Performance analysis and enhancement of the next generation cellular networks

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 time-scaled 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.