Literatura académica sobre el tema "Allocation des ressources radios"
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Artículos de revistas sobre el tema "Allocation des ressources radios"
Chung, J. M., C. H. Kim, J. H. Park, J. Shin y D. Kim. "Optimal channel allocation for cognitive radios". Electronics Letters 46, n.º 11 (2010): 802. http://dx.doi.org/10.1049/el.2010.0829.
Texto completoPrabhjot Kaur, Moin Uddin y Arun Khosla. "Adaptive Bandwidth Allocation Scheme for Cognitive Radios". International Journal of Advancements in Computing Technology 2, n.º 2 (30 de junio de 2010): 35–41. http://dx.doi.org/10.4156/ijact.vol2.issue2.3.
Texto completoBalassa, Bela. "Politiques agricoles et allocation internationale des ressources". Économie rurale 189, n.º 1 (1989): 22–28. http://dx.doi.org/10.3406/ecoru.1989.3948.
Texto completoYang, Zhou, Wenqian Jiang y Gang Li. "Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization". Wireless Communications and Mobile Computing 2018 (5 de julio de 2018): 1–16. http://dx.doi.org/10.1155/2018/1327030.
Texto completoKim, Seung-Jun, Nasim Yahya Soltani y Georgios B. Giannakis. "Resource Allocation for OFDMA Cognitive Radios Under Channel Uncertainty". IEEE Transactions on Wireless Communications 12, n.º 7 (julio de 2013): 3578–87. http://dx.doi.org/10.1109/twc.2013.062413.121892.
Texto completoBazerque, Juan-Andrés y Georgios B. Giannakis. "Distributed Scheduling and Resource Allocation for Cognitive OFDMA Radios". Mobile Networks and Applications 13, n.º 5 (23 de julio de 2008): 452–62. http://dx.doi.org/10.1007/s11036-008-0083-z.
Texto completoLuo, Rong-hua y Zhen Yang. "Stackelberg Game-based Distributed Power Allocation Algorithm in Cognitive Radios". Journal of Electronics & Information Technology 32, n.º 12 (25 de enero de 2011): 2964–69. http://dx.doi.org/10.3724/sp.j.1146.2010.00374.
Texto completoAzmat, Freeha, Yunfei Chen y Nigel Stocks. "Bio-inspired collaborative spectrum sensing and allocation for cognitive radios". IET Communications 9, n.º 16 (5 de noviembre de 2015): 1949–59. http://dx.doi.org/10.1049/iet-com.2014.0769.
Texto completoLiu, Yicong, Muhammad Iqbal, Muhammad Naeem, Alagan Anpalagan y N. N. Qadri. "Resource Allocation in Hospital Networks Based on Green Cognitive Radios". Wireless Personal Communications 85, n.º 3 (30 de junio de 2015): 1487–507. http://dx.doi.org/10.1007/s11277-015-2852-x.
Texto completoJian Tang, R. Hincapie, Guoliang Xue, Weiyi Zhang y R. Bustamante. "Fair Bandwidth Allocation in Wireless Mesh Networks With Cognitive Radios". IEEE Transactions on Vehicular Technology 59, n.º 3 (marzo de 2010): 1487–96. http://dx.doi.org/10.1109/tvt.2009.2038478.
Texto completoTesis sobre el tema "Allocation des ressources radios"
Enderle, Nicolas. "Allocation de ressources radios pour les services paquets dans l'umts". Paris, ENST, 2003. http://www.theses.fr/2003ENST0004.
Texto completoIn this thesis, we study scheduling policies on the umts radio interface for packet-switched services like web browsing. Moreover, we analyse the interaction between resource allocation algorithms and protocols involved in end-to-end data transmission like tcp (transmission control protocol) and rlc (radio link control). First, we model the radio resource consumption induced by active users. We then introduce a new factor : the total interference factor the radio efficiency factor. It represents the cost in radio resource per unit of throughput in order to serve a given user. Then, by formulating the resource allocation problem as an optimization problem of users' satisfaction, we present an optimal solution based on the ratio user satisfaction/radio efficiency. Thanks to these results, we build a dynamic allocation algorithm and compare our solution with existing ones from the litterature. The impact of tcp and rlc protocols is taken into account in this study
Enderlé, Nicolas. "Allocation de ressources radios pour les services paquets dans l'UMTS /". Paris : École nationale supérieure des télécommunications, 2003. http://catalogue.bnf.fr/ark:/12148/cb39085229r.
