Journal Article

Dynamic backhaul resource allocation in wireless networks using artificial neural networks

Loumiotis, I., Adamopoulou, E., Demestichas, K., Sykas, E., Stamatiadi, T.

2013

Overview

The increasing bandwidth demand of end-users renders the need for efficient resource management more compelling in next generation wireless networks. In the present work, a novel scheme incorporating the deployment of an intelligent agent capable of monitoring, storing, and predicting the forthcoming needs for resources of a base station (BS) is proposed. In this way, the BS can in advance commit the necessary resources for its backhaul connection, guaranteeing the end-user's quality of service. The prediction process is performed using machine learning techniques.

• Publisher: The Institution of Engineering and Technology,Institution of Engineering and Technology
• Subject: Applied sciences / Artificial intelligence / artificial neural Networks / backhaul connection / base station / Computer science; control theory; systems / Computer systems and distributed systems. User interface / Connectionism. Neural networks / dynamic backhaul resource allocation / end‐user quality of service / Exact sciences and technology / Information and communication / intelligent agent / learning (artificial intelligence) / machine learning techniques / neural nets / next generation networks / next generation wireless networks / prediction process / quality of service / Radiocommunications / resource allocation / resource management / Software / telecommunication computing / telecommunication network management / Telecommunications / Telecommunications and information theory