DYNAMIC LOCATION AREA PLANNING IN CELLULAR NETWORK USING FREQUENT PATTERN MINING

Abstract

Frequent pattern mining algorithms as the name says, mines sets frequent patterns form given datasets. These algorithms provide immensely helpful results which have a wide scope of application starting from simple decision problems to complex business intelligence aspects. This paper attempts to apply the same concept of frequent pattern mining to solve the location management problem of GSM networks. In GSM networks the task of keeping track of a mobile user (MU) and relaying an incoming call, is called location management. It basically includes two processes, location update and paging. Location update deals with managing the current location of the MUs. There are many approaches to do this like time based, movement based, distance based etc. In this paper the location update procedure relies on the collected data of user movements form one network cell to another cell. This data has a definite pattern, as in day to day life a person mostly has a fixed route of travelling e.g. home to office in the morning and office to home in the evening. During this movement he crosses a specific set of network cells which remains same throughout the week. Thus, frequent pattern mining algorithm can be applied on the user's mobility log and try to find out the most probable location where the mobile user could be found. Using the results, a dynamic location area for individual user's current location can be created. Thus minimizing cost related to location update which otherwise involves communication between the mobile handset and the base station, and calculations related to keeping track of the location of mobile users.