研究生: |
Mimi Mereditha Tjiotijono Mimi Mereditha Tjiotijono |
---|---|
論文名稱: |
On Modeling Massive Machine Type Communications Traffic with Spatial Point Event On Modeling Massive Machine Type Communications Traffic with Spatial Point Event |
指導教授: |
鄭欣明
Shin-Ming Cheng |
口試委員: |
張世豪
Shih-Hao Chang 沈上翔 Shan-Hsiang Shen 黃琴雅 Chin-Ya Huang 鄭欣明 Shin-Ming Cheng |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 英文 |
論文頁數: | 42 |
中文關鍵詞: | mMTC 、Traffic Modeling 、Spatial Point Event 、5G 、PPP |
外文關鍵詞: | mMTC, Traffic Modeling, Spatial Point Event, 5G, PPP |
相關次數: | 點閱:371 下載:3 |
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Machine-type communication (MTC) has the essential role for supporting
in connecting the huge number of devices in the 5G systems, which is predicted
to be officially released in 2020. Massive MTC (mMTC) is the key
to solve a large number of devices as its low-cost energy consumption and
wide area coverage. Enabling the massive number of machine-type devices(
MTDs) to request services to the base stations (BSs) resulted in the
randomness of the random access mechanism. The position of MTDs, BSs,
and the events are modeled in Spatial Poisson Point Process (SPPP), as the
arrival of the events is based on the Poisson Arrival Process. Stochastic geometry
is used to capture the characterization of the mMTC over cellular
and events with enhanced access-barring class and random access through
the PPP. Then, we proposed the event-based traffic model by combining
the spatial point process and the poisson arrival process as the event model
(Spatial Point Event) with the parameters from the Markov chain. Hence,
we calculate the approximation of the expected traffic rate. The validation
of the traffic model is using simulation and compared to the previous traffic
model.
Machine-type communication (MTC) has the essential role for supporting
in connecting the huge number of devices in the 5G systems, which is predicted
to be officially released in 2020. Massive MTC (mMTC) is the key
to solve a large number of devices as its low-cost energy consumption and
wide area coverage. Enabling the massive number of machine-type devices(
MTDs) to request services to the base stations (BSs) resulted in the
randomness of the random access mechanism. The position of MTDs, BSs,
and the events are modeled in Spatial Poisson Point Process (SPPP), as the
arrival of the events is based on the Poisson Arrival Process. Stochastic geometry
is used to capture the characterization of the mMTC over cellular
and events with enhanced access-barring class and random access through
the PPP. Then, we proposed the event-based traffic model by combining
the spatial point process and the poisson arrival process as the event model
(Spatial Point Event) with the parameters from the Markov chain. Hence,
we calculate the approximation of the expected traffic rate. The validation
of the traffic model is using simulation and compared to the previous traffic
model.
Recommendation Letter . . . . . . . . . . . . . . . . . . . . . . . . i
Approval Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Research Interests in Modeling mMTC for 5G . . 4
1.2.2 Interesting Approach from Event Point of View . . 6
1.3 Thesis Contribution and Limitations . . . . . . . . . . . . 7
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . 8
2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Background of the Event-based Concept . . . . . . . . . . 10
2.2 Relation of Markov Chain to Voronoi Tesselation [1] . . . 12
vi
2.3 Recent Traffic Models . . . . . . . . . . . . . . . . . . . 14
2.3.1 Source Traffic Model (STM) and Aggregated Traffic
Model (ATM) . . . . . . . . . . . . . . . . . . 15
2.3.2 Coupled Markov Modulated Poisson Processes (CMMPP) [2] 17
2.3.3 Spatial Poisson Point Processes (SPPP) [3] . . . . 19
2.3.4 Spatio Temporal Traffic Model (STTM) [4] . . . . 20
3 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 Symbols in Thesis . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . 23
3.3 Event Scenario . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 Spatial Point Event (SPE) . . . . . . . . . . . . . . . . . . 28
3.5 Traffic Modeling . . . . . . . . . . . . . . . . . . . . . . 31
3.6 Performance Metrics . . . . . . . . . . . . . . . . . . . . 33
4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . 34
4.1 Parameters Used for Simulation . . . . . . . . . . . . . . 34
4.2 Expected Total Rates . . . . . . . . . . . . . . . . . . . . 35
4.2.1 Event Density (E) . . . . . . . . . . . . . . . . . 35
4.2.2 Number of Events (N) . . . . . . . . . . . . . . . 36
4.2.3 Comparison to the other traffic model . . . . . . . 38
vii
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 40
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
viii