Paper Info
Title
Robust Cross-Layer Design with Reinforcement Learning for IEEE 802.11n Link Adaptation
Abstract
This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the wireless environment, the foresighted transmission strategy can be computed. The simulation results verify the proposed method and show that our proposed method can improve the goodput by 25% at most, compared with the MCS-oriented link adaptation method.
Year
DOI
Venue
2011
10.1109/icc.2011.5963257
ICC
Keywords
Field
DocType
ieee 802.11n link adaptation,modulation coding,reinforcement learning,phy layer,mac layer,learning (artificial intelligence),robust cross-layer design,mcs-oriented link adaptation method,modulation and coding scheme,access protocols,markov decision process,wireless lan,mdp model,markov processes,link adaptation,wireless communication,markov process,indexing terms,learning artificial intelligence,signal to noise ratio
Link adaptation,Markov process,Wireless,Computer science,Computer network,Markov decision process,Real-time computing,Physical layer,IEEE 802,Goodput,Reinforcement learning
Conference
ISSN
ISBN
Citations 
1550-3607 E-ISBN : 978-1-61284-231-8
978-1-61284-231-8
4
PageRank 
References 
Authors
0.57
6
1
Name
Order
Citations
PageRank
Kaijie Zhou1766.41