Reinforcement learning based Dynamic Network Self-optimization for heterogeneous networks

The coexistence of different heterogeneous Radio Access Technologies (RATs) is a significant feature of current wireless networks. Thus, it is important for network elements, such as the Base Stations (BSs) of cellular networks or access points (APs) of wireless local area networks (WLANs) to be rec...

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Bibliographic Details
Published in:2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing pp. 319 - 324
Main Authors: Feng, Zhiyong, Tan, Li, Li, Wei, Gulliver, T. Aaron
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2009
Subjects:
Femtocell networks
Heuristic algorithms
Intelligent agent
LAN interconnection
Land mobile radio cellular systems
Learning
Q-Learning
Radio access networks
Radio access technologies
Reconfigurable system
Reinforcement learning
Self-optimization
Wireless LAN
Wireless networks
ISBN:9781424445608, 1424445604
ISSN:1555-5798
Online Access:Get full text
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Summary:The coexistence of different heterogeneous Radio Access Technologies (RATs) is a significant feature of current wireless networks. Thus, it is important for network elements, such as the Base Stations (BSs) of cellular networks or access points (APs) of wireless local area networks (WLANs) to be reconfigurable according to the real-time network environment. This will enable interconnection between different networks. In this paper, we propose an efficient distributed reconfiguration algorithm: the Dynamic Network Self-optimization Algorithm (DNSA). This algorithm is based on the widely used Q-learning algorithm and regards the network self-optimization entity of each radio access network as an independent intelligent agent. Multiple agents perform the optimization cooperatively to reduce the system blocking rate and improve network revenue. In the proposed algorithm, the dynamic network self-optimization problem is transformed into a multi-agent reinforcement learning problem, which has much lower complexity and better performance.
ISBN:9781424445608
1424445604
ISSN:1555-5798
DOI:10.1109/PACRIM.2009.5291353