vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff62df120000000f97020001000900 Machine learning schemes can be employed in cognitive radio systems to intelligently locate the spectrum holes with some knowledge about the operating environment. In this paper, we formulate a variation of Actor Critic Learning algorithm known as Continuous Actor Critic Learning Automaton (CACLA) and compare this scheme with Actor Critic Learning scheme and existing Q–learning scheme. Simulation results show that our CACLA scheme has lesser execution time and achieves higher throughput compared to other two schemes.
Dhananjay Kumar, Pavithra Hari, Panbhazhagi Selvaraj, Sharavanti Baskaran
Anna University, MIT Campus, Chennai, India
Markov Decision Process, Reinforcement Learning, Q–learning, Actor–critic Learning, CACLA
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 4 , Issue: 3 , Pages: 790 - 795 )
Date of Publication :
September 2013
Page Views :
218
Full Text Views :
3
|