Relevant bibliographies by topics / Machine learning. radio resource management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Machine learning. radio resource management'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Machine learning. radio resource management"

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Ullah, Ruzat, Safdar Nawaz Khan Marwat, Arbab Masood Ahmad, et al. "A Machine Learning Approach for 5G SINR Prediction." Electronics 9, no. 10 (2020): 1660. http://dx.doi.org/10.3390/electronics9101660.

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Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ra
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Khan, Sahrish, Hasan Ali Khattak, Ahmad Almogren, et al. "5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning." IEEE Access 8 (2020): 6792–800. http://dx.doi.org/10.1109/access.2020.2964697.

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Wang, Jiao, Jay Weitzen, Oguz Bayat, Volkan Sevindik, and Mingzhe Li. "Performance Model for Video Service in 5G Networks." Future Internet 12, no. 6 (2020): 99. http://dx.doi.org/10.3390/fi12060099.

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Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches,
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Trejo Narváez, Omar Albeiro, and Víctor Fabián Miramá Pérez. "Machine learning algorithms for inter-cell interference coordination." Sistemas y Telemática 16, no. 46 (2018): 37–57. http://dx.doi.org/10.18046/syt.v16i46.3034.

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The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the netwo
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He, Jiayuan, Jeonghun Lee, Sithamparanathan Kandeepan, and Ke Wang. "Machine Learning Techniques in Radio-over-Fiber Systems and Networks." Photonics 7, no. 4 (2020): 105. http://dx.doi.org/10.3390/photonics7040105.

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The radio-over-fiber (RoF) technology has been widely studied during the past decades to extend the wireless communication coverage by leveraging the low-loss and broad bandwidth advantages of the optical fiber. With the increasing need for wireless communications, using millimeter-waves (mm-wave) in wireless communications has become the recent trend and many attempts have been made to build high-throughput and robust mm-wave RoF systems during the past a few years. Whilst the RoF technology provides many benefits, it suffers from several fundamental limitations due to the analog optical link
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Khammari, Hedi, Irfan Ahmed, Ghulam Bhatti, and Masoud Alajmi. "Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems." Electronics 8, no. 10 (2019): 1061. http://dx.doi.org/10.3390/electronics8101061.

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In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substa
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Lopez-de-Ipina, Karmele, Nora Barroso, Pilar M. Calvo, et al. "Multilingual audio information management system based on semantic knowledge in complex environments." Neural Computing and Applications 32, no. 24 (2020): 17869–86. http://dx.doi.org/10.1007/s00521-019-04618-7.

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AbstractThis paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilin
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Taras, Maksymyuk, Shubyn Bohdan, Andrushchak Volodymyr, Beshley Halyna, Dumych Stepan, and Klymash Mykhailo. "PRACTICAL IMPLEMENTATION OF THE SOFTWARE DEFINED 5G MOBILE NETWORK BASED ON CLOUD-RAN AND SDR TECHNOLOGIES." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 23–34. http://dx.doi.org/10.36994/2707-4110-2020-1-28-02.

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The paper is devoted to the practical implementation aspects of a software-defined mobile network based on the Cloud-RAN architecture and universal software radio peripheral National Instruments USRP 2900. We propose a multilayer network architecture, which consists of a radio access network (RAN) plane, a core plane, a control plane, an artificial intelligence (AI) plane, and a monitoring system that collects data about network performance. The RAN plane provides all functions related to channel scheduling, data encoding and signal processing and combines all macro and small cells, as well as
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Adeel, Ahsan, Hadi Larijani, Abbas Javed, and Ali Ahmadinia. "Impact of Learning Algorithms on Random Neural Network based Optimization for LTE-UL Systems." Network Protocols and Algorithms 7, no. 3 (2015): 157. http://dx.doi.org/10.5296/npa.v7i3.8295.

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This paper presents an application of context-aware decision making to the problem of radio resource management (RRM) and inter-cell interference coordination (ICIC) in long-term evolution-uplink (LTE-UL) system. The limitations of existing analytical, artificial intelligence (AI), and machine learning (ML) based approaches are highlighted and a novel integration of random neural network (RNN) based learning with genetic algorithm (GA) based reasoning is presented. In first part of the implementation, three learning algorithms (gradient descent (GD), adaptive inertia weight particle swarm opti
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Medhat Salih, Qusay, Md Arafatur Rahman, Fadi Al-Turjman, and Zafril Rizal M. Azmi. "Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey." IEEE Access 8 (2020): 67835–67. http://dx.doi.org/10.1109/access.2020.2986369.

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Dissertations / Theses on the topic "Machine learning. radio resource management"

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Svensson, Frida. "Scalable Distributed Reinforcement Learning for Radio Resource Management." Thesis, Linköpings universitet, Tillämpad matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177822.

