Understanding traffic dynamics in cellular data networks

By Utpal Paul, Anand Prabhu Subramanian, Milind Madhav Buddhikot, Samir R Das. 2011 Proceedings IEEE INFOCOM, 2011

449 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/766df29ab90d678c7d25cb8d08f3d66f6d501a69

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

1. Injong Rhee
2. Song Chong
3. Kyunghan Lee
4. Yung Yi
5. Moshe Zukerman
6. M. Shafiq
7. Lusheng Ji
8. Joohyung Lee

In Electrical and Electronic Engineering

1. Minggao Zhang
2. Injong Rhee
3. Song Chong
4. Yung Yi
5. Moshe Zukerman
6. Joohyung Lee
7. Haixia Zhang
8. Alex X. Liu

In Transportation

1. Song Chong
2. Kyunghan Lee
3. Injong Rhee

Citations in the corpus

• Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, background): “However, it is well understood that these influential factors are directly correlated to the generation of cellular traffic [5], [6].”
• A first look at cellular machine-to-machine traffic: large scale measurement and characterization (2012, result): “This observation is in accordance with the findings reported in prior literature [12,16].”
• Mobile Data Offloading: How Much Can WiFi Deliver? (2013, background): “It indicates that at any time, about 70% of users stay in a WiFi coverage area.”
• Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey (2015, background): “Ideally, the two theories would complement each other to obtain a more complete picture of probable blocking or dropping events in the network [21], [123], [124], [127].”