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Content Provider | IEEE Xplore Digital Library |
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Author | Hai-tao Zhao Rui Zhang Rong-fang Song Hui Zhang Yu-ning Dong Feng Tian |
Copyright Year | 2011 |
Description | Author affiliation: College of Communication and Information Engineering/Nanjing University of Posts and Telecommunications, Nanjing, China (Hai-tao Zhao; Rui Zhang; Rong-fang Song; Hui Zhang; Yu-ning Dong; Feng Tian) |
Abstract | A model-based channel capacity prediction scheme is proposed to provide statistical quality-of-service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 based wireless multimedia networks. The proposed scheme perceives the state of wireless link from the MAC retransmission information and calculates the statistical channel capacity especially in saturated traffic load. Based on the capacity prediction model, resource reservation and QoS routing optimization are carried out. Via a cross-layer design approach, the scheme allocates network resource and forwards data packet by taking into consideration of the interference among flows and the link state. Simulation results show that the proposed scheme can achieve more accurate capacity prediction and higher resource utilization than previous QoS routing schemes, and improve network performance in terms of end to end transmission delay, packet delivery ratio and network throughput. |
Starting Page | 1 |
Ending Page | 5 |
File Size | 755025 |
Page Count | 5 |
File Format | |
ISBN | 9781457710094 |
e-ISBN | 9781457710100 |
e-ISBN | 9781457710087 |
DOI | 10.1109/WCSP.2011.6096792 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-11-09 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Wireless communication Channel capacity Wireless multi-hop networks Quality of service Predictive models Routing Ad hoc networks Cross-layer Capacity prediction Delay Probability statistics |
Content Type | Text |
Resource Type | Article |
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