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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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Almazrouei, Ebtesam, Gabriele Gianini, Nawaf Almoosa, and Ernesto Damiani. "Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks." Sensors 21, no. 7 (2021): 2414. http://dx.doi.org/10.3390/s21072414.

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This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing s

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Sun, Hao, and Qian Xu. "Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China." Remote Sensing 13, no. 14 (2021): 2848. http://dx.doi.org/10.3390/rs13142848.

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Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to de

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13

Granek, Justin, Eldad Haber, and Elliot Holtham. "Resource Management through Machine Learning." ASEG Extended Abstracts 2016, no. 1 (2016): 1–5. http://dx.doi.org/10.1071/aseg2016ab253.

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14

Xia, Nian, Hsiao-Hwa Chen, and Chu-Sing Yang. "Radio Resource Management in Machine-to-Machine Communications—A Survey." IEEE Communications Surveys & Tutorials 20, no. 1 (2018): 791–828. http://dx.doi.org/10.1109/comst.2017.2765344.

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15

Liu, Yuan, Zhi Zeng, Weijun Tang, and Fangjiong Chen. "Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learning." IEEE Communications Letters 24, no. 9 (2020): 1981–85. http://dx.doi.org/10.1109/lcomm.2020.2996605.

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16

Martinez, Jose F., and Engin Ipek. "Dynamic Multicore Resource Management: A Machine Learning Approach." IEEE Micro 29, no. 5 (2009): 8–17. http://dx.doi.org/10.1109/mm.2009.77.

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17

Stavroulaki, Vera, Aimilia Bantouna, Yiouli Kritikou, et al. "Knowledge Management Toolbox: Machine Learning for Cognitive Radio Networks." IEEE Vehicular Technology Magazine 7, no. 2 (2012): 91–99. http://dx.doi.org/10.1109/mvt.2012.2190196.

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18

Wasilewska, Małgorzata, and Hanna Bogucka. "Machine Learning for LTE Energy Detection Performance Improvement." Sensors 19, no. 19 (2019): 4348. http://dx.doi.org/10.3390/s19194348.

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The growing number of radio communication devices and limited spectrum resources are drivers for the development of new techniques of dynamic spectrum access and spectrum sharing. In order to make use of the spectrum opportunistically, the concept of cognitive radio was proposed, where intelligent decisions on transmission opportunities are based on spectrum sensing. In this paper, two Machine Learning (ML) algorithms, namely k-Nearest Neighbours and Random Forest, have been proposed to increase spectrum sensing performance. These algorithms have been applied to Energy Detection (ED) and Energ

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19

Zhang, Yong-jing, Zhi-yong Feng, and Ping Zhang. "A Q-learning Based Autonomic Joint Radio Resource Management Algorithm." Journal of Electronics & Information Technology 30, no. 3 (2011): 676–80. http://dx.doi.org/10.3724/sp.j.1146.2006.01357.

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20

Calabrese, Francesco Davide, Li Wang, Euhanna Ghadimi, Gunnar Peters, Lajos Hanzo, and Pablo Soldati. "Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges." IEEE Communications Magazine 56, no. 9 (2018): 138–45. http://dx.doi.org/10.1109/mcom.2018.1701031.

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21

Vučević, Nemanja, Jordi Pérez-Romero, Oriol Sallent, and Ramon Agustí. "Reinforcement learning for joint radio resource management in LTE-UMTS scenarios." Computer Networks 55, no. 7 (2011): 1487–97. http://dx.doi.org/10.1016/j.comnet.2010.12.029.

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22

Williams, Alan, Ann-Marie Mekhail, James Williams, Johanna McCord, and Vanessa Buchan. "Effective resource management using machine learning in medicine: an applied example." BMJ Simulation and Technology Enhanced Learning 5, no. 2 (2018): 85–90. http://dx.doi.org/10.1136/bmjstel-2017-000289.

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BackgroundThe field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools.MethodsCanterbury District Health Board has recently implemented a system for urgent lab sample processing in the community, reducing unnecessary emergency presentations to hospital. Samples

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23

Csaji, B. C., and L. Monostori. "Adaptive Stochastic Resource Control: A Machine Learning Approach." Journal of Artificial Intelligence Research 32 (June 25, 2008): 453–86. http://dx.doi.org/10.1613/jair.2548.

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The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks. This approach is a natural generalization of several standard resource management problems, such as scheduling and transportation problems. First, reactive solutions are considered and defined as control policies of suitably reformulated Markov decision processes (MDPs). We argue that this reformulation has several favorable properties, such as it has finite state and action spaces, it is aperiodic, hence all policies are proper and the space o

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24

Duque-Anón, Manuel, Patrik Koch, Dietmar Kunz, Bemhard Rüber, and Meinhard Ullrich. "Learning the compatibility matrix for adaptive resource management in cellular radio networks." European Transactions on Telecommunications 6, no. 6 (1995): 657–63. http://dx.doi.org/10.1002/ett.4460060608.

