Estimation of the self-similarity parameter in linear fractional stable motion
Signal Processing - Signal processing with heavy-tailed models
Kalman filtering for self-similar processes
Signal Processing
| Hi-index | 35.68 |
Extensive studies indicate that traffic in high-speed communication networks exhibits long-range dependence (LRD) and impulsiveness, which pose new challenges in network engineering. While many models have appeared for capturing the traffic LRD, fewer models exist that account for impulsiveness as well as LRD. One of the few existing constructive models for network traffic is the celebrated on/off model or the alternating fractal renewal process (AFRP). However, although the AFRP results in aggregated traffic with LRD, it fails to capture impulsiveness, yielding traffic with Gaussian marginal distribution. A new constructive model, namely the extended AFRP (EAFRP), is proposed here, which overcomes the limitations of the AFRP model. We show that for both single-user and aggregated traffic, it results in impulsiveness and long-range dependence, the LRD being defined here in a generalized sense. We provide queueing analysis of the proposed model, which clearly demonstrates the implications of the impulsiveness in traffic engineering, and validate all theoretical findings based on real traffic data