NDLI: Merging CBR and Neural Networks for SLA-Based Radio Resource Management for QoS Sensitive Cellular Networks

Content Provider	IEEE Xplore Digital Library
Author	Khan, M.U. Ch, M.Q. Ahmad, H.F. Ali, L. Ali, A. Suguri, H.
Copyright Year	2007
Description	Author affiliation: Inst. of Inf. Technol., NUST, Rawalpindi (Khan, M.U.; Ch, M.Q.)
Abstract	We propose a radio resource management (RRM) approach to guarantee a predefined service level agreement (SLA) with different classes of users, for on-going and in-coming connections in QoS sensitive cellular networks. This approach is based on intelligent agent architecture which gives autonomy to radio network controller (RNC) or base station (BS) in accepting, rejecting or buffering a connection request to manage system capacity. Instead of simply blocking the connection request, different buffering times are allocated to different classes of users based on their SLA. This increases the chances of connection establishment and reduces the call blocking rate extensively. In this paper, we have described a merger of two AI (artificial intelligence) techniques, case based reasoning (CBR) and neural networks (NN). CBR is a library that stores previous successful decisions as cases and allows the agent to deal with current congestion situation efficiently. We have introduced a new concept of training neural network on the records of CBR library to avoid sequential search. In this way, an intelligent solution is proposed to control call blocking rate which is not only accurate but also time efficient
Starting Page	263
Ending Page	269
File Size	541683
Page Count	7
File Format	PDF
ISBN	076952804X
DOI	10.1109/ISADS.2007.48
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Merging Cellular neural networks Neural networks Resource management Land mobile radio cellular systems Artificial intelligence Artificial neural networks Libraries Intelligent agent Radio network
Content Type	Text
Resource Type	Article

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