A state action frequency approach to throughput maximization over uncertain wireless channels
Name
Modiano_A State Action.pdf
Size
141.56 KB
Format
Adobe PDF
Checksum (MD5)
225631244253b799eaa5abaaa634877b
Author(s)
Jagannathan, Krishna Prasanna
Mannor, Shie
Menache, Ishai
Modiano, Eytan H.
Date Issued
June 2011
Journal
Proceedings of the 30th IEEE International Conference on Computer Communications (IEEE INFOCOM 2011)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Jagannathan, Krishna et al. “A state action frequency approach to throughput maximization over uncertain wireless channels.” In Proceedings of the 30th IEEE International Conference on Computer Communications, INFOCOM 2011, IEEE, 2011. 491-495.
Version
Author's final manuscript
Abstract
We consider scheduling over a wireless system, where the channel state information is not available a priori to the scheduler, but can be inferred from the past. Specifically, the wireless system is modeled as a network of parallel queues. We assume that the channel state of each queue evolves stochastically as an ON/OFF Markov chain. The scheduler, which is aware of the queue lengths but is oblivious of the channel states, has to choose one queue at a time for transmission. The scheduler has no information regarding the current channel states, but can estimate them by using the acknowledgment history. We first characterize the capacity region of the system using tools from Markov Decision Processes (MDP) theory. Specifically, we prove that the capacity region boundary is the uniform limit of a sequence of Linear Programming (LP) solutions. Next, we combine the LP solution with a queue length based scheduling mechanism that operates over long `frames,' to obtain a throughput optimal policy for the system. By incorporating results from MDP theory within the Lyapunov-stability framework, we show that our frame-based policy stabilizes the system for all arrival rates that lie in the interior of the capacity region.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/INFCOM.2011.5935211
