Application of Extreme Value Theory to the Analysis of Wireless Network Traffic

By Chunfeng Liu, Yantai Shu, Jiakun Liu, Oliver W. W. Yang. 2007

11 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Merouane Debbah
2. Walid Saad
3. Medhi Bennis
4. Alexandre Mouradian
5. Sumudu Samarakoon

In Management Science and Operations Research

1. Walid Saad
2. Medhi Bennis
3. Sumudu Samarakoon
4. Merouane Debbah
5. Alexandre Mouradian

In Statistics and Probability

1. Walid Saad
2. Sumudu Samarakoon
3. Merouane Debbah
4. Medhi Bennis
5. Alexandre Mouradian

Citations in the corpus

• Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “EVT asymptotically characterizes the statistics of extreme events by providing analytical models to analyze network traffic, worst-case delays, peak-to-average-power ratios, and ultra-reliable V2V communication in wireless systems [2], [13]–[15].”
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, methodology): “In [23], EVT is used to model the distributions of data rates exceeding a threshold for few traffic traces”
• Extreme value theory for the study of probabilistic worst case delays in wireless networks (2016, methodology): “In another study [23], the authors apply EVT to the study of traffic throughput in wireless networks and show that the generalized EVT distribution is a better match for large deviation prediction than exponential, gamma or lognormal distributions.”