• The KIPS Transactions:PartC
• /
• v.11C no.2
• /
• pp.193-202
• /
• 2004

Handoff if the most Important feature for the user's mobility in a cellular communication system, which is related to resource reservation at nearby cells. For efficient resource reservation, mobility prediction has been reported as an effective means to decrease call dropping probability and to shorten handoff latency in wireless cellular environments. Several early proposed handoff schemes making use of tile user's movement history on a cell-by-cell basis work on the assumption that the user's movements are restricted to the indoor locations such as an office or a building. However, those algorithms cannot be applied to a micro-cell structure or a metropolis with complicated structure of roads. In this paper, to overcome those drawbacks we propose a new mobility prediction algorithm, which stores and uses the history of the user's positions within the current cell to predict the next cell.

• The KIPS Transactions:PartC
• /
• v.11C no.5
• /
• pp.659-670
• /
• 2004

Handoff is one of the most important features for the user's mobility in a wireless cellular communication system. It is related to resource reservation at nearby cells. Resource reservation to the new connection point should occur prior to handoff to enable the user to receive the data or services at the new location, at the same level of service as at the previous location. For the efficient resource reservation, mobility prediction has been reported as an effective means to decrease the call dropping probability and to shorten the handoff latency in a wireless cellular environment. A recently proposed algorithm, ZMHB, makes use of the history of the user's positions within the current cell to predict the next cell. But, the prediction of the ZMHB algorithm is found to be 80∼85% accurate for regular and random movements. In this paper, we propose a new improved ZMHB mobility prediction algorithm, which is called Detailed-ZMHB that uses detailed-zone-based tracking of mo-bile users to predict user movements. The effectiveness of the proposed algorithm is then demonstrated through a simulation.