Reinforcement Learning for Control of Traffic and Access Points in Intelligent Wireless ATM Networks

Authors:
Jerzy Martyna
Affiliations:
-
Venue:
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Year:
2001

Citing 14
Cited 0

Learning to Perceive and Act by Trial and Error

Machine Learning
Automatic programming of behavior-based robots using reinforcement learning

Artificial Intelligence
Technical Note: \cal Q-Learning

Machine Learning
Reinforcement learning algorithms for average-payoff Markovian decision processes

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Reinforcement learning for call admission control and routing in integrated service networks

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Wireless Multimedia Communications: Networking Video, Voice and Data

Wireless Multimedia Communications: Networking Video, Voice and Data
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Wireless Intelligent Networking

Wireless Intelligent Networking
Learning to Predict by the Methods of Temporal Differences

Machine Learning
Call Admissibility for Multirate Traffic in Wireless ATM Networks

INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Dynamic Programming

Dynamic Programming
Temporal credit assignment in reinforcement learning

Temporal credit assignment in reinforcement learning
Call admission control and routing in integrated services networks using neuro-dynamic programming

IEEE Journal on Selected Areas in Communications
Adaptive call admission control under quality of service constraints: a reinforcement learning solution

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we introduce the scheme for bandwidth allocation and congestion avoidance in wireless intelligent ATM networks which is based on reinforcement learning (RL). Our solution guarantees a desired bandwidth to connections which require a fixed wide bandwidth according to QoS constraints. Any unused bandwidth is momentarily backed up (returned) to Virtual Circuits. Proposed RL method is also suitable for supporting the mapping between a single ATM switch port and a wireless access points. It can control the access points in wireless intelligent ATM networks.