Texto completoNasreddine, Jad. "Allocation de ressources radios dans les systèmes UMTS à duplexage temporel". Rennes 1, 2005. http://www.theses.fr/2005REN1S009.
Texto completoVivier, Emmanuelle. "Allocation de ressources radios dans les réseaux cellulaires paquets de 2. 5 et 3ème génération". Paris, CNAM, 2004. http://www.theses.fr/2004CNAM0477.
Texto completoOur work focuses on the sharing of the system’s radio resources between all active users in a packet-switching network. In the part dedicated to GPRS, all the possible resources repartitions are enumerated. The optimal allocation of the system’s resources, depending on users requests, is identified by an exhaustive search. We propose a faster process, requiring less computation and yet leading to the determination of the same optimal allocation. The second part focuses on UMTS. Real-time constrained communications are served with the highest priority and non real-time constrained services use the leftover capacity. We propose allocation algorithms that maximize the aggregate uplink and downlink throughputs, or the number of simultaneously served flows, with standardized spreading factor values for actual UMTS networks. Those algorithms’ performances are compared with the optimal ones that provide theoretical bounds only and do not yield to standardized spreading factor values
Allouch, Mahdi Mariem. "Gestion intelligente des ressources radios dans les réseaux véhiculaires de la 4G vers la 5G". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG061.
Texto completoVehicular networks are a class of mobile networks allowing vehicles to communicate with each other in the context of high spatial mobility, as well as with cellular networks and communication networks deployed on the road infrastructure. In order to support Level 5 autonomous vehicle communications, these networks need to provide a QoS that addresses the time-critical constraints of communications while ensuring a high level of integrity of the data exchanged. The LTE technology, used in cellular mobile networks with a strict QoS, has been selected by 3GPP (Release 14) for communication in vehicular networks under the reference LTE-V2X/ cellular V2X. Release 14 introduces two modes (3,4) of LTE communication specifically designed for V2V communication. In mode 3, radio channel selection is managed by the eNodeB base station. In Mode 4, vehicles select their radio resources autonomously regardless of any cellular network coverage. In the literature, different resource allocation algorithms for modes 3 and 4 have been proposed.In the first part of the thesis, we focus on mode 3 addressing the requirements of monitoring level 5 autonomous vehicles through the infrastructure deployed on the road and in the Cloud. An exhaustive study of the existing proposals in the literature shows that the majority of the proposed solutions only deal with periodic messages (non-safety e.g. CAM) while ensuring a minimum of security. Therefore, we introduced aperiodic messages (safety ex. DENM) which are generated in critical situations (accident, traffic jam). We proposed a resource allocation policy based on a priority system with a strict guarantee of minimum capacity for critical applications and a dynamic sharing of the remaining capacity with other applications. We also proposed a new resource reuse technique for both types of messages (critical and less critical) that allows efficient use of network capacity while satisfying the requirements of critical applications without affecting less critical applications.LTE-V technology presents an important step towards the V2X/5G network. This 5G network offers, through URLLC, high integrity and low latency for real-time critical applications. Furthermore, with the concept of "Network Slicing", the functional architecture of the 5G network offers the portability of the vehicular network with its services alongside other service networks within the 5G mobile network. We have chosen to integrate the 5G vehicular network architecture in the same slice at the access network level which allows to benefit from the statistical gain in terms of radio resources utilization. We focused on the MAC and physical NR layers. We studied the dynamic allocation of radio resources between critical URLLC communications and streaming communications carried in the same Slice. The scheduler used for resource allocation is specified to dynamically manage spectral resources between critical URLLC flows and streaming flows exchanged between vehicles and application servers. We proposed several statistical models of exchanged flows and analyzed by simulation the QoS offered in the access network to critical/streaming flows. We have also proposed a quasi-exact Markovian analytical model of the MAC/Physical layer traversal of the URLLC flow with the objective of dimensioning an admission control mechanism (CAC) of the critical flows in the slice and to guarantee the QoS required by these flows
LE, BRIS LOIC. "Allocation de ressources radio dans les reseaux cellulaires". Paris 7, 1999. http://www.theses.fr/1999PA077136.
