NDLI: Stochastic learning automata based resource allocation for LTE-advanced heterogeneous networks

Content Provider	IEEE Xplore Digital Library
Author	Zanyu Chen Tsungnan Lin
Copyright Year	2013
Description	Author affiliation: Grad. Inst. of Commun. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Zanyu Chen; Tsungnan Lin)
Abstract	A heterogeneous network (HetNet) contains macrocells and different small cells, such as picocells, femtocells, and relay nodes. In HetNets, small cells are used to increase network capacity. Femtocells are usually used to increase coverage range in indoor environment, and can be deployed by users arbitrary. Besides, carrier aggregation is a major improvement in Long-Term-Evolution-Advanced (LTE-Advanced), and component carriers (CCs) are the basic aggregated units which are shared among cells. Therefore, cells use the same CCs lead inter-cell interference among them. In this work, we propose a resource allocation algorithm in LTE-Advanced HetNet called ”Stochastic Automata component Carrier Selection” (SACS) based on stochastic learning automata. SACS is a fully-distributed and self-optimizing algorithm, and it doesn't any information exchange among cells. SACS has some good properties:(i) energy saving, (ii) no information exchange, and (iii) low complexity. From the simulation results, SACS has fast convergent time and saves more energy than others.
Starting Page	1952
Ending Page	1956
File Size	233391
Page Count	5
File Format	PDF
ISBN	9781467362351
ISSN	21669570
DOI	10.1109/PIMRC.2013.6666463
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Femtocells Power demand Learning automata Interference Macrocell networks Long Term Evolution Bandwidth LTE-advanced heterogeneous networks carrier aggregation energy saving stochastic learning automata
Content Type	Text
Resource Type	Article

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