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Artykuły w czasopismach na temat „User scheduling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 1 lutego 2022

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1

Hang, Juan, and Feng She. "User Scheduling in Multi-User MIMO Systems with Buffer Counting." Advanced Materials Research 461 (February 2012): 62–66. http://dx.doi.org/10.4028/www.scientific.net/amr.461.62.

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This paper studies the user scheduling in MIMO systems with queue control. We assume the data packets are buffered at the transmitter. By modeling the channel as a queuing system, the property of queue control is analyzed. Then, user scheduling algorithm with consideration of queue state and channel state together is also investigated. Simulation results show that queue control can achieve fairness and keep stability among users

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2

Snyder, R. L. "DESIGNING USER-FRIENDLY IRRIGATION SCHEDULING SOFTWARE." Acta Horticulturae, no. 278 (June 1990): 673–80. http://dx.doi.org/10.17660/actahortic.1990.278.65.

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3

Pecora, Federico, Riccardo Rasconi, Gabriella Cortellessa, and Amedeo Cesta. "User-oriented problem abstractions in scheduling." Innovations in Systems and Software Engineering 2, no. 1 (2006): 1–16. http://dx.doi.org/10.1007/s11334-006-0022-8.

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4

SOHN, Illsoo, Joonil CHOI, Byong OK LEE, and Kwang-Bok LEE. "Unitary Beamforming Multi-User MIMO System with Efficient User Scheduling Algorithm." IEICE Transactions on Communications E93-B, no. 7 (2010): 1965–68. http://dx.doi.org/10.1587/transcom.e93.b.1965.

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5

Park, Jonggyu, and Young Ik Eom. "URS: User-Based Resource Scheduling for Multi-User Surface Computing Systems." IEEE Transactions on Consumer Electronics 65, no. 3 (2019): 426–33. http://dx.doi.org/10.1109/tce.2019.2924480.

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6

Ni, Jun Hong, Yi Zhou, Zhen Dong Zhao, Peng Peng, and Rui Ju Xiao. "Scheduling Algorithm for High-Mobility User in LTE Downlink System." Applied Mechanics and Materials 644-650 (September 2014): 2143–46. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2143.

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Frequency diversity scheduling allocates whole system bandwidth to a user in order to conquer deep fading of the sub-band when user with high-mobility. Though OFDMA technology mitigates interference of users in same cell, it increases interference of cell edge user as base stations use same frequency. Thus, we developed a frequency diversity fairness scheduling algorithm to balance fairness of cell center user and cell edge user. It is demonstrated by computer simulation that the proposed algorithm increases user fairness of the system with same system throughput and fairness between cell edge

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7

Gao, Hui, Chau Yuen, Yuan Ren, Tiejun Lv, and Wei Long. "Distributed User Scheduling for MIMO-Y Channel." IEEE Transactions on Wireless Communications 14, no. 12 (2015): 7123–39. http://dx.doi.org/10.1109/twc.2015.2464806.

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8

Chen, Xiang, Feng-Kui Gong, Hang Zhang, and Guo Li. "Cooperative User Scheduling in Massive MIMO Systems." IEEE Access 6 (2018): 21910–23. http://dx.doi.org/10.1109/access.2018.2828403.

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9

Narbutt, M., A. Kelly, L. Murphy, and P. Perry. "Adaptive VoIP Playout Scheduling: Assessing User Satisfaction." IEEE Internet Computing 9, no. 4 (2005): 28–34. http://dx.doi.org/10.1109/mic.2005.72.

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10

Hang, Juan, and Feng She. "Vector Quantization Multi-User Scheduling in Broadcast Channel." Advanced Materials Research 461 (February 2012): 53–57. http://dx.doi.org/10.4028/www.scientific.net/amr.461.53.

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This paper deals with the research on user scheduling algorithm in downlink MIMO systems with limited feedback of channel state information (CSI). In this paper, we use vector quantization to simplify the procedure of user selection. By quantizing MIMO channel into several un-overlapped channel regions, we can get semi-orthogonal region sets with predefined threshold. Then, based on the semi-orthogonal region sets, we present a low complexity user scheduling algorithm. Simulation results show that the achieved sum rate by the presented algorithm can achieve nearly optimal sum rate while with m

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11

Kim, I.-Cheon, and Chung G. Kang. "Dynamic Feedback Selection Scheme for User Scheduling in Multi-user MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 40, no. 4 (2015): 646–52. http://dx.doi.org/10.7840/kics.2015.40.4.646.

