JACIII Vol.22 No.7 pp. 1065-1070
doi: 10.20965/jaciii.2018.p1065


Control Technology for Power Resources Based on Improved Q Learning Algorithm for Automated Intelligent Control

Run Ma

School of Automation, Chongqing Industry Polytechnic College
No.1000 Taoyuan Road, Yubei District, Chongqing 401120, China

August 23, 2018
June 11, 2018
November 20, 2018
femtocell, power resource control, Q learning algorithm, intellectualization, message capacity

With the advancement in internet technologies, requirements for quality of indoor wireless communication have increased. Femtocell, which is an effective approach to improve indoor communication quality, can provide highly-efficient indoor network services for users. This study puts forward a power resource control method based on Q learning algorithm for improved solutions to the problems of frequency spectrum and power resource allocation of a two-tier femtocell network. The algorithm was further improved, and was compared with the traditional algorithm via a simulation experiment. It was found that the improved Q learning algorithm could enhance the message capacity and control power resource; this provides a reference for the application of Q learning algorithm in femtocell communication.

Tow-tier femtocell network structure

Tow-tier femtocell network structure

Cite this article as:
R. Ma, “Control Technology for Power Resources Based on Improved Q Learning Algorithm for Automated Intelligent Control,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.7, pp. 1065-1070, 2018.
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Last updated on Jun. 19, 2024