Texto completoMaaz, Bilal. "Allocation des ressources radio dans les réseaux sans fil de la 5 G". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV010/document.
Texto completoMobile communication is considered as one of the building blocks of smart cities, where citizens should be able to benefit from telecommunications services, wherever they are, whenever they want, and in a secure and non-costly way. This can be done by dense deployment of the latest generation of mobile broadband networks. However, this dense deployment will lead to higher energy consumption, and thus more gas emission and pollution. Therefore, it is crucial from environmental point of view to propose solution reducing energy consumption. In this thesis, we introduce dynamic resource management methods that increase throughput and energy efficiency, and thus reduce pollution. In this framework, we are targeting green multi-cell networks where increased energy efficiency must take into account the increased demand of data by mobile users. This increase, which is exponential in terms of throughput, pushed operators to use the entire frequency spectrum in all cells of the latest generation of mobile networks. As a result, Inter-Cellular Interference (ICI) became preponderant and degraded the performance of users, particularly those with poor radio conditions. In this thesis, we focus on the techniques of power control on the downlink direction, which is considered as one of the key methods of Inter-Cell Interference Coordination (ICIC) while focusing on energy efficient methods. We propose centralized and decentralized methods for this problem of power allocation: centralized methods through convex optimization, and decentralized methods based on non-cooperative game theory. Furthermore, we propose a power control heuristic which has the advantage of being stable and based on signaling messages already existing in the system. The power control problem has a relevant impact on the allocation of radio resources and on the association of mobile users with their servicing Base Station. Therefore, in the second part of the thesis, we formulated a global problem encompassing power control, radio resources allocation, and control of users’ association to a base station. These three sub-problems are treated iteratively until the convergence to the overall solution. In particular, we propose three algorithms for the user association problem: a centralized algorithm, a semi-distributed algorithm and finally a fully distributed algorithm based on reinforcement learning. In addition, for power allocation we implement centralized solutions and distributed solutions. The proof of convergence for the various algorithms is established and the in-depth simulations allow us to evaluate and compare quantitatively the performance, the energy efficiency, and the convergence time of the proposed algorithms
Sharara, Mahdi. "Resource Allocation in Future Radio Access Networks". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG024.
Texto completoThis dissertation considers radio and computing resource allocation in future radio access networks and more precisely Cloud Radio Access Network (Cloud-RAN) and Open Radio Access Network (Open-RAN). In these architectures, the baseband processing of multiple base stations is centralized and virtualized. This permits better network optimization and allows for saving capital expenditure and operational expenditure. In the first part, we consider a coordination scheme between radio and computing schedulers. In case the computing resources are not sufficient, the computing scheduler sends feedback to the radio scheduler to update the radio parameters. While this reduces the radio throughput of the user, it guarantees that the frame will be processed at the computing scheduler level. We model this coordination scheme using Integer Linear Programming (ILP) with the objectives of maximizing the total throughput and users' satisfaction. The results demonstrate the ability of this scheme to improve different parameters, including the reduction of wasted transmission power. Then, we propose low-complexity heuristics, and we test them in an environment of multiple services with different requirements. In the second part, we consider the joint radio and computing resource allocation. Radio and computing resources are jointly allocated with the aim of minimizing energy consumption. The problem is modeled as a Mixed Integer Linear Programming Problem (MILP) and is compared to another MILP problem that maximizes the total throughput. The results demonstrate the ability of joint allocation to minimize energy consumption in comparison with the sequential allocation. Finally, we propose a low-complexity matching game-based algorithm that can be an alternative for solving the high-complexity MILP problem. In the last part, we investigate the usage of machine learning tools. First, we consider a deep learning model that aims to learn how to solve the coordination ILP problem, but with a much shorter time. Then, we consider a reinforcement learning model that aims to allocate computing resources for users to maximize the operator's profit
Abgrall, Cédric. "Allocation de ressources dans les réseaux sans fil denses". Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00581776.
Texto completoAbgrall, Cédric. "Allocation de ressources dans les réseaux sans fil denses". Phd thesis, Paris, Télécom ParisTech, 2010. https://pastel.hal.science/pastel-00581776.
