Letteratura scientifica selezionata sul tema "Radio Resources Allocation"
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Articoli di riviste sul tema "Radio Resources Allocation"
Ma, Lu, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp e Irfan Ghauri. "A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet". Wireless Communications and Mobile Computing 2018 (27 settembre 2018): 1–11. http://dx.doi.org/10.1155/2018/1745904.
Testo completo.., Ishwarlal, e Ankit Saxena. "Design, Simulation and Analysis of Multi-Dimensional Multiple Access (MDMA) Schemes Using MATLAB for Quality of Service (QoS) Enhancement". Journal of Intelligent Systems and Internet of Things 11, n. 2 (2024): 111–28. http://dx.doi.org/10.54216/jisiot.110210.
Testo completoR G, Umesh, Sushil Kumar G N, Santhosh K, Suraksha M S e 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, n. 3 (31 marzo 2023): 677–83. http://dx.doi.org/10.22214/ijraset.2023.49468.
Testo completoBenmammar, Badr. "Recent Advances on Artificial Intelligence in Cognitive Radio Networks". International Journal of Wireless Networks and Broadband Technologies 9, n. 1 (gennaio 2020): 27–42. http://dx.doi.org/10.4018/ijwnbt.2020010102.
Testo completoMathew, Alex. "SliceOptiAI: Smart Resource Allocation for Seamless Network Slicing". International Journal of Computer Science and Mobile Computing 13, n. 1 (30 gennaio 2024): 82–87. http://dx.doi.org/10.47760/ijcsmc.2024.v13i01.006.
Testo completoWulandari, Astri, Nachwan Mufti Adriansyah e Vinsensius Sigit Widhi Prabowo. "Greedy Based Radio Resource Allocation Algorithm with SARSA Power Control Scheme in D2D Underlaying Communication". Journal of Measurements, Electronics, Communications, and Systems 7, n. 1 (30 dicembre 2020): 6. http://dx.doi.org/10.25124/jmecs.v7i1.3472.
Testo completoYadav, Savita, Pradeep Kumar Shah, Sowmiya Kumar e 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 (12 luglio 2024): 2024ss0307. http://dx.doi.org/10.31893/multiscience.2024ss0307.
Testo completoMathonsi, Topside E., Tshimangadzo Mavin Tshilongamulenzhe e Bongisizwe Erasmus Buthelezi. "Enhanced Resource Allocation Algorithm for Heterogeneous Wireless Networks". Journal of Advanced Computational Intelligence and Intelligent Informatics 24, n. 6 (20 novembre 2020): 763–73. http://dx.doi.org/10.20965/jaciii.2020.p0763.
Testo completoRazmi, Shirin, e Naser Parhizgar. "Adaptive resources assignment in OFDM-based cognitive radio systems". International Journal of Electrical and Computer Engineering (IJECE) 9, n. 3 (1 giugno 2019): 1935. http://dx.doi.org/10.11591/ijece.v9i3.pp1935-1943.
Testo completoMach, Pavel, e Robert Bestak. "Radio resources allocation for decentrally controlled relay stations". Wireless Networks 17, n. 1 (30 luglio 2010): 133–48. http://dx.doi.org/10.1007/s11276-010-0269-8.
Testo completoTesi sul tema "Radio Resources Allocation"
Weng, Lingfan. "Analysis and allocation of radio resources in cooperative wireless networks /". View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20WENG.
Testo completoSharara, Mahdi. "Resource Allocation in Future Radio Access Networks". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG024.
Testo 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
Schimuneck, Matias Artur Klafke. "Adaptive Monte Carlo algorithm to global radio resources optimization in H-CRAN". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169922.
