Browsing by Author "Jeng, Jeu-Yih"

Now showing 1 - 4 of 4

Modeling credit reservation procedure for UMTS online charging system
(2007-11-01)
Son, Sok-Ian
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Hung, Hui-Nien
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Lin, Yi-Bing
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Peng, Nan-Fu
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Jeng, Jeu-Yih
The IP Multimedia Core Network Subsystem (IMS) provides real-time multimedia services for Universal Mobile Telecommunications System (UMTS). Through Recharge Threshold-based Credit Reservation (RTCR) mechanism, prepaid IMS services can be supported by the Online Charging System (OCS) in UMTS. In RTCR, when the remaining amount of prepaid credit is below a threshold, the OCS reminds the user to recharge the prepaid account. It is essential to choose an appropriate recharge threshold to reduce the probability that the in-progress service sessions are forced-terminated. An analytic model is developed to investigate the performance of RTCR for the OCS. Based on our study, the network operator can select the appropriate parameter values for various traffic conditions.
Modeling prepaid application server of VoIP and messaging services for UMTS
(2007-05-01)
Sou, Sok-Ian
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Lin, Yi-Bing
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Wu, Quincy
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Jeng, Jeu-Yih
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資訊工程學系
Universal mobile-telecommunication system supports IP multimedia services through the IP multimedia core-network subsystem (IMS). This paper proposes a prepaid application server (PAS) to handle both the prepaid calls and messaging services in the IMS. When both voice and messaging are simultaneously offered, a potential problem is that the delivery of a message during a call may result in force-termination of that call due to credit depletion. To address this issue, we describe a strategy to determine if a prepaid message can be sent out during a call session. We propose an analytic model to investigate the performance of this strategy. This paper provides guidelines to select appropriate input parameters for the PAS.
Reducing credit re-authorization cost in UMTS Online Charging System
(2008-09-01)
Sou, Sok-Ian
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Lin, Yi-Bing
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Jeng, Jeu-Yih
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資訊工程學系
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Department of Computer Science
During an online charging General Packet Radio Service (GPRS) session, a number of mid-session events, such as changes of Quality of Service (QoS), could dynamically affect the rating of the in-progress service. When such events occur, the GPRS support node needs to re-authorize the granted credit units with the Online Charging System (OCS). This paper proposes a threshold-based scheme that utilizes a threshold parameter 6 to reduce the signaling traffic for the credit re-authorization procedure. By selecting an appropriate 6 value, the signaling overhead in the OCS can be significantly reduced while the inaccuracy of the credit information insignificantly increases. The mobile operator can choose appropriate parameter values in the threshold-based scheme based on our study.
Time-Dependent Smart Data Pricing Based on Machine Learning
(2017-01-01)
Tsai, Yi-Chia
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Cheng, Yu-Da
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Wu, Cheng-Wei
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Lai, Yueh-Ting
;
Hu, Wan-Hsun
The purpose of time-dependent smart data pricing (abbreviated as TDP) is to relieve network congestion by offering network users different prices over varied periods. However, traditional TDP has not considered applying machine learning concepts in determining prices. In this paper, we propose a new framework for TDP based on machine learning concepts. We propose two different pricing algorithms, named TDP-TR (TDP based on Transition Rules) and TDP-KNN (TDP based on K-Nearest Neighbors). TDP-TR determines prices based on users' past willingness to pay given different prices, while TDP-KNN determines prices based on the similarity of users' past network usages. The main merit of TDP-TR is low computational cost, while that of TDP-KNN is low maintenance cost. Experimental results on simulated datasets show that the proposed algorithms have good performance and profitability.