 (September 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/1745904.

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Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic-aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resour

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18

Mahyari, Mohammad Mirtavoosi, Arman Shojaeifard, and Mohammad Shikh-Bahaei. "Probabilistic Radio Resource Allocation Over CDMA-Based Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 64, no. 8 (2015): 3560–65. http://dx.doi.org/10.1109/tvt.2014.2360814.

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19

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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20

Shin, Jung-Chae, Seung-Hwa Min, Ho-Shin Cho, and Youn-Seon Jang. "Efficient Radio Resource Allocation for Cognitive Radio Based Multi-hop Systems." Journal of Korean Institute of Communications and Information Sciences 37, no. 5A (2012): 325–38. http://dx.doi.org/10.7840/kics.2012.37a.5.325.

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21

Alsenwi, Madyan, Kitae Kim, and Choong Seon Hong. "Radio Resource Allocation in 5G New Radio: A Neural Networks Approach." Journal of KIISE 46, no. 9 (2019): 961–67. http://dx.doi.org/10.5626/jok.2019.46.9.961.

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22

Tong, Shiyuan, Yun Liu, Hsin-Hung Cho, Hua-Pei Chiang, and Zhenjiang Zhang. "Joint radio resource allocation in fog radio access network for healthcare." Peer-to-Peer Networking and Applications 12, no. 5 (2018): 1277–88. http://dx.doi.org/10.1007/s12083-018-0707-4.

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23

Almalfouh, S. M., and Gordon L. Stüber. "Interference-Aware Radio Resource Allocation in OFDMA-Based Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 60, no. 4 (2011): 1699–713. http://dx.doi.org/10.1109/tvt.2011.2126613.

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24

Ahmad, Ayaz, Sadiq Ahmad, Mubashir Husain Rehmani, and Naveed Ul Hassan. "A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks." IEEE Communications Surveys & Tutorials 17, no. 2 (2015): 888–917. http://dx.doi.org/10.1109/comst.2015.2401597.

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25

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 (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 enhanc

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26

Liu, Jing Xue, Zhi Bo Li, and Hong Li. "Dynamic Allocation of Radio Frequency in the Battlefield Environment." Applied Mechanics and Materials 197 (September 2012): 496–501. http://dx.doi.org/10.4028/www.scientific.net/amm.197.496.

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Dynamic allocation of Radio Frequency (Hereafter called “RF”) is critical in the battlefield spectrum management. The article analyzes the conventional method of RF dynamic allocation in the battlefield environment, and set up the mathematic model of the RF dynamic allocation by using the results of spectrum detected in battlefield. It designs the algorithms with the combination of Genetic Algorithms and Tabu Search Algorithms. The simulation experiment proves the high efficiency of hybrid algorithms and it suit for solving the RF dynamic allocation problem in the battlefield environment.

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27

Husar, Andrej, Peter Cepel, Peter Brida, and Vladimir Wieser. "Radio Resource Allocation in Mobile Communication Systems." Communications - Scientific letters of the University of Zilina 7, no. 4 (2005): 66–68. http://dx.doi.org/10.26552/com.c.2005.4.66-68.

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28

Helmy, Maram, Mohamed S. Hassan, and Mahmoud H. Ismail. "Spectrum Allocation Techniques for Cognitive Radio Networks." IEEE Access 10 (2022): 28180–93. http://dx.doi.org/10.1109/access.2022.3157340.

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29

Zhao, Zhijin, Zhen Peng, Shilian Zheng, and Junna Shang. "Cognitive radio spectrum allocation using evolutionary algorithms." IEEE Transactions on Wireless Communications 8, no. 9 (2009): 4421–25. http://dx.doi.org/10.1109/twc.2009.080939.

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30

Jain, Madhu. "Channel allocation policy in cellular radio network." Applied Mathematical Modelling 29, no. 1 (2005): 65–83. http://dx.doi.org/10.1016/j.apm.2004.07.003.

