Performance Analysis of Adaptive Location Update Schemes for Continuous Cell Zooming Algorithm in Wireless Networks

To reduce the transmitted power of base stations in mobile wireless networks, continuous cell zooming algorithm is a feasible dynamic cell zooming algorithm. In this algorithm, location management is required in order to know the locations of users. Movement-based Update is not compatible and the application of Convention Periodic Update (CPU) scheme in continuous cell zooming algorithm can lead to a high signaling cost. Thus, aiming to highlight the effectiveness of newly proposed location update schemes, Time-Adaptive Periodic Update (TAPU) and Location-Adaptive Periodic Update (LAPU), a simulation-based performance analysis is conducted. Applying in continuous cell zooming algorithm, the performances of TAPU and LAPU are compared to that of Convention Periodic Update (CPU) scheme in terms of transmitted power ratio, outage ratio and the number of update messages. The performances of TAPU and LAPU are analyzed in a network with different number of users and in a network with different average moving speeds of users. The results show that compared to CPU, both TAPU and LAPU have no significant effect on power saving capability of continuous cell zooming algorithm in every scenario. Meanwhile, LAPU and TAPU give a significant reduction of update messages in every scenario. In terms of QoS effect, LAPU gives approximately the same outage ratio as CPU and a higher outage ratio occurs in TAPU

Mobility Pattern Learning and Route Prediction Based Location Management in PCS Network

Mobile host (MH) has to be tracked in personal communication service (PCS) network, for which update and paging signals are required. The number of PCS network subscribers skyrocketed in recent years. To reuse channels over a distance, cell size is reduced and the number of cell crossing by user is becoming high. That makes optimal use of paging and update signal very important. In fact, most MH has unique movement profile, that contains the information of time, route, direction, etc., which is possible to learn and used to predict location. In this paper, we propose mobility pattern based location management scheme using the movement profile. Mobility pattern is learned and system will page only the restricted probable area. We compared the proposed scheme with distance-based location management. Improved cost saving is achieved