Testo completoUp until 2020 it is expected that cellular networks must raise the coverage area in 10-fold, support a 100-fold more user equipments, and increase the data rate capacity by a 1000-fold in comparison with current cellular networks. The dense deployment of small cells is considered a promising solution to reach such aggressive improvements, once it moves the antennas closer to the users, achieving higher data rates due to the signal quality at short distances. However, operating a massive number of antennas can significantly increase the energy consumption of the network infrastructure. Furthermore, the large insertion of new radios brings greater spectral interference between the cells. In this scenery, the optimal management of radio resources turn an exaction due to the impact on the quality of service provided to the users. For example, low transmission powers can leave users without connection, while high transmission powers can contribute to inter radios interference. Furthermore, the interference can be raised on the unplanned reuse of the radio resources, resulting in low data transmission per radio resource, as the under-reuse of radio resources limits the overall data transmission capacity. A solution to control the transmission power, assign the spectral radio resources, and ensure the service to the users is essential. In this thesis, we propose an Adaptive Monte Carlo algorithm to perform global energy efficient resource allocation for Heterogeneous Cloud Radio Access Network (HCRAN) architectures, which are forecast as future fifth-generation (5G) networks. We argue that our global proposal offers an efficient solution to the resource allocation for both high and low density scenarios. Our contributions are threefold: (i) the proposal of a global approach to the radio resource assignment problem in H-CRAN architecture, whose stochastic character ensures an overall solution space sampling; (ii) a critical comparison between our global solution and a local model; (iii) the demonstration that, for high density scenarios, Energy Efficiency is not a well suited metric for efficient allocation, considering data rate capacity, fairness, and served users. Moreover, we compare our proposal against three state-of-the-art resource allocation algorithms for 5G networks.
Amer, Asmaa. "Resource Allocation in NOMA-based cellular networks". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG089.
Testo completoThis thesis aims to optimize resources allocation within NOMA systems, particularly downlink cooperative NOMA systems, within single-antenna and multiple antenna base station (BS) configurations. This aims to maximize spectral and energy efficiency, and to propose more efficient NOMA schemes that can reap benefits of NOMA and address the limitations of currently considered NOMA schemes, in terms of power consumption and receiver complexity. In the first contribution, a network-slicing-based cooperative NOMA based system is investigated to accommodate both cellular users and device-to-device (D2D) pairs with enhanced mobile broadband (eMBB) and Ultra reliable low latency communication (URLLC) services requirements. The optimization problem is formulated as sum-throughput maximization with three optimization variables: NOMA-users clustering, underlying D2D- admission, and resource blocks (RBs) allocation. The problem is decoupled into three sub-problems. A sequential algorithmic solution is proposed, starting by users clustering, followed by RBs allocation, and finally D2D admission. The users clustering and D2D admission sub-problems are solved using low-complexity many-to-one matching theory solution. The RBs allocation problem is solved using heuristics approach. In the second contribution, we revisit the trade-off between user access and the successive interference cancellation (SIC) complexity of NOMA receivers. As more users share the same resources, interference and SIC complexity escalate. Unlike conventional pairing-based NOMA schemes, we propose an overlapping cooperative NOMA scheme where each cell-edge user can share resources with multiple cell-center users, even if cell-center users are using orthogonal resources between each other. This approach enhances user connectivity, improves cell-edge user performance, and maintains low SIC complexity. The problem is formulated as maximization of Quality-of-Service (QoS) satisfaction of cell-edge users, and is solved using a many-to-many matching theory algorithm with swapping and add/remove strategies. In the third thesis contribution, we propose a hybrid Space Division Multiple Access (SDMA)/NOMA system, to adapt the multiple access mode, either NOMA or SDMA, based on the power consumption. In the power consumption model, unlike NOMA literature, where power induced by SIC units at the receiver is overlooked, we introduce dynamic power consumption model based on the SIC power. The problem is formulated as maximizing energy efficiency by optimizing the multiple access mode selection, BS beamforming, and user power allocation. This approach prevents overestimation of energy efficiency, consequently, avoids gaps between its theoretical evaluation and practical system design, an aspect particularly critical for energy-constrained NOMA devices. The problem is solved using successive convex approximation (SCA), difference of convex (DC) programming and semidefinite programming (SDP) approaches
Masmoudi, Raouia. "Télécommunications domotiques efficaces en termes de consommation d’énergie". Thesis, Cergy-Pontoise, 2015. http://www.theses.fr/2015CERG0791.