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31

Liang, Jui-Chi, and Jyh-Cheng Chen. "Resource Allocation in Cognitive Radio Relay Networks." IEEE Journal on Selected Areas in Communications 31, no. 3 (2013): 476–88. http://dx.doi.org/10.1109/jsac.2013.130313.

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32

Zhang, Rui, Ying-chang Liang, and Shuguang Cui. "Dynamic Resource Allocation in Cognitive Radio Networks." IEEE Signal Processing Magazine 27, no. 3 (2010): 102–14. http://dx.doi.org/10.1109/msp.2010.936022.

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33

Wang, Qinhui, Baoliu Ye, Tianyin Xu, Sanglu Lu, and Song Guo. "Approximately Truthful Mechanisms for Radio Spectrum Allocation." IEEE Transactions on Vehicular Technology 64, no. 6 (2015): 2615–26. http://dx.doi.org/10.1109/tvt.2014.2345418.

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34

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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35

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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36

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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37

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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38

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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39

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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40

R G, Umesh, Sushil Kumar G N, Santhosh K, Suraksha M S, and Dr Praveen Kumar K V. "Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 677–83. http://dx.doi.org/10.22214/ijraset.2023.49468.

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Abstract: Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resou

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41

de Guise, Jacques. "Langue de diffusion et ressources publicitaires des media." Articles 13, no. 1 (2005): 91–105. http://dx.doi.org/10.7202/055560ar.

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Les mass média prennent une part de plus en plus grande dans la production et la diffusion des œuvres culturelles et de l'information dans notre société. Les ressources financières qui supportent ces média deviennent alors, pour une part de plus en plus importante, les ressources même de la culture. Dans notre système actuel de diffusion, les média vivent d'abord et avant tout de publicité. Certes ils ne vivent pas uniquement de publicité : en radio et en télévision nous avons instauré un système où l'État intervient dans le financement des stations ; dans les journaux, le public lecteur fourn

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42

Razmi, Shirin, and Naser Parhizgar. "Adaptive resources assignment in OFDM-based cognitive radio systems." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1935. http://dx.doi.org/10.11591/ijece.v9i3.pp1935-1943.

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Spectrum efficiency of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems can be improved by adaptive resources allocation. In resources allocation, transmission resources such as modulation level and transmission power are adaptively assigned based on channel variations. The goal of this paper is maximize the total transmission rate of secondary user (SU). Hence, we investigate adaptive power and modulation allocation to achieve this purpose. For power allocation, we investigate optimal and conventional methods and then introduce a novel suboptimal algorithm

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43

Shirin, Razmi, and Parhizgar Naser. "Adaptive resources assignment in OFDM-based cognitive radio systems." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1935–43. https://doi.org/10.11591/ijece.v9i3.pp1935-1943.

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Spectrum efficiency of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems can be improved by adaptive resources allocation. In resources allocation, transmission resources such as modulation level and transmission power are adaptively assigned based on channel variations. The goal of this paper is maximize the total transmission rate of secondary user (SU). Hence, we investigate adaptive power and modulation allocation to achieve this purpose. For power allocation, we investigate optimal and conventional methods and then introduce a novel suboptimal algorithm

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44

Yang, Xiao Long, Lin Ma, and Xue Zhi Tan. "Power-Weight Allocation Algorithm for OFDM-Based Cognitive Radio System." Advanced Materials Research 546-547 (July 2012): 932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.932.

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Cognitive Radio (CR) has been proposed as one of the most promising technologies to provide efficient utilization of the limited wireless spectrum. In this paper, we investigate the power allocation for CR system based on orthogonal frequency division multiplexing (OFDM), and an improved allocation algorithm called power-weight algorithm is proposed. This algorithm performs initial allocation via Lagrange multiplier method, and then accomplishes second allocation based on power weight obtained in initial allocation. The analytical results reveal that the total transmission rate of all subcarri

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45

Al-Medhwahi, Mohammed, Fazirulhisyam Hashim, Borhanuddin Mohd Ali, A. Sali, and Abdulsalam Alkholidi. "Resource allocation in heterogeneous cognitive radio sensor networks." International Journal of Distributed Sensor Networks 15, no. 7 (2019): 155014771985194. http://dx.doi.org/10.1177/1550147719851944.

