Performance Evaluation of Packet Mobile Communications through LevelCrossing Analysis
Wireless Personal Communications: An International Journal
WiMAX channel: PHY model in network simulator 2
WNS2 '06 Proceeding from the 2006 workshop on ns-2: the IP network simulator
Effective capacity channel model for frequency-selective fading channels
Wireless Networks
Cross-layer modeling of wireless channels for data-link and IP layer performance evaluation
Computer Communications
High quality, low delay foveated visual communications over mobile channels
Journal of Visual Communication and Image Representation
State description of wireless channels using change-point statistical tests
WWIC'06 Proceedings of the 4th international conference on Wired/Wireless Internet Communications
The structure of the reactive performance control system for wireless channels
NEW2AN'06 Proceedings of the 6th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
International Journal of Communication Systems
Effective capacity of a correlated Nakagami-m fading channel
Wireless Communications & Mobile Computing
Joint source and sending rate modeling in adaptive video streaming
Image Communication
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We identify important characteristics of a fading/shadowing channel and present the work of measurement-based channel modeling for packet-level network queueing analysis. Our integration of wireless channel modeling and data queueing analysis at the packet-level provides a unique approach to study the effect of various channel dynamics on high-layer network performance, which otherwise cannot be captured through the traditional bit-level physical-layer channel modeling. In our study, the channel statistics are decomposed into three frequency regions [i.e., low (LF), mid (MF), and high (HF)]; the statistics in each frequency region is found to have significantly different impact on the queueing performance. While the HF statistics can be largely ignored in channel modeling due to their negligible impact on queueing performance, the LF statistics play the most important role in channel modeling because of substantial impact on queueing performance. Since the shadowing mainly represents the LF behavior of a channel, its dynamics are found to have a dominant effect on network performance as compared to the effect of multipath fading dynamics. In wireless networks, there are many other system factors which may change the channel dynamics, such as mobile user driving patterns, and forward-error-correction (FEC) coding (fixed or adaptive) using automated repeat request (ARQ) scheme. Our study further examines the individual impact of these factors on the network performance. In the measurement-based channel modeling, we use a Markov chain modeling technique to match the important channel statistics for queueing system analysis. The study shows an excellent agreement in queueing solutions between using the real original channel traces and using the sequences generated by the matched Markov chain models