Texto completoThis PhD thesis focuses on interference mitigation techniques for wireless communication networks to limit detrimental effects of in-band interference. First, we consider cooperative communication systems and study the trade-off between cooperation benefits and increased level of interference. Cooperation in wireless networks is like a crowded cocktail party with a cacophony of conversations all around. The more people repeat the same information, the more likely you understand it. However, neighbour repeaters act as interferers which harm your understanding. We propose to coordinate and adapt the activation of cooperation and the resource allocation of neighbour cells to time, frequency and space variations of communication context. Second, we propose to classify interference a destination senses on a given frequency band by differentiating three regimes of interference: noisy, intermediate and very strong. This classifier ensures an adaptive and effective processing of in-band interference adapted to time-varying nature of channel. Then, we combine this classifier with QoS constraints to derive centralized and distributed algorithms for power allocation. Both approaches aim at allocating the minimal transmit power vector while meeting QoS requirements of each user, whatever the communication scenario may be. Our simulations show how an adaptive handling of in-band interference may notably reduce the power budget without affecting transmission reliability
Libros sobre el tema "Allocation des ressources radios"
Naoki, Katoh, ed. Resource allocation problems: Algorithmic approaches. Cambridge, Mass: MIT Press, 1988.
Buscar texto completoResource allocation mechanisms. Cambridge [Cambridgeshire]: Cambridge University Press, 1987.
Buscar texto completoBoitte, Pierre. Éthique, justice et santé: Allocation des ressources en soins dans une population vieillissante. Namur [Belgique]: Artel, 1995.
Buscar texto completoReal options: Managerial flexibility and strategy in resource allocation. Cambridge, Mass: MIT Press, 1996.
Buscar texto completoRees, Judith A. Natural resources: Allocation, economics, and policy. 2a ed. London: Routledge, 1990.
Buscar texto completoNatural resources: Allocation, economics, and policy. London: Methuen, 1985.
Buscar texto completosociaux, Québec (Province) Commission d'enquête sur les services de santé et les services. L' allocation des ressources humaines dans les conventions collectives des secteurs de la santé et des services sociaux. [Québec]: Commission d'enquête sur les services de santé et les services sociaux, 1987.
Buscar texto completoWilliam, Easter K., ed. Water allocation and water markets: An analysis of gains-from-trade in Chile. Washington, D.C: World Bank, 1995.
Buscar texto completoNew York State College of Agriculture and Life Sciences. Dept. of Agricultural Economics, ed. Resource economics: Five easy pieces. Ithaca, N.Y: Dept. of Agricultural Economics, New York State College of Agriculture and Life Sciences, Cornell University, 1989.
Buscar texto completoResource economics. Cambrdige, UK: Cambridge University Press, 1999.
Buscar texto completoCapítulos de libros sobre el tema "Allocation des ressources radios"
Venkatesh, Bhukya, Nadella Bala Sai Krishna y Sonali Chouhan. "Distributed Optimal Power Allocation Using Game Theory in Underlay Cognitive Radios". En Advances in Intelligent Systems and Computing, 295–304. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0132-6_21.
Texto completoWang, Hongzhi, Yongfei Yan y Mingyue Zhou. "Resource Allocation in OFDM-Based Cognitive Radios Under Proportional Rate Constraint". En Communications and Networking, 416–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78139-6_42.
Texto completoUddin, Mohammad Faisal, Mohammad Nurujjaman y Chadi Assi. "Joint Scheduling and Spectrum Allocation in Wireless Networks with Frequency-Agile Radios". En Ad-Hoc, Mobile and Wireless Networks, 95–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14785-2_8.
Texto completoLiu, Zhixin, Jinfeng Wang, Hongjiu Yang y Kai Ma. "Joint Rate and Power Allocation for Cognitive Radios Networks with Uncertain Channel Gains". En Foundations and Applications of Intelligent Systems, 309–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37829-4_26.
Texto completoCarrier, Annie y Mélanie Levasseur. "Allocation des ressources ergothérapiques en CLSC : enjeux éthiques et juridiques". En Les enjeux éthiques de la limite des ressources en santé, 51–58. Les Presses de l’Université de Montréal, 2016. http://dx.doi.org/10.1515/9782760634886-006.
