Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Fenton:ieeeTCyB,
-
author = "Michael Fenton and David Lynch and Stepan Kucera and
Holger Claussen and Michael O'Neill",
-
title = "Multilayer Optimization of Heterogeneous Networks
Using Grammatical Genetic Programming",
-
journal = "IEEE Transactions on Cybernetics",
-
year = "2017",
-
volume = "47",
-
number = "9",
-
pages = "2938--2950",
-
month = sep,
-
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Evolutionary computation, wireless
communications networks",
-
ISSN = "2168-2267",
-
URL = "http://ieeexplore.ieee.org/abstract/document/7893786/",
-
DOI = "doi:10.1109/TCYB.2017.2688280",
-
size = "13 pages",
-
abstract = "Heterogeneous cellular networks are composed of macro
cells (MCs) and small cells (SCs) in which all cells
occupy the same bandwidth. Provision has been made
under the third generation partnership project-long
term evolution framework for enhanced intercell
interference coordination (eICIC) between cell tiers.
Expanding on previous works, this paper instruments
grammatical genetic programming to evolve control
heuristics for heterogeneous networks. Three aspects of
the eICIC framework are addressed including setting SC
powers and selection biases, MC duty cycles, and
scheduling of user equipments (UEs) at SCs. The evolved
heuristics yield minimum downlink rates three times
higher than a baseline method, and twice that of a
state-of-the-art benchmark. Furthermore, a greater
number of UEs receive transmissions under the proposed
scheme than in either the baseline or benchmark
cases.",
-
notes = "PonyGE2 Python",
- }
Genetic Programming entries for
Michael Fenton
David Lynch
Stepan Kucera
Holger Claussen
Michael O'Neill
Citations