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研究生:符傳傑
研究生(外文):Nuntanut Bhooanusas
論文名稱:利用無線網路協助行動數據分流之動態頻寬配置方法
論文名稱(外文):Dynamic Bandwidth Reallocation in Multipath Mobile Data Offloading to WiFi networks
指導教授:蘇淑茵蘇淑茵引用關係
指導教授(外文):Sou, Sok-Ian
口試委員:李忠憲蔡孟勳李彥文王友群
口試委員(外文):Li, Jung-ShianTsai, Meng-HsunLee, YinmanWang, You-Chiun
口試日期:2022-01-17
學位類別:博士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:130
外文關鍵詞:Pricing modelMobile Data offloadingBandwidth uncertaintyMultipath TCP (MPTCP)
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In the advanced technology era, smartphones play a central role in everyday life for connecting with people worldwide via the internet. Accordingly, there is a rapid growth in the data volume of cellular traffic every year. Mobile data offloading is a promising technique to combat this issue by offloading cellular traffic to a WiFi network. In multipath offloading, the simultaneous transmissions of cellular and WiFi network interfaces are enabled. Hence, the transmission delay using this technique is significantly reduced, and the offloading efficiency is improved accordingly. However, the selfish mobile operators may not agree to accept massive cellular data traffic to WiFi paths since this also induces traffic congestion over the WiFi networks.

Hence, a Satisfaction-based Dynamic Bandwidth Reallocation (SDBR) method is proposed to motivate the mobile operator to let WiFi APs participate in offloading activities. Based on this method, the mobile operator voluntarily utilizes WiFi APs to improve service such that offloading revenues will increase and the user satisfaction of the service will simultaneously increase. However, most studies on dynamic bandwidth allocation algorithms are conducted with a smart WiFi AP in which the bandwidth rate remains steady throughout the entire offloading period without any degradation. However, it is generally impossible in the real-world network to perform the offloading activity with the constant data rate until the completion, since the data transmission rate can vary in accordance with the physical network condition at that time. In other words, the offloading results obtained from studies in the past is intangible since it is not following the real-world offloading scenarios.

Accordingly, the present study further proposes an Incentive-Reward-based Dynamic Bandwidth Reallocation (IDBR) in Multipath data offloading with bandwidth uncertainty. Mobile users select a WiFi AP according to their money and service requirements. The WiFi connection model simulates the network environment of bandwidth uncertainty in terms of the unpredictable amount of WiFi bandwidth for allocation. The theoretical results confirm the validity of IDBR by comparing it with numerical simulations, and the effectiveness of the proposed IDBR is further investigated by comparing the performance metrics and QoS metrics of the offloading process with those obtained from the existing five bandwidth allocation methods. Overall, the results show that despite the bandwidth uncertainty, the proposed IDBR attains the goals of maximizing the revenues, reducing blocking probability, and maintaining the QoS levels. Finally, the performance and QoS results of IDBR are additionally evaluated under three different network conditions to confirm the feasibility that the present study can produce tangible offloading results approaching those in real-world scenarios.
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Research contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Research organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Satisfaction-based Bandwidth Reallocation Algorithm . . . . . . . . . . . . . . . 7
2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Satisfaction-based revenue calculation . . . . . . . . . . . . . . . . 10
2.2.2 Blocking probability . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 Workflow of SDBR scheme . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Detail of SDBR procedure . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Analysis on blocking probability . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5.1 Effect of user arrival rate, λµ . . . . . . . . . . . . . . . . . . . . . 23
2.5.2 Effect of expected WiFi connection time, E[t1] . . . . . . . . . . . 25
2.5.3 Effect of shape parameter b . . . . . . . . . . . . . . . . . . . . . . 26
2.5.4 Time-complexity comparison . . . . . . . . . . . . . . . . . . . . 28
2.6 Discussion and summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Performance Modeling of Mobile Data Offloading with Bandwidth Uncertainty . 31
3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.1 WiFi connection model . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.2 Deriving the distribution of tW . . . . . . . . . . . . . . . . . . . . 36
3.2.3 Deriving the distribution of ti w . . . . . . . . . . . . . . . . . . . . 40
3.2.4 Deriving the number of visits of State j . . . . . . . . . . . . . . . 42
3.2.5 Deriving the number of visits of States 1, 2 for m=2 . . . . . . . . . 43
3.2.6 Deriving the maximum offloading volume . . . . . . . . . . . . . . 45
3.2.7 Deriving the deadline miss ratio . . . . . . . . . . . . . . . . . . . 47
3.3 Model validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.1 CDF of WiFi connection time, tw . . . . . . . . . . . . . . . . . . 48
3.3.2 Validation of number of WiFi visits for state j . . . . . . . . . . . . 49
3.4 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.1 Effect of the expected value of WiFi connection time TC . . . . . . 53
3.4.2 Effect of the expected value of LTE bandwidth b1 . . . . . . . . . . 56
3.4.3 Effect of the expected ratio of TC to T0 . . . . . . . . . . . . . . . 58
3.5 Discussion and summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4 Incentive Reward Offloading Scenario in Wireless Network . . . . . . . . . . . . 62
4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.1 Incentive-based WiFi selection . . . . . . . . . . . . . . . . . . . . 66
4.2.2 Satisfaction-based revenue calculation . . . . . . . . . . . . . . . . 79
4.2.3 Blocking probability . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.3 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.3.1 Workflow of proposed IDBR method . . . . . . . . . . . . . . . . 80
4.3.2 Details of proposed IDBR procedure . . . . . . . . . . . . . . . . . 82
4.4 Computational complexity of proposed IDBR algorithm . . . . . . . . . . 91
4.5 Model validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.5.1 Validation of WiFi selection . . . . . . . . . . . . . . . . . . . . . 92
4.5.2 K-Measurement selection with non-incentive scenario . . . . . . . 93
4.5.3 K-Measurement selection with incentive-based scenario . . . . . . 96
4.5.4 Analysis on blocking probability . . . . . . . . . . . . . . . . . . . 99
4.6 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.6.1 Effect of arrival rate, λµ . . . . . . . . . . . . . . . . . . . . . . . 107
4.6.2 Effect of the ratio of the expected WiFi disconnection time, E[T0],
to the expected WiFi connection time, E[TC] . . . . . . . . . . . . 109
4.6.3 Effect of user budget amount, E[BUE] . . . . . . . . . . . . . . . . 111
4.6.4 Effect of the proportion of VIP users, zVIP, in IDBR . . . . . . . . . 113
4.6.5 Comparison of IDBR performances with bandwidth certainty and
uncertainty schemes . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.6.6 Time complexity comparison . . . . . . . . . . . . . . . . . . . . . 118
4.7 Discussion and summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.1 Future study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

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