Resource Management in WCDMA Network using Learning

N. Yao and L. Cuthbert (UK)


WCDMA, case-based reasoning, matching, learning


This paper proposes a new method of balancing traffic load in mobile cellular networks by using Case-Based Reasoning (CBR) to learn traffic patterns at periods of congestion, the obtained traffic patterns then being used to control co-operating semi-smart antennas to optimise the radio coverage, hence minimising the effects of the congestion. Unlike previous work, this scheme does not require calculating the optimum patterns each time. The emphasis of this paper is to demonstrate case matching of congestion for CBR in CDMA networks, which represents the first, but perhaps the most challenging step, towards a resource management system using learning.

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