Mobility patterns: implications on network parameters and handover

Abstract

The purpose of a wireless network is to provide coverage to mobile users (MU). Knowledge about the pattern followed by MU in a given scenario may help network planning to guarantee service along the pathway followed by each user. MU’s movements have a noticeable impact on end-to-end delay, data packet delivery ratio, and other metrics related to QoS. Thus, it is really important that the mobility pattern mimics the movements of real users so to appropriately plan the network layout and allocate resources at each cell. Both trace-based models and synthetic models can be used. Despite the ability of the firsts to reflect real movements, they may be too specific for the environment from which they have been extracted. Moreover, sometimes traces are not available, as for example in new network environments, and it is necessary to use synthetic models. Synthetic models attempt to realistically represent the behaviors of MUs over time while providing a simplified algorithm that describes their movements. Despite the simplified and less realistic movement pattern generated, they capture enough of the key characteristics of human mobility to make protocol evaluation meaningful and easier. MANET simulation studies, published in a premiere conference for the MANET community between 2000 and 2005, have been analyzed. 66% of those studies involving mobility used the Random Waypoint (RWP) mobility model. Despite it has been criticized for not being representative of how humans actually move, nowadays it is still largely used in many studies. The RWP has been validated against real mobility data: with small changes to the distributions used in the RWP, it can be used as a good model for mobility in large geographic areas such as a city. The RWP mobility model has been deeply studied in the past, gaining understanding of the implications that its use may cause. Given a formal description of the RWP model, some key parameters of the model can be easily derived (i.e., the length and time of the movement between waypoints, the spatial distribution of nodes, the direction angle at a new waypoint, and the cell change rate). In the last years, our research aimed at better understanding user mobility and its implications on network parameters. The handover (HO) procedure is essential in cellular networks. The thorough understanding of the parameters related with the HO handling (e.g., statistics on the time spent by a mobile user in the same cell) is crucial for a better planning of the network. With this aim, several issues related with the HO have been investigated. Simulations have been performed in different scenarios and under different network layouts (i.e., minimum number of antennas for coverage vs. higher density of antennas for capacity constraints) and key statistics have been extracted. Trace data from the WLAN in our Campus have been also analyzed in order to gain understanding on the user behavior in real environments. Among other user behavior trends, results on the cell residence time in an academic environment are shown which can be compared to simulation results. Also, mobility trends inside classrooms and the library, together with the frequency of users’ connections have been analyzed. Taking advantage of the knowledge of the statistical properties of the RWP, the predictability of the HO in a given scenario has been recently investigated. An analytical framework has been proposed which can predict the next cell to which a user can move in the near future, provided that this user is moving according to the RWP mobility model. Interest in forecasting the cell to which a device may be handed off depending on the movement pattern is twofold. First, it gives insight into properties and statistics of the mobility model. Second, and from a more practical perspective, it is useful to manage resource allocation and reservation strategies in order to smooth the HO process, which then turns in an improvement in the QoS.