Dynamic-cost-reward connection admission control for maximizing system reward in 4G wireless multihop relaying networks |
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Authors: | Ben-Jye Chang Ying-Hsin Liang Yu-Hsien Lee |
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Affiliation: | 1. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Taiwan, ROC;2. Department of Multimedia Animation and Application, Nan Kai University of Technology, Taiwan, ROC;3. Department of Information and Communication Engineering, Chaoyang University of Technology, Taiwan, ROC |
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Abstract: | The 3GPP Long Term Evolution (LTE) Advanced and IEEE 802.16j specifications adopt the mobile multi-hop relaying (MMR) mechanism for enlarging service area and improving wireless transmission quality simultaneously. By deploying different types of Relay Stations (RSs), MMR can bring some advantages: (1) the signal fading and wireless interference of a single long wireless link is improved obviously; (2) the ranges of wireless access and relay area are extended, etc. MMR can offer a high data rate transmission for packet services and can increase system capacity. Note that MMR can be applied to the public transportation system, e.g., equipped a mobile RS on a high-speed train. A mobile RS handoff initializes a multiple handoff requests of different types of traffics. It becomes as a critical handoff issue in 4G MMR. Thus, the MMR handoff needs a new efficient Connection Admission Control (CAC) to guarantee qualities for various types of traffics and to increase system revenue. However, traditional CACs are difficult to fulfill the objectives. This paper thus proposes the Dynamic Cost-Reward-based (DCR) CAC that consists of two key mechanisms: (1) adopting a Markov decision process-based (MDP) cost function and (2) providing different reward functions for different types of nodes and various types of connection. Additionally, a mathematical analytical Markov chain is modeled for DCR. The simulation results are very close to the analysis results, which justifies the correctness of the analytical model. Numerical results demonstrate that DCA outperforms the compared CACs in the probabilities of new blocking, MS-handoff, and RS-handoff dropping, FRL, GoS, and system reward. |
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Keywords: | MMR Dynamic CAC Markov chain model analysis LTE-Advanced IEEE 802 16j |
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