Two Methods of Adaptive Controlled Channel Resource Allocation using Reinforcement Learning and Supervised Learning Techniques
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.
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.
Artificial Neural Networks in Engineering Conference, ANNIE (1996: Nov. 10-13, St. Louis, MO)
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)
Article - Conference proceedings
© 1996 American Society of Mechanical Engineers (ASME), All rights reserved.