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12

Ngoc Van, Nguyen. "A Low Complexity User Grouping Strategy for Downlink Multi-User MIMO Scheduling." International journal of Computer Networks & Communications 9, no. 5 (2017): 31–39. http://dx.doi.org/10.5121/ijcnc.2017.9503.

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13

Niu, Jinping, Daewon Lee, Tao Su, Geoffrey Y. Li, and Xiaofeng Ren. "User Classification and Scheduling in LTE Downlink Systems with Heterogeneous User Mobilities." IEEE Transactions on Wireless Communications 12, no. 12 (2013): 6205–13. http://dx.doi.org/10.1109/tw.2013.103113.130058.

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14

Ge, Xin, Xiuhua Li, Hu Jin, Julian Cheng, and Victor C. M. Leung. "Joint User Association and User Scheduling for Load Balancing in Heterogeneous Networks." IEEE Transactions on Wireless Communications 17, no. 5 (2018): 3211–25. http://dx.doi.org/10.1109/twc.2018.2808488.

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15

Wang, QunHuan, HuiMing Wang, and QinYe Yin. "Optimal user scheduling and power control in multi-user cognitive broadcast systems." Science China Information Sciences 55, no. 6 (2012): 1402–14. http://dx.doi.org/10.1007/s11432-012-4562-2.

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16

Zhu, Zhen Jia, and Yun Xia Liu. "Mass Data Efficient Scheduling Model with Cloud Computing on Android Platform." Applied Mechanics and Materials 644-650 (September 2014): 1817–21. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1817.

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The Android operating system is the most popular handheld devices operating system, the cloud computing is taken for processing large scale data efficiently, the commonly used scheduling technology of cloud computing needs to assign the user jobs to the reasonable resources for execution, the scheduling performance is not good. The mass data scheduling model is proposed based on application service quality limitation, and the cloud computing data scheduling principle is analyzed on the Android platform, the Android platform user requirements and the resources of node vectors are described, ind

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17

Suresh, P., and P. Balasubramanie. "User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/439362.

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Grid computing is a collection of computational and data resources, providing the means to support both computational intensive applications and data intensive applications. In order to improve the overall performance and efficient utilization of the resources, an efficient load balanced scheduling algorithm has to be implemented. The scheduling approach also needs to consider user demand to improve user satisfaction. This paper proposes a dynamic hierarchical load balancing approach which considers load of each resource and performs load balancing. It minimizes the response time of the jobs a

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18

Tang, Dong, and Ming Xia Lv. "A Fast Scheduling Algorithm for Multiple User MIMO-MRC System." Applied Mechanics and Materials 385-386 (August 2013): 1738–42. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1738.

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When multiple users share the same channel at one time, the total throughput of the communication system can be maximized by allocating the common radio resource to the user or the user group having the best channel quality at a given time and the multiuser diversity gain can be obtained. The object to select the users in the best group is to select the users with the maximum sum capacity. Because of the co-channel interferences among the users, user in the best group is often not the user with the best channel quality when only does it transmit to the base station. As for a scheduling algorit

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19

Zan, Chen. "A Distributed Distribution and Scheduling Algorithm of Educational Resources Based on Vector Space Model." International Journal of Emerging Technologies in Learning (iJET) 14, no. 04 (2019): 58. http://dx.doi.org/10.3991/ijet.v14i04.10132.

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To explore the distributed dispatching and scheduling algorithm of educational resources, based on the vector space model, the user and resource description model is built. In addition, the user interest representation method based on the background and tense vector space model (BTVSM) is proposed, and the distributed architecture based on the scheduling server is used to provide the personalized distribution service to the resource users. At the same time, the task scheduling model and algorithm are analyzed. The task scheduling optimization based on ant colony algorithm is mainly used in the

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20

Yuan, Li Li, and Lin Liu. "The Optimization for Management Model of Directly Transfer User in Regional Power Grid." Advanced Materials Research 1049-1050 (October 2014): 609–12. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.609.

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This paper analyzes the direct transfer of user management problems in the existing schedule management, analysis how to optimize the current large regional power grid management model for the user’s scheduling, establish the effective management of large user scheduling system.

