Precise Location Prediction Algorithms Using Improved Random Walk-based and Generalized Markovian Mobility Models

The paper discusses about the precise location prediction algorithms using improved random walk-based and generalized Markovian mobility models. This algorithm is used in mobile and cellular network dimensioning, dynamic resource allocation in cells, justifying CAC decisions and QoS parameter tuning, predicting user distribution and motion drifts in network, and estimating number of users in current and adjacent cells. It presents the mobility modeling approaches, random walked model extension, proposition of a Markovian model with memory extension and accuracy measurement results. The said algorithm is most efficient in call admission control (CAC) approach or other QoS decisions.