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Cognitive radio sensor networks offer a promising means of meeting rapidly expanding demand for wireless sensor network applications in new monitoring and objects tracking fields. Several challenges, particularly in terms of quality of service provisioning, arise because of the inherited capability-limitation of end-sensor nodes. In this article, an efficient resource allocation scheme, improved Pliable Cognitive Medium Access Protocol, is proposed to tackle multilevel of heterogeneity in cognitive radio sensor networks. The first level is the network’s application heterogeneity, and the secon

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46

Hernandez, Cesar, Jorge Rodriguez, and Diego Giral. "Spectrum allocation model for cognitive wireless networks based on the artificial bee colony algorithm." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 257. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp257-266.

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Cognitive radio through dynamic spectrum allocation allows an efficient use of the radio-electric spectrum. It is a key subject for the performance of cognitive radio networks. The purpose of the present article is to develop a spectrum allocation model for cognitive wireless networks based on the Artificial Bee Colony algorithm and assess its performance in spectrum occupancy traces obtained from monitoring the spectrum using the energy detection technique. Results show a reduction in the number of spectral handoff with no excessive execution times.

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47

César, Hernández, Rodríguez Jorge, and Giral Diego. "Spectrum allocation model for cognitive wireless networks based on the artificial bee colony algorithm." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 257–66. https://doi.org/10.11591/ijeecs.v19.i1.pp257-266.

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Cognitive radio through dynamic spectrum allocation allows an efficient use of the radio-electric spectrum. It is a key subject for the performance of cognitive radio networks. The purpose of the present article is to develop a spectrum allocation model for cognitive wireless networks based on the Artificial Bee Colony algorithm and assess its performance in spectrum occupancy traces obtained from monitoring the spectrum using the energy detection technique. Results show a reduction in the number of spectral handoff with no excessive execution times.

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48

El Misilmani, H. M., M. Y. Abou-Shahine, Y. Nasser, and K. Y. Kabalan. "Recent Advances on Radio-Frequency Design in Cognitive Radio." International Journal of Antennas and Propagation 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/9878475.

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With the growth of mobile data applications, the spectrum allocation is becoming very scarce. To ease congestion and boost speeds, cognitive radio (CR) is currently seen as a major solution and expected to be the key player in the new wireless technologies. In this paper, we will start by introducing the cognitive radio systems, followed by exploring the challenges in designing RF engine, along with an investigation of its antennas, amplifiers, oscillators, and the components that are expected to operate over a wide range of frequencies.

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49

Devi, Monisha, Nityananda Sarma, and Sanjib K. Deka. "A Centralized Model Enabling Channel Reuse for Spectrum Allocation in Cognitive Radio Networks." Cybernetics and Information Technologies 21, no. 2 (2021): 183–200. http://dx.doi.org/10.2478/cait-2021-0026.

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Abstract Cognitive Radio (CR) is an advanced technology, which intends to boost the radio spectrum utilization. On perceiving the spectrum holes, next there is a need to provide a fair distribution of the vacant licensed channels amongst Secondary Users (SUs) during the spectrum allocation process. In this context, our paper introduces two allocation models to resolve the spectrum allocation problem. Initially, we design a simple centralized model to assign the channels. Then, we extend it to a centralized fair allocation model that aims to impart a better utilization of the free channels. Bot

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50

Sun, Yaohua, Mugen Peng, Shiwen Mao, and Shi Yan. "Hierarchical Radio Resource Allocation for Network Slicing in Fog Radio Access Networks." IEEE Transactions on Vehicular Technology 68, no. 4 (2019): 3866–81. http://dx.doi.org/10.1109/tvt.2019.2896586.

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