DocumentCode :
3122819
Title :
Enterprise femtocell network optimization based on neural network modeling
Author :
Li, Yizhe ; Feng, Zhiyong
Author_Institution :
Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2011
fDate :
9-12 Jan. 2011
Firstpage :
1130
Lastpage :
1131
Abstract :
In future B3G/4G communication systems, the special application of femtocell in the enterprise offices and public places has broad prospect. However, as the femtocell operation under such multi-femtocell environment is significantly different from that of usual residential femtocells, the femtocell configuration and optimization might be much more complex. One key issue is to find how will the femtocell network performance be influenced by the change of the femtocell access point´s (FAP´s) parameters (power, assigned channel, e.g.) in the multi-FAPs environment. This paper proposes a neural network (NN) modeling approach to approximate the relation between the femtocell network performance and the FAPs´ operating parameters. Simulation results show that the proposed model works well and is very close to the actual situation.
Keywords :
3G mobile communication; 4G mobile communication; femtocellular radio; neural nets; optimisation; performance evaluation; telecommunication computing; B3G/4G communication systems; FAP operating parameters; FAP parameters; NN modeling approach; enterprise femtocell network optimization; enterprise offices; femtocell access point parameters; femtocell configuration; femtocell network performance; femtocell operation; multiFAPs environment; multifemtocell environment; neural network modeling; public places; residential femtocells; Artificial neural networks; Femtocell networks; Interference; Macrocell networks; Optimization; Throughput; Wireless communication; FAP´s parameters; enterprise femtocell; femtocell network performance; neural network modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Communications and Networking Conference (CCNC), 2011 IEEE
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-8789-9
Type :
conf
DOI :
10.1109/CCNC.2011.5766352
Filename :
5766352
Link To Document :
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