Reduced-State SARSA featuring extended channel reassignment for dynamic channel allocation in mobile cellular networks

Authors:
Nimrod Lilith;Kutluyıl Dogançay
Affiliations:
School of Electrical and Information Engineering, University of South Australia, Mawson Lakes, Australia;School of Electrical and Information Engineering, University of South Australia, Mawson Lakes, Australia
Venue:
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part II
Year:
2005

Citing 6
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Resource allocation in wireless networks

Journal of High Speed Networks - Special issue: wireless networks
Telecommunication System Engineering

Telecommunication System Engineering
Machine Learning

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a reinforcement learning solution to the problem of dynamic channel allocation for cellular telecommunication networks featuring either uniform or non-uniform offered traffic loads and call mobility. The performance of various dynamic channel allocation schemes are compared via extensive computer simulations, and it is shown that a reduced-state SARSA reinforcement learning algorithm can achieve superior new call and handoff blocking probabilities. A new reduced-state SARSA algorithm featuring an extended channel reassignment functionality and an initial table seeding is also demonstrated. The reduced-state SARSA incorporating the extended channel reassignment algorithm and table seeding is shown to produce superior new call and handoff blocking probabilities by way of computer simulations.