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There is a large potential for automation and optimization in radio access networks (RANs) using a data-driven approach to efficiently handle the increase in complexity due to the steep growth in traffic and new technologies introduced with the development of 5G. Reinforcement learning (RL) has natural applications in RAN control loops such as link adaptation, interference management and power control at different timescales commonly occurring in the RAN context. Elevating the status of data-driven solutions in RAN and building a new, scalable, distributed and data-friendly RAN architecture wi
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Elsayed, Medhat. "Machine Learning-Enabled Radio Resource Management for Next-Generation Wireless Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42476.

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A new era of wireless networks is evolving, thanks to the significant advances in communications and networking technologies. In parallel, wireless services are witnessing a tremendous change due to increasingly heterogeneous and stringent demands, whose quality of service requirements are expanding in several dimensions, putting pressure on mobile networks. Examples of those services are augmented and virtual reality, as well as self-driving cars. Furthermore, many physical systems are witnessing a dramatic shift into autonomy by enabling the devices of those systems to communicate and transf
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Morales-Tirado, Lizdabel. "An Approach to Using Cognition in Wireless Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37185.

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Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for ap
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Stancanelli, Elvis Miguel Galeas. "AplicaÃÃes de aprendizagem de mÃquinas Ãs comunicaÃÃes mÃveis: gerenciamento de recursos e avaliaÃÃo de desempenho." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8386.

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nÃo hÃ<br>De modo a suprirem o aumento de trÃfego previsto para os prÃximos anos, os sistemas de comunicaÃÃes mÃveis da prÃxima geraÃÃo contam com tecnologias avanÃadas, como mÃltiplas subportadoras ortogonais e coordenaÃÃo entre pontos de transmissÃo. Os recursos de rÃdio passam a ser organizados em um nÃmero maior de dimensÃes, tornando mais complexas tarefas como a alocaÃÃo de recursos e a avaliaÃÃo de desempenho do enlace. Com base em tÃcnicas de aprendizagem de mÃquinas, foram investigadas novas maneiras de abordar essas tarefas, de modo a realizÃ-las eficientemente. Esta tese traz duas p
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Ben, Hassine Nesrine. "Machine Learning for Network Resource Management." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV061.

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Une exploitation intelligente des données qui circulent sur les réseaux pourrait entraîner une amélioration de la qualité d'expérience (QoE) des utilisateurs. Les techniques d'apprentissage automatique offrent des fonctionnalités multiples, ce qui permet d’optimiser l'utilisation des ressources réseau.Dans cette thèse, deux contextes d’application sont étudiés : les réseaux de capteurs sans fil (WSNs) et les réseaux de contenus (CDNs). Dans les WSNs, il s’agit de prédire la qualité des liens sans fil afin d’améliorer la qualité des routes et donc d’augmenter le taux de remise des paquets ce qu
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Tania, Zannatun Nayem. "Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the who
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Rihani, Mohamad-Al-Fadl. "Management of Dynamic Reconfiguration in a Wireless Digital Communication Context." Thesis, Rennes, INSA, 2018. http://www.theses.fr/2018ISAR0030/document.

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Aujourd'hui, les appareils sans fil disposent généralement de plusieurs technologies d'accès radio (LTE, WiFi,WiMax, ...) pour gérer une grande variété de normes ou de technologies. Ces appareils doivent être suffisamment intelligents et autonomes pour atteindre un niveau de performance donné ou sélectionne automatiquement la meilleure technologie sans fil disponible en fonction de la disponibilité des normes. Du point de vue matériel, les périphériques System on Chip (SoC) intègrent des processeurs et des structures logiques FPGA sur la même puce avec une interconnexion rapide. Cela permet de
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Capela, Nelson Filipe. "Intelligent and transparent resource management." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22720.

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Doutoramento em Engenharia Eletrotécnica<br>Wireless access networks have become available almost everywhere. In the same area we can have access to several networks of the same or di erent technologies that can present di erent characteristics. Alongside the evolution of access networks, we have the evolution of terminals. These are currently equipped with a multitude of wireless interfaces, easier to carry and more accessible to users. In uenced by these features, users began to change the way they use their devices to obtain information. The introduction of these new terminals in env
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Galanis, Ioannis. "RESOURCE MANAGEMENT IN EDGE COMPUTING FOR INTERNET OF THINGS APPLICATIONS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1871.

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The Internet of Things (IoT) computing paradigm has connected smart objects “things” and has brought new services at the proximity of the user. Edge Computing, a natural evolution of the traditional IoT, has been proposed to deal with the ever-increasing (i) number of IoT devices and (ii) the amount of data traffic that is produced by the IoT endpoints. EC promises to significantly reduce the unwanted latency that is imposed by the multi-hop communication delays and suggests that instead of uploading all the data to the remote cloud for further processing, it is beneficial to perform computati
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Mendil, Mouhcine. "Joint radio and power resource optimal management for wireless cellular networks interconnected through smart grids." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT087/document.