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21

Kim, Tae-Kyoung. "User scheduling and grouping in massive MIMO broadcast channels with heterogeneous users." Journal of Communications and Networks 21, no. 4 (2019): 385–94. http://dx.doi.org/10.1109/jcn.2019.000028.

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22

Kim, Taehyoung, and Sangjoon Park. "Scaling Laws of Scheduling Gain for Uplink Massive MIMO Systems: Is User Scheduling Still Beneficial for Massive MIMO?" Electronics 9, no. 10 (2020): 1650. http://dx.doi.org/10.3390/electronics9101650.

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In this paper, the scaling laws of scheduling gain and the feasibility of user scheduling for uplink massive multiple input–multiple output (MIMO) systems are investigated by analyzing the second moment of mutual information. We consider two well-known linear receivers of matched filter (MF) and zero-forcing (ZF). The exact distribution of the signal-to-interference-plus-noise ratio (SINR) and its moment-generating function are first obtained, and the approximated variance of the mutual information for a user is derived as a closed form with a function of the number of antennas. The achievable

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23

SUN, H., X. WANG, and X. YOU. "A Novel User Scheduling Algorithm in Inhomogeneous Networks." IEICE Transactions on Communications E91-B, no. 3 (2008): 918–21. http://dx.doi.org/10.1093/ietcom/e91-b.3.918.

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24

Andrienko, Gennady, Natalia Andrienko, and Ulrich Bartling. "Visual Analytics Approach to User-Controlled Evacuation Scheduling." Information Visualization 7, no. 1 (2008): 89–103. http://dx.doi.org/10.1057/palgrave.ivs.9500174.

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Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster-affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool

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25

Shaw, K. J., and P. J. Fleming. "Genetic algorithms for scheduling: incorporation of user preferences." Transactions of the Institute of Measurement and Control 22, no. 2 (2000): 195–210. http://dx.doi.org/10.1177/014233120002200205.

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26

Kappes, Ronald, Alfred Romito, and Arnold Strashun. "A USER FRIENDLY SCHEDULING PROGRAM WRITTEN IN dBASE." CLINICAL NUCLEAR MEDICINE 21, no. 4 (1996): 348. http://dx.doi.org/10.1097/00003072-199604000-00033.

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27

Anton-Haro, C., P. Svedman, M. Bengtsson, A. Alexiou, and A. Gameiro. "Cross-layer scheduling for multi-user MIMO systems." IEEE Communications Magazine 44, no. 9 (2006): 39–45. http://dx.doi.org/10.1109/mcom.2006.1705977.

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28

Shaw, K. J., and P. J. Fleming. "Genetic algorithms for scheduling: incorporation of user preferences." Transactions of the Institute of Measurement and Control 22, no. 2 (2000): 195–210. http://dx.doi.org/10.1191/014233100672192658.

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29

Karaca, Mehmet, and Ozgur Ercetin. "Throughput Optimal Multi-User Scheduling via Hierarchical Modulation." IEEE Wireless Communications Letters 1, no. 3 (2012): 245–48. http://dx.doi.org/10.1109/wcl.2012.033012.120008.

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30

Ming Hoe, Hui, and Md Pauzi Abdullah. "Smart home appliances scheduling considering user comfort level." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 593. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp593-601.

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<a name="_Hlk38174666"></a><span>Smart home appliances scheduling, employing optimization optimization algorithms to reduce utility costs, is gaining traction under the introduction of time-of-use tariffs and the development of Internet of Things (IoT). The prior electricity cost reduction scheduling algorithms, however, causes substantial discomfort to users for restricting users from using the appliances at their desired times. To address the problem, a novel versatile systematic method is proposed by pricing the mismatch of proposed schedule with users’ usage preference pa

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31

Markovic, Nikola, Daniel Nemirovsky, Osman Unsal, Mateo Valero, and Adrian Cristal. "Kernel-to-User-Mode Transition-Aware Hardware Scheduling." IEEE Micro 35, no. 4 (2015): 37–47. http://dx.doi.org/10.1109/mm.2015.80.

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32

Ng, K. W., and K. A. Yeung. "Analysis on disk scheduling for special user functions." IEEE Transactions on Circuits and Systems for Video Technology 9, no. 5 (1999): 752–65. http://dx.doi.org/10.1109/76.780364.

