Two Methods of Adaptive Controlled Channel Resource Allocation using Reinforcement Learning and Supervised Learning Techniques

Author

Edward J. Wilmes
Kelvin T. Erickson, Missouri University of Science and TechnologyFollow

Abstract

Two methods of dynamic channel allocation for a cellular telephone network using neural networks are investigated. Both methods continuously optimize the mobile network based on changes in calling traffic. The first method uses backpropagation (supervised learning) model predictions to aid the channel allocator. The second method uses the same backpropagation models along with actor-critic (reinforcement learning) models to perform the channel allocation. A comparison shows that both methods significantly outperform fixed channel allocation, even when the call traffic activity deviates from the previously learned models of the call traffic activity.

Recommended Citation

E. J. Wilmes and K. T. Erickson, "Two Methods of Adaptive Controlled Channel Resource Allocation using Reinforcement Learning and Supervised Learning Techniques," Proceedings of the Artificial Neural Networks in Engineering Conference (1996, St. Louis, MO), vol. 6, pp. 613 - 618, American Society of Mechanical Engineers (ASME), Nov 1996.

Meeting Name

Artificial Neural Networks in Engineering Conference, ANNIE (1996: Nov. 10-13, St. Louis, MO)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive Control Systems; Backpropagation; Cellular Telephone Systems; Learning Systems; Neural Networks; Optimization; Supervised Learning; Resource Allocation

International Standard Book Number (ISBN)

0-7918-0051-2

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1996 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Nov 1996