Texto completoRaffour, Catherine. "Chapitre 5. Stratégie et allocation des ressources dans six universités européennes". En La Recherche et l’Innovation en France, 131–65. Odile Jacob, 2013. http://dx.doi.org/10.3917/oj.lesou.2013.01.0131.
Texto completoChapuisat, Michel. "Chapitre 11. Allocation différentielle des ressources dans la progéniture mâle et femelle". En Écologie comportementale, 331–63. Dunod, 2021. http://dx.doi.org/10.3917/dunod.danch.2021.01.0331.
Texto completo"Le marché du logement-moins réglementer pour une meilleure allocation des ressources". En Études économiques de l'OCDE : Suède, 119–61. OECD, 2007. http://dx.doi.org/10.1787/eco_surveys-swe-2007-6-fr.
Texto completoAcharya, Tamaghna y Santi P. Maity. "Power Allocation in Cognitive Radio in Energy Constrained Wireless Ad Hoc Networks". En Advances in Wireless Technologies and Telecommunication, 248–70. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4221-8.ch013.
Texto completoAbdelsalam, Hisham M., Haitham S. Hamza, Abdoulraham M. Al-Shaar y Abdelbaset S. Hamza. "On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks". En Multidisciplinary Computational Intelligence Techniques, 202–14. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch012.
Texto completoActas de conferencias sobre el tema "Allocation des ressources radios"
Khabir, Abdelilah, Zoubir Elfelssoufi y Hamid Azzouzi. "Flexible Allocation of Human Ressources Uder Constraints". En 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2019. http://dx.doi.org/10.1109/logistiqua.2019.8907314.
Texto completoZhioua, Ghayet El Mouna, Soumaya Hamouda, Philippe Godlewski y Sami Tabbane. "A femtocells ressources allocation scheme in OFDMA based networks". En 2010 Second International Conference on Communications and Networking (ComNet). IEEE, 2010. http://dx.doi.org/10.1109/comnet.2010.5699812.
Texto completoLabdaoui, Rym, Khalida Ghanem y Fatiha Youcef Ettoumi. "Using market equilibrium optimization technique for ressource allocations in underlay cognitive radio". En 2015 International Conference on Electrical and Information Technologies (ICEIT). IEEE, 2015. http://dx.doi.org/10.1109/eitech.2015.7162981.
Texto completoKabaou, Mohamed Ouwais, Belgacem Chibani Rhaimi, Mohamed Naceur Abdelkrim y Mongi Marzoug. "Radio ressource allocation for multimedia traffic over wireless channels in OFDMA downlink systems". En 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5486849.
Texto completoGuha, Arpita y Viswanath Ganapathy. "Power allocation schemes for cognitive radios". En Middleware and Workshops (COMSWARE '08). IEEE, 2008. http://dx.doi.org/10.1109/comswa.2008.4554378.
Texto completoDigham, Fadel F. "Joint Power and Channel Allocation for Cognitive Radios". En 2008 IEEE Wireless Communications and Networking Conference. IEEE, 2008. http://dx.doi.org/10.1109/wcnc.2008.161.
Texto completoKim, Seung-Jun, Nasim Yahya Soltani y Georgios B. Giannakis. "Resource allocation for OFDMA cognitive radios under channel uncertainty". En ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946699.
Texto completoBazerque, Juan-Andres y Georgios B. Giannakis. "Distributed Scheduling and Resource Allocation for Cognitive OFDMA Radios". En 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM). IEEE, 2007. http://dx.doi.org/10.1109/crowncom.2007.4549822.
Texto completoLopez-Ramos, Luis M., Antonio G. Marques y Javier Ramos. "Joint sensing and resource allocation for underlay cognitive radios". En ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855014.
Texto completoNicolicin-Georgescu, Vlad, Vincent Benatier, Remi Lehn y Henri Briand. "Ontology-Based Autonomic Computing for Decision Support Systems Management: Shared Ressources Allocation between Groups of Data Warehouses". En 2010 Third International Conference on Communication Theory, Reliability, and Quality of Service. IEEE, 2010. http://dx.doi.org/10.1109/ctrq.2010.46.
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