Testo completoThe radio spectrum is a limited resource which must be used in an optimal way. Recent works in the literature aim to improve the use of radio frequencies by exploiting intelligent techniques from signal processing, such as the cognitive radio paradigm. In this thesis, we study a joint spectrum scheduling and power allocation problem in a Cognitive Radio (CR) system composed of several secondary users (SUs) and primary users (PUs). The objective is to optimize the energy efficiency of the SUs while guaranteeing that the interference created to the PUs is kept below a maximum tolerated level. We analyze energy efficiency metrics in wireless communications using a common unifying framework based on convex multi-criteria optimization tools, which includes the three of the most popular energyefficiency metrics in the literature : weighted difference between overall achievable rate and power consumption, the ratio between the overall rate and consumed power and overall consumed power under minimum rate constraint. Then, we further focus on the study of the opportunistic power minimization problem over several orthogonal frequency bands under constraints on the minimum Quality of Service (QoS) and maximum interference to the PUs. Given the opposing nature of these constraints, we first study the feasibility of the problem and we provide sufficient conditions and necessary conditions that guarantee the existence of a solution. The main challenge lies in the non-convexity of the joint spectrum and power allocation problem due to the discrete spectrum scheduling parameter of SUs. To overcome this issue, we use a Lagrangian relaxation technique to solve a convexproblem. We prove that the discrete solutions of the relaxed problem are the solutions of the initial problem. When a solution exists, we propose an iterative algorithm based on subgradient method to compute an optimal solution. We show that the optimal scheduling is more efficient compared to other conventional spectrum allocations (e.g. interlaced, blockwise). In the particular case of two orthogonal bands and an unique SU, we provide an analytical solution that does not require an iterative algorithm
Suliman, I. M. (Isameldin Mohammed). "Performance analysis of cognitive radio networks and radio resource allocation". Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526212753.
Testo completoTiivistelmä Kognitiivinen radio (CR) on nousemassa lupaavaksi työkaluksi niukkojen radioresurssien ja spektrin käytön tehottomuuden ratkaisemisessa. Spektrin nuuskiminen (signaalin ilmaisu) mahdollistaa spektriaukkojen reaaliaikaisen tunnistamisen toissijaisten käyttäjien (SU) toimesta kognitiivisissa radioverkoissa (CRN). Tässä väitöskirjassa painotus on CRN verkkojen suorituskykyanalyysissa ja radioresurssien hallinnassa. Työssä kehitetään jatkuva-aikaiseen Markov ketjuun (CTMC) perustuva analyyttinen malli joka ottaa huomioon kaikki olennaiset asiat mukaan lukien jatkuva-aikaiseen spektrin nuuskimiseen liittyvän väärien hälytysten tiheyden (FAR). Joissakin tapauksissa PU:ta voidaan mallintaa aikajaoteltuna siten että PU:n tila on vakio kussakin aikavälissä. Olettaen että SU voi synkronoitua aikaväleihin, on intuitiivista käyttää aikavälin alkua nuuskimiselle ja loppuosaa (mahdollisesti) viestintää varten. M/D/1:n ensisijaisuus-jonotus-suunnitelmaa soveltamalla tässä väitöskirjassa saadaan tuloksia odotusajalle ja jonon pituudelle sekä SU:lle että PU:lle. Seuraavaksi käsitellään monikäyttöä SU:den joukossa aikajaotellussa kanavassa. Tavanomainen menetelmä käyttää esimerkiksi kanavapääsytodennäköisyyttä ψ kussakin aikavälissä vastaten aikajaoteltua ALOHA protokollaa. Tässä väitöskirjassa esitetään radikaali uusi idea: miksei lisätä väärän hälytyksen todennäköisyyttä kussakin SU:ssa ja käytetä sitä moniliittymämenetelmänä? Työssä esitetään peliteoreettinen lähestymistapa radioresurssien allokointiin siten että resurssit jaetaan oikeudenmukaisesti monen yhteysvälin linkeissä. Lisäksi tutkitaan myös resursoinnin ongelmaa heterogeenisissa langattomissa verkoissa. Lopuksi tutkitaan laitteiden välistä suoraa viestintää (D2D) paikallisen jakauman kanssa, jossa käyttäjillä on tapana kasaantua solun sisällä esim. rakennuksiin. Esitetään teoreettinen analyysi kaksiulotteisella klusteroinnilla myös korreloitujen ryhmien kanssa. Osoitetaan että korrelaatio ryhmän valinnassa parantavaa merkittävästi suorituskykyä
Ellingsæter, Brage Høyland. "Cognitive Radio: Interference Management and Resource Allocation". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11295.
