A Markov-Based Channel Model Algorithm for Wireless Networks
By A. Konrad, Ben Y. Zhao, Anthony D. Joseph, R. Ludwig. International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2001
314 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/f58d47724ec8f552352c899aa6d1655c63f446d2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Sanjit Biswas
- Robert Morris
- John C. Bicket
- Daniel Aguayo
- Ananthram Swami
- Anima Anandkumar
- Qi Zhang
- Gerhard Haßlinger
In Electrical and Electronic Engineering
- Ananthram Swami
- Jieyu Lin
- Anima Anandkumar
- Qi Zhang
- Sanjit Biswas
- Robert Morris
- Gerhard Haßlinger
- John C. Bicket
Citations in the corpus
- Variational Approach for Learning Markov Processes from Time Series Data (2017, background): “Examples include molecular dynamics [9, 41], wireless communications [24, 27] and fluid dynamics [32, 15].”
- Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret (2010, background): “ognitive systems. Remark: We note some of the shortcomings of our approach. The i.i.d. model1 for primary transmissions is indeed idealistic and in practice, a Markovian model may be more appropriate [5], [6]. However, the i.i.d. model is a good approximation if the time slots for transmissions are ”
- The Gilbert-Elliott Model for Packet Loss in Real Time Services on the Internet (2011, methodology): “The McDougall model and the Markov-based Trace Analysis (MTA) [26] outperformed the Gilbert model, as it was unable to reproduce the variance in burst error lengths.”
- Succinct and Robust Multi-Agent Communication With Temporal Message Control (2020, background): “Moreover, packet loss over wireless channels typically reveals strong temporal correlations and occurs in a bursty fashion [34], which motivates the multi-state Markov model for simulating the loss pattern [35, 16, 40].”
- Link-level measurements from an 802.11b mesh network (2004, methodology): “In general, multi-state Markov models such as the Gilbert Model and those developed in [9] are found to fit loss patterns induced by movement well, but are over-specified for static environments.”