Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks

Abstract

Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (q) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when q was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme.

Mobility Prediction using Artificial Neural Network in Mobile Communication Network

“…Various results have been proposed for mobility management schemes to minimize the total signalling cost, for example, zone-based [5], movement-based [9], distance-based [6], and TAL-based registration [3,4]. Among them, Jang and Baek [3] proposed a flexible TAL-forming registration method to reduce the total signal cost of automobile users in LTE advanced communication networks, using the direction of travel information in UE. In this method, the movement direction of the UE could be predicted based on a known geographic area of the trace, a map and movement statistics of the UE.…”

“…Experimental data used in the study [3] are used to develop neural network. Figure 3 shows the data used in this study, and shows the (regist, paging1, paging2, total1, total2, enterq) = (registration cost, onestep paging cost, twostep paging cost, total cost in onestep paging, total cost in twostep paging, q) for 10,000 mobile subscribers.…”

“…In addition, when the call arrives to the UE, through the paging process, the broadcast messages from the last TAL where UE were registered. So far, many studies have been proposed to reduce registration and paging costs [3,4,5,9]. Among them, it can be observed that the performance of the registration scheme using the moving probability of the UE is superior to that of the existing zonebased [5] or distance-based method [6].…”

“…Therefore, in this study, an artificial neural network model that predicts the movement direction of UEs using cost (number of signalling bits) values such as registration and paging cost is presented. In the proposed neural network model [2,8], previously used experimental data [3] are used to classify into learning and validation dataset and analyse the prediction performance of the proposed model.…”

Human Mobility Prediction with Calibration for Noisy Trajectories

“…Noise trajectories are often generated due to technical deviations from positioning devices and methods. Taking cellular mobility datasets [14] as an example, usually, a user connects to the cell tower closest to them, so the location of the cell tower to which the user is connected is used as an estimate of the user's location. When there is a load balancing or signal strength change in cell towers [15], even if the user is not moving, the recorded locations switch between two or more cell towers, which is called oscillation [16][17][18].…”