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