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Face à l'explosion du trafic mobile entraînée par le succès des smartphones, les opérateurs de réseaux mobiles (MNOs) densifient leurs réseaux à travers le déploiement massif des stations de base à faible portée (SBS), capable d’offrir des services très haut débit et de remplir les exigences de capacité et de couverture. Cette nouvelle infrastructure, appelée réseau cellulaire hétérogène (HetNet), utilise un mix de stations de base hiérarchisées, comprenant des macro-cellule à forte puissance et des SBS à faible puissance.La prolifération des HetNets soulève une nouvelle préoccupation concerna
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Books on the topic "Machine learning. radio resource management"

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Humphries, Grant, Dawn R. Magness, and Falk Huettmann, eds. Machine Learning for Ecology and Sustainable Natural Resource Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96978-7.

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Huettmann, Falk, Grant Humphries, and Dawn R. Magness. Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, 2018.

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Frey, Ulrich. Sustainable Governance of Natural Resources. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197502211.001.0001.

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Natural resources are often overexploited. Nevertheless, there are counterexamples of sustainably using common-pool resources. This book analyses the most important factors influencing the management of natural resources. Hence, the important question—What makes some systems successful?—is answered in this book. Based on three of the world’s largest data sets on fisheries, forest management, and irrigation systems, success factors are empirically examined. The book presents a synthesis of twenty-four success factors that explain ecological success, such as participation possibilities. The anal
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Book chapters on the topic "Machine learning. radio resource management"

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Punithavalli, M. "Machine Learning in Human Resource Management." In Artificial Intelligence Theory, Models, and Applications. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003175865-15.

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Collados, Kevin, Juan-Luis Gorricho, Joan Serrat, and Hu Zheng. "Multilayered Reinforcement Learning Approach for Radio Resource Management." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01766-2_135.

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Maharaj, Bodhaswar TJ, and Babatunde Seun Awoyemi. "Deep Learning Opportunities for Resource Management in Cognitive Radio Networks." In Developments in Cognitive Radio Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64653-0_10.

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Farha, Ramy, and Alberto Leon-Garcia. "Market-Based Hierarchical Resource Management Using Machine Learning." In Managing Virtualization of Networks and Services. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75694-1_3.

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Jin, Yue, Dimitre Kostadinov, Makram Bouzid, and Armen Aghasaryan. "Common Structures in Resource Management as Driver for Reinforcement Learning: A Survey and Research Tracks." In Machine Learning for Networking. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19945-6_8.

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Mahapatra, Bandana. "AI Techniques for Resource Management During COVID-19." In Artificial Intelligence and Machine Learning for COVID-19. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60188-1_11.

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Chochliouros, Ioannis P., Alexandros Kostopoulos, Miquel Payaró, et al. "Machine Learning-Based, Networking and Computing Infrastructure Resource Management." In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79157-5_8.

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Humphries, Grant R. W., and Falk Huettmann. "Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective." In Machine Learning for Ecology and Sustainable Natural Resource Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96978-7_1.

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Baltensperger, Andrew P. "Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska." In Machine Learning for Ecology and Sustainable Natural Resource Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96978-7_10.

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Huettmann, Falk. "Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences." In Machine Learning for Ecology and Sustainable Natural Resource Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96978-7_11.

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Conference papers on the topic "Machine learning. radio resource management"

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Farha, Ramy, Nadeem Abji, Omar Sheikh, and Alberto Leon-Garcia. "Market-Based Resource Management for Cognitive Radios Using Machine Learning." In IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. IEEE, 2007. http://dx.doi.org/10.1109/glocom.2007.879.

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Shaghaghi, Mahdi, and Raviraj S. Adve. "Machine learning based cognitive radar resource management." In 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8378775.

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Xun, Lei, Long Tran-Thanh, Bashir M. Al-Hashimi, and Geoff V. Merrett. "Optimising Resource Management for Embedded Machine Learning." In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020. http://dx.doi.org/10.23919/date48585.2020.9116235.

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Huang, Botong, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, and Frederick R. Reiss. "Resource Elasticity for Large-Scale Machine Learning." In SIGMOD/PODS'15: International Conference on Management of Data. ACM, 2015. http://dx.doi.org/10.1145/2723372.2749432.

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Fabbricatore, Christian, Harold Boley, and Achim P. Karduck. "Machine learning for resource management in smart environments." In 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST) - Complex Environment Engineering. IEEE, 2012. http://dx.doi.org/10.1109/dest.2012.6227910.

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Chiang, Min-Chi, and Jerry Chou. "DynamoML: Dynamic Resource Management Operators for Machine Learning Workloads." In 11th International Conference on Cloud Computing and Services Science. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010483401220132.

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Chen, Duan-Yu, and Jeng-Tsung Tsai. "Resource-limited intelligent photo management on mobile platforms." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016796.

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Almazrouei, Ebtesam, Gabriele Gianini, Nawaf Almoosa, and Ernesto Damiani. "What can Machine Learning do for Radio Spectrum Management?" In MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 2020. http://dx.doi.org/10.1145/3416013.3426443.

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Lu, Yun, Ming Zhao, Guangqiang Zhao, Lixi Wang, and Naphtali Rishe. "Massive GIS Database System with Autonomic Resource Management." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.161.

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Chen, Lydia Y. "Opportunities and Challenges for Resource Management and Machine Learning Clusters." In the 12th IEEE/ACM International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3368235.3369376.

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