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33

Espinosa-Aranda, José Luis, Ricardo García-Ródenas, María del Carmen Ramírez-Flores, María Luz López-García, and Eusebio Angulo. "High-speed railway scheduling based on user preferences." European Journal of Operational Research 246, no. 3 (2015): 772–86. http://dx.doi.org/10.1016/j.ejor.2015.05.052.

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34

Ding, Ding, Xiaocong Fan, and Siwei Luo. "User-oriented cloud resource scheduling with feedback integration." Journal of Supercomputing 72, no. 8 (2015): 3114–35. http://dx.doi.org/10.1007/s11227-015-1530-9.

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35

Lee, Gilwon, and Youngchul Sung. "A New Approach to User Scheduling in Massive Multi-User MIMO Broadcast Channels." IEEE Transactions on Communications 66, no. 4 (2018): 1481–95. http://dx.doi.org/10.1109/tcomm.2017.2786670.

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36

Liu, Yang, Jun Xuan Wang, Peng Wang, and Ren Kai Yu. "Joint Scheduling and Precoding Based on SLNR Criteria in MU-MIMO System." Applied Mechanics and Materials 668-669 (October 2014): 1273–77. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1273.

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The user scheduling and precoding schemes in Multi-User Multiple Input Multiple Output (MU-MIMO) system have the problem of high complexity and the performance of traditional criteria is not good. This paper analysis the advantages of Signal to Leakage plus Noise Ratio (SLNR) criteria firstly, then propose a cross-layer design of user scheduling and precoding scheme based on SLNR criteria. The design only uses SLNR criteria and includes an improved SLNR user scheduling scheme which is accomplished by iteration procedure. Numerical results verify the improved user scheduling scheme can improve

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37

Ali, Ahmed A., Rosdiadee Nordin, Mahamod Ismail, and Huda Abdullah. "Impact of Feedback Channel Delay over Joint User Scheduling Scheme and Separated Random User Scheduling Scheme in LTE-A System with Carrier Aggregation." Journal of Computer Networks and Communications 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/926424.

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In Long Term Evolution-Advanced (LTE-A), the signal quality in a wireless channel is estimated based on the channel quality measurements. The measurement results are used to select suitable modulation and coding scheme for each transmission, feedback, and processing delay, which can cause a mismatch between channel quality information (CQI) and current channel state at the base station. However, prospect delays in the reception of such CQI may lead to a system performance degradation. This study analyzes the impact of CQI feedback delay on joint user scheduling (JUS) scheme and separated rando

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38

Borst, Sem. "Flow-level performance and user mobility in wireless data networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1872 (2008): 2047–58. http://dx.doi.org/10.1098/rsta.2008.0025.

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Channel conditions in wireless networks exhibit huge variations across space and time, giving rise to vast fluctuations in the transmission rates. Channel-aware scheduling strategies provide an effective mechanism for improving throughput performance by exploiting such rate variations, and these have been extensively examined at the packet level for a static user configuration. In this paper, we discuss the performance implications at the flow level for a dynamic user population, taking into account variations on a slower time scale and wide-range user mobility as well. First of all, we presen

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39

Jiang, Jing, and Deting Kong. "Joint User Scheduling and MU-MIMO Hybrid Beamforming Algorithm for mmWave FDMA Massive MIMO System." International Journal of Antennas and Propagation 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4341068.

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The large bandwidth and multipath in millimeter wave (mmWave) cellular system assure the existence of frequency selective channels; it is necessary that mmWave system remains with frequency division multiple access (FDMA) and user scheduling. But for the hybrid beamforming system, the analog beamforming is implemented by the same phase shifts in the entire frequency band, and the wideband phase shifts may not be harmonious with all users scheduled in frequency resources. This paper proposes a joint user scheduling and multiuser hybrid beamforming algorithm for downlink massive multiple input m

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40

Anastasiou, E., Z. Tsiropoulos, S. Fountas, et al. "User requirements for a satellite-based advisory platform." Advances in Animal Biosciences 8, no. 2 (2017): 368–71. http://dx.doi.org/10.1017/s2040470017000942.

