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Random Access Channel Management based Q-learning to Improve the Quality of M2M Communication
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Young-June Choi | - |
| dc.contributor.author | AGUSTA DANIEL ANTHONI KURNIA | - |
| dc.date.accessioned | 2019-04-01T16:41:08Z | - |
| dc.date.available | 2019-04-01T16:41:08Z | - |
| dc.date.issued | 2019-02 | - |
| dc.identifier.other | 28951 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/15012 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2019. 2 | - |
| dc.description.tableofcontents | 1 Introduction 1 1.1 Machine-to-Machine Communication 1 1.2 Random Access Management in M2M Network 2 1.3 Reinforcement Learning Approach 3 1.4 Thesis Contribution 4 1.5 Thesis Organization 4 2 Related Works: Random Access Management and Scheduling Technique in Machine-to-Machine Networks 6 2.1 Random Access and Resources Management 6 2.1.1 Random Access in Machine-to-Machine Devices 7 2.1.2 Traffic Overload 9 2.1.3 Access Delay 12 2.1.4 Resource Management 13 2.2 Scheduling in Machine-to-Machine Communication 16 2.2.1 The Awareness of Channel in Scheduling 16 2.2.2 Scheduling Depends on Delay 17 2.2.3 Group Scheduling based on Quality of Service Provisioning 19 2.3 Chapter Summary 21 3 Random Access Channel Management 22 3.1 Introduction 22 3.2 System Model and Problem Formulation 23 3.2.1. System Model 24 3.2.2. Random Access Success Probability Formulation 25 3.3 Random Access Management 26 3.3.1 Grouping and Leader Selection 26 3.3.2 Redundancy Check 28 3.3.3 Radio Resource Coordination 29 3.4 Performance Analysis and Results 30 3.4.1. Performance Analysis 30 3.4.2. Results 32 4. Slot Allocation 35 4.1. Slot Allocation without Interference 35 4.1.1 System Model 35 4.1.2 Problem Formulation 36 4.1.3 Proposed Solution 40 4.2. Slot Allocation with Interference 47 4.2.1. System Model 47 4.2.2. Proposed Solution 49 4.2.3. Results and Discussion 52 4.3 Chapter Summary 59 5. Conclusion and Future Work 61 5.1. Conclusion 61 5.2. Future Work 62 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Random Access Channel Management based Q-learning to Improve the Quality of M2M Communication | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2019. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 905293 | - |
| dc.identifier.uci | I804:41038-000000028951 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000028951 | - |
| dc.description.alternativeAbstract | Machine-to-machine communication becoming one of the research topics that get more attention these days. Machine-to-Machine itself can be define as an assortment technologies that being used to associate every systems with an aim to monitoring and control remotely without any intervention from human. One of the problem that may occur is the massive number of M2M devices that trying to attempt the resources in the same time. This condition may drives the network to have issues of overload and the random access. A deployment of a random access management and an efficient scheduling are needed to handle the issues of signaling overhead and the large gap of machine-to-machine devices requirement. In this dissertation, first we presented some related works about resource management and scheduling techniques that already exist as a base background of our works. Our works propose a random access management system that applies coordination scheme for Random Access Channel (RACH) that can fairly reduce the signaling congestion and network overload that affect to better performance of random access probability. The signaling process and available uplink resources will be handled efficiently with a group classification of related M2M devices and the leader selection, meanwhile the scheme also will preventing unnecessary recurring data transmission that can reducing the signaling overhead. This dissertation also present a slot allocation for scheduling M2M devices using Q-learning algorithm. Simulation results showed that the Q-learning algorithm can help maximizing the Quality of Service of M2M communication in terms of the better convergence time, success probability, and can ensure the interference that may happen on the M2M Networks. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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