The impact of imperfect scheduling on cross-Layer congestion control in wireless networks
By Xiaojun Lin, Ness B. Shroff. IEEE/ACM Transactions on Networking, 2006
364 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a438253fe5c8c4e89e17e96215859cc54e53ca0f
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
- Ness B. Shroff
- Rayadurgam Srikant
- Xiaojun Lin
- Changhee Joo
- Sanjay Shakkottai
- Tutorial Paper
- Mung Chiang
- Michael J. Neely
In Electrical and Electronic Engineering
- Rayadurgam Srikant
- Ness B. Shroff
- Xiaojun Lin
- Tutorial Paper
- Sanjay Shakkottai
- Changhee Joo
- Mung Chiang
- Michael J. Neely
Citations in the corpus
- A tutorial on cross-layer optimization in wireless networks (2006, methodology): “Channel variations: Both formulations can be generalized to the case with channel variations (e.g., due to fading and/or mobility of the nodes), in which case only the scheduling component needs to be changed [30] , [33], [40].”
- FlashLinQ: A Synchronous Distributed Scheduler for Peer-to-Peer Ad Hoc Networks (2013, methodology): “the potential throughput loss suffered by a class of maximal matching distributed scheduling algorithms, as compared to a genie-aided centralized algorithm [9][10][11].”
- Understanding the Capacity Region of the Greedy Maximal Scheduling Algorithm in Multihop Wireless Networks (2008, methodology): “In this paper, we are interested in a well-known suboptimal scheduling policy called the greedy maximal scheduling (GMS) [2], [15] (also known as longest queue first (LQF) in [16], [17]), which determines a schedule by choosing links in a decreasing order of the backlog while conforming to interfere”