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25

Sande, Malcolm M., Mduduzi C. Hlophe, and Bodhaswar T. Maharaj. "Access and Radio Resource Management for IAB Networks Using Deep Reinforcement Learning." IEEE Access 9 (2021): 114218–34. http://dx.doi.org/10.1109/access.2021.3104322.

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26

Kumpikaitė, Vilmantė. "HUMAN RESOURCE DEVELOPMENT IN LEARNING ORGANIZATION." Journal of Business Economics and Management 9, no. 1 (2008): 25–31. http://dx.doi.org/10.3846/1611-1699.2008.9.25-31.

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This paper draws on prior exploration on human resource development in a learning organization, including theories about a learning organization, its features, human resource development and learning styles in organizations. The main aim of this paper is to explore human resource development and learning styles in organizations. The author introduces results of the survey covering 37 Lithuanian organizations selected from various industries ranging from a newspaper and transportation, insurance and radio station, to those in trade and manure production. The research shows that mostly explored

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27

Chen, Wen, Wu, et al. "Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication." Sensors 19, no. 16 (2019): 3610. http://dx.doi.org/10.3390/s19163610.

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In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning alg

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Cao, Jiawei. "Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm." Complexity 2021 (June 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/1909518.

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Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, constru

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Li, Ying Kui. "QoS-Aware Dynamic Virtual Resource Management in the Cloud." Applied Mechanics and Materials 556-562 (May 2014): 5809–12. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5809.

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Virtual resource management is a key issue in cloud computing paradigm. This paper focuses on the long term/short term virtual machine rental problem. A learning algorithm based on statistical learning techniques for resource requirement is proposed, and a dynamic virtual machine rental algorithm is given. These algorithms minimized the operational cost while preserving predetermined Quality-of-Service (QoS) specification. A simulation platform is constructed, and end users’ arrivals are generated according to a Markov Modulated Poisson Process (MMPP) to simulate the load in the real world. Th

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Patil, Dipak Raghunath. "Dynamic Resource Allocation and Memory Management Using Machine Learning for Cloud Environments." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 5921–27. http://dx.doi.org/10.30534/ijatcse/2020/255942020.

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Orlova, Ekaterina V. "Decision-Making Techniques for Credit Resource Management Using Machine Learning and Optimization." Information 11, no. 3 (2020): 144. http://dx.doi.org/10.3390/info11030144.

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Credit operations are fundamental in the banks’ activities and provide a significant share of their income. Under an increased demand for credit resources, credit risks are growth. It keeps the importance of the problem of an increase in the efficiency of lending management processes in financial institutions. The aim of the work is the justification and development of new technology and models for the management of bank lending that reduce credit risks and increases lending efficiency. The research materials are statistical data from the Bank of Russia and Rosstat. The methods of system analy

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Cui, Yaping, Xinyun Huang, Dapeng Wu, and Hao Zheng. "Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks." Wireless Communications and Mobile Computing 2020 (November 17, 2020): 1–10. http://dx.doi.org/10.1155/2020/8836315.

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The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce th

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Benmammar, Badr. "Recent Advances on Artificial Intelligence in Cognitive Radio Networks." International Journal of Wireless Networks and Broadband Technologies 9, no. 1 (2020): 27–42. http://dx.doi.org/10.4018/ijwnbt.2020010102.

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Cognitive radio is a form of wireless communication that makes decisions about allocating and managing radio resources after detecting its environment and analyzing the parameters of its radio frequency environment. Decision making in cognitive radio can be based on optimization techniques. In this context, machine learning and artificial intelligence are to be used in cognitive radio networks in order to reduce complexity, obtain resource allocation in a reasonable time and improve the user's quality of service. This article presents recent advances on artificial intelligence in cognitive rad

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Taleb Zadeh Kasgari, Ali, Walid Saad, and Merouane Debbah. "Human-in-the-Loop Wireless Communications: Machine Learning and Brain-Aware Resource Management." IEEE Transactions on Communications 67, no. 11 (2019): 7727–43. http://dx.doi.org/10.1109/tcomm.2019.2930275.

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Guerra-Gomez, Rolando, Silvia Ruiz-Boque, Mario Garcia-Lozano, and Joan Olmos Bonafe. "Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN." IEEE Access 8 (2020): 89130–42. http://dx.doi.org/10.1109/access.2020.2994258.

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Rawat, Sandeep Singh, and Rubeena Sultana. "Advance Resource Planning in Hospital Emergency Departments Using Machine Learning Techniques." International Journal of Human Capital and Information Technology Professionals 12, no. 3 (2021): 74–86. http://dx.doi.org/10.4018/ijhcitp.2021070105.

