On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Heavy Tails and Long Range Dependence in On/Off Processes and Associated Fluid Models
Mathematics of Operations Research
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Bursty traffic over CDMA: predictive MAI temporal structure, rate control and admission control
Computer Networks: The International Journal of Computer and Telecommunications Networking
Queueing processes in GPS and PGPS with LRD traffic inputs
IEEE/ACM Transactions on Networking (TON)
Broadband traffic modeling: simple solutions to hard problems
IEEE Communications Magazine
A framework for uplink power control in cellular radio systems
IEEE Journal on Selected Areas in Communications
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As more and more wireless subscribers access the Internet through cellular networks, Internet data traffic, which is known to be long range dependent (LRD), will soon dominate the conventional voice traffic. In this paper, we study the impact of such LRD data traffic on the statistical characteristics of Multi-Access Interference (MAI) and Signal to Interference-plus-Noise Ratio (SINR) in a Code Division Multiple Access (CDMA) network. Through analysis and simulation, we show that the timescaled MAI and SINR have slow decaying tail distributions due to the LRD data traffic. As a result, the outage probability is larger for data users than that for voice users. To improve the performance of the CDMA network in the presence of LRD data traffic, we propose a variable period prediction scheme to predict MAI or the equivalent number of active users. We show that the proposed variable period prediction is not only more accurate for data users but also less memory-consuming than existing fixed period prediction. In addition, rate control based on variable period prediction can achieve lower outage probability and higher throughput for data users than that based on fixed period prediction.