Testo completoHashmi, Ziaul Hasan. "Dynamic resource allocation for cognitive radio systems". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/961.
Testo completoWong, Chung Kit. "Resource allocation for multihop packet radio networks". Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68059.
Testo completoNgo, Duy. "Radio resource allocation for wireless heterogeneous networks". Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119622.
Testo completoEn déployant de petites cellules (dénommées les femtocells) au sein de la même zone de service que les cellules de tailles régulières (dénommées cellules macro), une efficacité spectrale zone beaucoup plus élevé, une meilleure couverture à l'intérieur, et d'importantes données mobiles de déchargement entre les deux cellules peuvent être réalisé tout en gardant faible coût. Vu que les femtocells réutilisent le spectre de fréquence déjà consacré à la cellule macro, auxquelles ils sont assignés, d'une manière non-coordonnée, de nouvelles limites de cellules sont créées et l'interférence devient beaucoup plus imprévisible que dans les réseaux traditionnels. Dans ce contexte réseau hétérogène, une allocation adaptative de puissance et des méthodes d'accès dynamiques au spectre sont nécessaires pour assurer une coexistence harmonisée des entités du réseau avec les nouvelles spécifications imposées par les femtocells. Depuis que les femtocells sont déployées par les terminaux sans aucune planification au préalable du réseau, des solutions qui s'adaptent automatiquement sont toujours désirable pour contrôler efficacement les sévères interférences entre les différents niveaux du réseau sans fil hétérogène.Dans cette étude, nous développons et évaluons des algorithmes distribués pour l'allocation de ressources radio dans les réseaux sans fil hétérogènes employant l accès multiple par répartition en code (CDMA) et Accès multiple par répartition en fréquence (OFDMA). En évitant une coordination centralisée, les solutions proposées protègent le fonctionnement de tous les utilisateurs de la cellule macro existantes, tout en exploitant de manière optimale la capacité résiduelle du réseau pour les utilisateurs du femtocells. Dans les réseaux CDMA, nous proposons un schéma de tarification dynamique associé à un contrôle d'admission des utilisateurs de la femtocell nous permettant de gérer indirectement l'interférence inter-niveaux (entre cellule macro et femtocell). Le contrôle simultané de la puissance et les algorithmes de contrôle d'admission proposés peut être exécuté localement sur chaque lien pour offrir un maximum d'utilité pour les utilisateurs individuels. Pour maximiser l'utilité totale du réseau, nous développons un algorithme de contrôle simultané de puissance basant sur l'optimalité de Pareto et le rapport signal sur interférence plus bruit (SINR) qui peut partager équitablement les ressources radio entre les utilisateurs. En appliquant d'une méthode d'optimisation, les SINR minimaux prescrits par les utilisateurs des cellules macro sont garantis, alors que le maximal global la somme de l'utilité du réseau est trouvé.Dans les réseaux OFDMA, afin de résoudre le problème non convexe et combinatoire de l'allocation conjointe de la puissance et des sous-porteuses, nous proposons un schéma alternatif de gestion dynamique du spectre qui optimise la distribution de puissance et des sous-porteuses. Avec l'approche par approximations successives convexe adoptée, le débit total de tous les femtocells est maximisé alors que la capacité du réseau de la cellule macro est toujours protégée. En femtocells cognitives où les utilisateurs du femtocell accèdent au spectre autorisé à la cellule macro d'une manière opportuniste, nous appliquons la dualité lagrangienne pour optimiser la distribution de la puissance et des sous-porteuses. Nous prouvons que les solutions distribuées proposées atteignent leur optimal global avec une faible complexité.
Libri sul tema "Radio Resources Allocation"
Jean-Marc, Chaduc, e Pogorel Gerard, a cura di. The radio spectrum: Managing a strategic resource. Hoboken, NJ, USA: Wiley, 2008.
Cerca il testo completoBenmammar, Badr, e Asma Amraoui. Radio Resource Allocation and Dynamic Spectrum Access. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.