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Accidents are likely to happen at workplaces which requires employees to rush to the hospitals for emergency treatment. Due to increase in population, treating various medical cases has led to longer waiting times at emergency treatment units (ETUs). The reasons being the ambulance divergence, less staff, and reduced management. An approach to decrease overcrowding at ETU can be the application of modern techniques. Machine learning (ML) is the one which is used to find patients with high illness, therefore developing models that can avoid jams at ETU. In this paper, a new ML technique, light

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Khumalo, Nosipho N., Olutayo O. Oyerinde, and Luzango Mfupe. "Reinforcement Learning-Based Resource Management Model for Fog Radio Access Network Architectures in 5G." IEEE Access 9 (2021): 12706–16. http://dx.doi.org/10.1109/access.2021.3051695.

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Zhu, Hong. "Research on Human Resource Recommendation Algorithm Based on Machine Learning." Scientific Programming 2021 (August 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/8387277.

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The economic environment has changed dramatically around the world in recent years, generating favorable conditions for the growth of small- and medium-sized firms. The socioeconomic development and international integration of China are greatly influenced by the growth in both quality and quantity, the scale of operations, and the internal force of small- and medium-sized businesses. Moreover, in comparison with other developed countries around the world, Chinese small- and medium-sized enterprises continue to face many limitations in terms of size and contribution levels and have not yet ful

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Orlov, Aleksandr, Mikhail Rovnyagin, Anastasiia Aminova, Anna Guminskaia, Fedor Chernilin, and Alexander Hrapov. "Using the machine learning methods for resource management of high availability broadcasting containerized system." Procedia Computer Science 169 (2020): 773–79. http://dx.doi.org/10.1016/j.procs.2020.02.166.

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Pham, Nam Khanh, Akash Kumar, Amit Kumar Singh, and Mi Mi Aung Khin. "Leakage aware resource management approach with machine learning optimization framework for partially reconfigurable architectures." Microprocessors and Microsystems 47 (November 2016): 231–43. http://dx.doi.org/10.1016/j.micpro.2016.09.012.

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Kvamsdal, Sturla F., Ivan Belik, Arnt Ove Hopland, and Yuanhao Li. "A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature." Environmental and Resource Economics 79, no. 1 (2021): 93–115. http://dx.doi.org/10.1007/s10640-021-00554-0.

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Hassan, M. K., A. Babiker, M. Baker, and M. Hamad. "SLA Management For Virtual Machine Live Migration Using Machine Learning with Modified Kernel and Statistical Approach." Engineering, Technology & Applied Science Research 8, no. 1 (2018): 2459–63. http://dx.doi.org/10.48084/etasr.1692.

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Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physica

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Sun, Yaohua, Mugen Peng, and Shiwen Mao. "Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks." IEEE Internet of Things Journal 6, no. 2 (2019): 1960–71. http://dx.doi.org/10.1109/jiot.2018.2871020.

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Zhang, Haijun, Haisen Zhang, Keping Long, and George K. Karagiannidis. "Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation." IEEE Transactions on Network Science and Engineering 7, no. 4 (2020): 2406–15. http://dx.doi.org/10.1109/tnse.2020.3004333.

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C.S Kaushik, V., S. Kolangiammal, and B. E Manoj Kumar. "Multi Feature Based Classifier for Spectrum Sensing in Cognitive Radio." International Journal of Engineering & Technology 7, no. 3.12 (2018): 894. http://dx.doi.org/10.14419/ijet.v7i3.12.16557.

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Cognitive Radio (CR) is an important technology which can enable the implementation of Dynamic Spectrum Access, which is a paradigm shift from the static spectrum access model. It is an intelligent wireless communication system which can sense the environment and can take decisions to effectively use the available radio resource without creating any interference to the Licensed Primary Users. Hence sensing of the spectrum plays a very important role in the effective implementation of this technology. We propose a new spectrum sensing algorithm in this paper which is based on machine learning a

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Zhi, Congying, Wei Ji, Rui Yin, et al. "The flexible resource management in optical data center networks based on machine learning and SDON." Optical Switching and Networking 39 (November 2020): 100594. http://dx.doi.org/10.1016/j.osn.2020.100594.

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Mhetre, Nalini A., Arvind V. Deshpande, and Parikshit Narendra Mahalle. "Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 135–42. http://dx.doi.org/10.35940/ijeat.e2688.0610521.

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Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using

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Chen, Xianfu, Celimuge Wu, Tao Chen, et al. "Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective." IEEE Transactions on Wireless Communications 19, no. 4 (2020): 2268–81. http://dx.doi.org/10.1109/twc.2019.2963667.

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Ban, Tae-Won, and Woongsup Lee. "A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks." Electronics 8, no. 11 (2019): 1361. http://dx.doi.org/10.3390/electronics8111361.

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Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the

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Hwang, Ren-Hung, Min-Chun Peng, and Kai-Chung Cheng. "QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity." Applied Sciences 9, no. 15 (2019): 3018. http://dx.doi.org/10.3390/app9153018.

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Abstract:

Dual connectivity (DC) was first proposed in 3GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we

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