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APOLLO, a newly funded H2020 EU project will develop an agricultural advisory platform for small farmers based on Copernicus Sentinel satellites. It will provide services for tillage scheduling, irrigation scheduling, crop growth monitoring and yield estimation. The aim of this study was to identify the farmers’ requirements of the APOLLO platform. In total 121 farmers were interviewed in Spain, Serbia and Greece. More than 90% of the farmers pointed out that smart agriculture and use of satellite data in agriculture are important. Additionally, more than 80% want to have access to historical

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41

Keerthika, P., and N. Kasthuri. "An Efficient Grid Scheduling Algorithm with Fault Tolerance and User Satisfaction." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/340294.

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Problem Statement. The advances in human civilization lead to more complications in problem solving. Grid computing serves as an efficient technology in solving those complicated problems. In computational grids, the grid scheduler schedules the task and finds the appropriate resource for each task. The scheduler must consider several factors such as user demand, communication time, failure handling mechanisms, and reduced makespan. Most of the existing algorithms do not consider user satisfaction. Thus a scheduling algorithm that handles failure of resources and achieves user satisfaction gai

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42

Wang, Hongyu, Weixiao Meng, and Trungtan Nguyen. "User Fairness Scheme with Proportional Fair Scheduling in Multi-user MIMO Limited Feedback System." Communications and Network 05, no. 03 (2013): 113–18. http://dx.doi.org/10.4236/cn.2013.53b2022.

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43

Razi, Adeel, Daniel Ryan, Iain Collings, and Jinhong Yuan. "Sum rates, rate allocation, and user scheduling for multi-user MIMO vector perturbation precoding." IEEE Transactions on Wireless Communications 9, no. 1 (2010): 356–65. http://dx.doi.org/10.1109/twc.2010.01.090649.

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44

Rigi, Mohammad Amin, and Farid Khoshalhan. "Eliciting User Preferences in Multi-Agent Meeting Scheduling Problem." International Journal of Intelligent Information Technologies 7, no. 2 (2011): 45–62. http://dx.doi.org/10.4018/jiit.2011040103.

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Meeting Scheduling Problem (MSP) arranges meetings between a number of participants. Reaching consensus in arranging a meeting is very diffuclt and time-consuming when the number of participants is large. One efficient approach for overcoming this problem is the use of multi-agent systems. In a multi-agent system, agents are deciding on behalf of their users. They must be able to elicite their users’ preferences in an effective way. This paper focuses on the elicitation of users’ preferences. Analytical hierarchy process (AHP) - which is known for its ability to determine preferences - is used

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45

Chanthirasekaran, K., M. A. Bhagyaveni, and L. Rama Parvathy. "Multi-Parameter Based Scheduling for Multi-user MIMO Systems." Journal of Electrical Engineering and Technology 10, no. 6 (2015): 2406–12. http://dx.doi.org/10.5370/jeet.2015.10.6.2406.

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46

Park, Jong-Rok, and Sang-Hoon Lee. "Beamforming Matrix Transformation and User Scheduling for MIMO Systems." Journal of Korea Information and Communications Society 37, no. 1A (2012): 25–33. http://dx.doi.org/10.7840/kics.2012.37a.1.25.

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47

Yu Liping, Yang Lishen, and Zhu Liang. "Multi User Scheduling Strategy Research based on Mapreduce Cluster." Journal of Convergence Information Technology 8, no. 10 (2013): 1159–66. http://dx.doi.org/10.4156/jcit.vol8.issue10.140.

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48

KIM, Jaehong, Sangjae LEE, and Sehun KIM. "An Adjustable Scheduling Algorithm for Multi-User MIMO Systems." IEICE Transactions on Communications E92-B, no. 2 (2009): 527–32. http://dx.doi.org/10.1587/transcom.e92.b.527.

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49

Alvi, Sheeraz A., Xiangyun Zhou, Salman Durrani, and Duy Trong Ngo. "Sequencing and Scheduling for Multi-User Machine-Type Communication." IEEE Transactions on Communications 68, no. 4 (2020): 2459–73. http://dx.doi.org/10.1109/tcomm.2020.2967745.

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50

Hossain, Md, Mohamed-slim Alouini, and Vijay Bhargava. "Rate Adaptive Hierarchical Modulation-Assisted Two-User Opportunistic Scheduling." IEEE Transactions on Wireless Communications 6, no. 6 (2007): 2076–85. http://dx.doi.org/10.1109/twc.2007.05406.

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