Testo completoKarl, Nebbia, e United States. National Telecommunications and Information Administration, a cura di. Spectrum resource assessment of unlicensed electronic devices. [Annapolis, MD]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.
Cerca il testo completoYŏnʼguwŏn, Hanʼguk Chŏnja Tʻongsin, a cura di. Chŏnpʻa chawŏn iyong kiban kisul kaebal =: Development of basic spectrum resource utilizing technology. [Seoul]: Chŏngbo Tʻongsinbu, 2007.
Cerca il testo completoYŏnʼguwŏn, Hanʼguk Chŏnja Tʻongsin, a cura di. Chŏnpʻa chawŏn iyong kiban kisul kaebal =: Development of basic spectrum resource utilizing technology. [Seoul]: Chŏngbo Tʻongsinbu, 2007.
Cerca il testo completoLe-Ngoc, Tho, e Khoa Tran Phan. Radio Resource Allocation Over Fading Channels Under Statistical Delay Constraints. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57693-0.
Testo completoFernando, Xavier, Ajmery Sultana, Sattar Hussain e Lian Zhao. Cooperative Spectrum Sensing and Resource Allocation Strategies in Cognitive Radio Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-73957-1.
Testo completo1971-, Chen Huan, a cura di. Radio resource management for multimedia QoS support in wireless networks. Boston: Kluwer Academic Publishers, 2004.
Cerca il testo completoCarvalho, Nuno Borges, Alessandro Cidronali e Roberto Gómez-García. White space communication technologies. Cambridge, United Kingdom: Cambridge University Press, 2015.
Cerca il testo completoUnderwood, Caroline D. Spectrum issues for the new communications age. A cura di Griswold Mary E e Moore, L. K. S. (Linda K. S.). Hauppauge, N.Y: Nova Science Publishers, 2010.
Cerca il testo completoCapitoli di libri sul tema "Radio Resources Allocation"
Vivier, Emmanuelle, Michel Terré e Bernard Fino. "On the Complexity of Radio Resources Allocation in WCDMA Systems". In IFIP International Federation for Information Processing, 179–90. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-23150-1_16.
Testo completoHouda, Mzoughi, Faouzi Zarai, Mohammad S. Obaidat, Balqies Sadoun e Lotfi Kamoun. "Cooperative Radio Resources Allocation and Congestion Prevention Scheme for LTE-A". In Advances in Intelligent Systems and Computing, 297–314. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69832-8_17.
Testo completoMikaeil, Ahmed Mohammed, e Weisheng Hu. "Q-Learning Based Joint Allocation of Fronthaul and Radio Resources in Multiwavelength-Enabled C-RAN". In Optical Network Design and Modeling, 623–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38085-4_53.
Testo completoWang, Shaowei. "Dynamic Resource Allocation". In Cognitive Radio Networks, 9–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08936-2_2.
Testo completoGhorbanzadeh, Michael, e Ahmed Abdelhadi. "Radio Resource Block Allocation". In Practical Channel-Aware Resource Allocation, 71–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73632-3_4.
Testo completoGhorbanzadeh, Mo, Ahmed Abdelhadi e Charles Clancy. "Radio Resource Block Allocation". In Cellular Communications Systems in Congested Environments, 117–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46267-7_6.
Testo completoBenmammar, Badr, e Asma Amraoui. "Cognitive Radio". In Radio Resource Allocation and Dynamic Spectrum Access, 23–38. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.ch2.
Testo completoGhorbanzadeh, Michael, e Ahmed Abdelhadi. "Utility Functions and Radio Resource Allocation". In Practical Channel-Aware Resource Allocation, 17–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73632-3_2.
Testo completoKazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran e Choong Seon Hong. "Network Slicing: Radio Resource Allocation". In Network Slicing for 5G and Beyond Networks, 43–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_4.
Testo completoZhang, Huaqing, e Zhu Han. "Distributed Resource Allocation for Network Virtualization". In Handbook of Cognitive Radio, 1–18. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_39-1.
Testo completoAtti di convegni sul tema "Radio Resources Allocation"
Upadhyay, Deepak, Mridul Gupta, Kunj Bihari Sharma, Abhay Upadhyay e Satya Prakash Yadav. "Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment". In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10738873.
