On time diversity for packet channels
Computer Communications
Internet traffic modeling by means of Hidden Markov Models
Computer Networks: The International Journal of Computer and Telecommunications Networking
An HMM Approach to Anonymity Analysis of Continuous Mixes
Advanced Web and NetworkTechnologies, and Applications
A delay monitoring method for up-link flows in IEEE 802.11e EDCA networks
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Internet traffic source based on hidden Markov model
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
| Hi-index | 35.68 |
Performance of real-time applications on network communication channels is strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and exhibit a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, an HMM is proposed that shows excellent performance in modeling typical channel behaviors in a set of real packet links. The system is trained with a modified version of the Expectation-Maximization (EM) algorithm. Hidden-state analysis shows how the state variables characterize channel dynamics. State-sequence estimation is obtained by the use of Viterbi algorithm. Real-time modeling of the channel is the first step to implement adaptive communication strategies.