NDLI: Distributed Dynamic Call Admission Control and Channel Allocation Using SARSA

Content Provider	IEEE Xplore Digital Library
Author	Lilith, N. Dogangay, K.
Copyright Year	2005
Description	Author affiliation: Sch. of Electr. & Inf. Eng., South Australia Univ., The Levels, SA (Lilith, N.; Dogangay, K.)
Abstract	This paper introduces novel reinforcement learning agent-based solutions to the problems of call admission control (CAC) and dynamic channel allocation (DCA) in multi-cellular telecommunications environments featuring multi-class traffic and intercell handoffs. Both agents providing the CAC and DCA functionality make use of an on-policy reinforcement learning technique known as SARSA and are designed to be implemented at the cellular level in a distributed manner. Furthermore, both are capable of on-line learning without any initial training period. Both of the reinforcement learning agents are examined via computer simulations and are shown to provide superior results in terms of call blocking probabilities and revenue raised under a variety of traffic conditions
Starting Page	376
Ending Page	380
File Size	212074
Page Count	5
File Format	PDF
ISBN	0780391322
DOI	10.1109/APCC.2005.1554084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-05
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Call admission control Channel allocation Learning Lakes Australia Communication system traffic control Computer simulation Telecommunication traffic Traffic control Communication channels
Content Type	Text
Resource Type	Article

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