Testo completoVijayasarveswari, V., S. Khatun, M. M. Fakir, M. N. Nayeem, L. M. Kamarudin e A. Jakaria. "Cognitive radio based optimal channel sensing and resources allocation". In 11TH ASIAN CONFERENCE ON CHEMICAL SENSORS: (ACCS2015). Author(s), 2017. http://dx.doi.org/10.1063/1.4975292.
Testo completode Souza, Phelipe A., Abdallah S. Abdallah, Elivelton F. Bueno e Kleber V. Cardoso. "Virtualized Radio Access Networks: Centralization, Allocation, and Positioning of Resources". In 2018 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2018. http://dx.doi.org/10.1109/iccw.2018.8403667.
Testo completoVijayasarveswari, V., S. Khatun e M. N. Morshed. "Performance of spectrum sensing in cognitive radio for resources allocation". In PROCEEDINGS OF GREEN DESIGN AND MANUFACTURE 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0044715.
Testo completoWang, Chiapin, Yu-Chia Liu, Han-Chi Gao e Tsung-Yi Fan Chiang. "Reinforcement-Learning Based Radio Resources Allocation in Licensed Assisted Access". In 2020 IEEE International Conference on Smart Cloud (SmartCloud). IEEE, 2020. http://dx.doi.org/10.1109/smartcloud49737.2020.00040.
Testo completoJun, Wang, e Zhu Jinzhou. "Q-learning Based Radio Resources Allocation in Cognitive Satellite Communication". In 2022 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2022. http://dx.doi.org/10.1109/isncc55209.2022.9851730.
Testo completoMalde, Keval A., Venkatarami Reddy Chintapalli, Bhavishya Sharma, Bheemarjuna Reddy Tamma e A. Antony Franklin. "JARS: A Joint Allocation of Radio and System Resources for Virtualized Radio Access Networks". In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2023. http://dx.doi.org/10.1109/noms56928.2023.10154407.
Testo completoBoskov, Ivan, e Ales Svigelj. "Dynamic allocation of resources in a heterogeneous Cloud Radio Access Network". In 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom). IEEE, 2022. http://dx.doi.org/10.1109/cobcom55489.2022.9880676.
Testo completoMzoughi, Houda, Faouzi Zarai e Lotfi Kamoun. "Interference-limited radio resources allocation in LTE_A system with MIH cooperation". In 2016 22nd Asia-Pacific Conference on Communications (APCC). IEEE, 2016. http://dx.doi.org/10.1109/apcc.2016.7581465.
Testo completoDandachi, Ghina, Tijani Chahed, Salah Eddine Elayoubi, Nada Chendeb Taher e Ziad Fawal. "Joint allocation strategies for radio and processing resources in Virtual Radio Access Networks (V-RAN)". In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2017. http://dx.doi.org/10.1109/pimrc.2017.8292512.
Testo completoRapporti di organizzazioni sul tema "Radio Resources Allocation"
Bruce. L52090 Near-Neutral pH SCC - Dormancy and Re-Initiation of Stress Corrosion Cracks. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), aprile 2003. http://dx.doi.org/10.55274/r0011360.
Testo completoAlbert, Jose Ramon, Ronald Mendoza, Deanne Lorraine Cabalfin, Mohammad Mahmoud e Mika Muñoz. A Process Evaluation of the Philippine Alternative Learning System. Philippine Institute for Development Studies, dicembre 2024. https://doi.org/10.62986/dp2024.31.
Testo completoAlmaden, Catherine Roween. Economics of Satellite Campuses. Philippine Institute for Development Studies, dicembre 2024. https://doi.org/10.62986/dp2024.33.
Testo completoFinancial Stability Report - First Half of 2023. Banco de la República, settembre 2024. http://dx.doi.org/10.32468/rept-estab-fin.sem1.eng-2023.
Testo completoFinancial Stability Report - First Half of 2022. Banco de la República, settembre 2023. http://dx.doi.org/10.32468/rept-estab-fin.sem1.eng-2022.
Testo completoFinancial Stability Report - Second Half of 2022. Banco de la República, settembre 2023. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2022.
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