Diurnal Mobility Prediction to Assist Context Aware RRM Sample Clauses

Diurnal Mobility Prediction to Assist Context Aware RRM. Mobility prediction plays an important role in designing of context aware radio resource management (RRM), which aims at providing uniform service quality. Knowledge of future user location (position, route or next cell) can be used to anticipate future data traffic conditions, future events (crowd formation, traffic jams etc.) [KKS+13] [KKS15] and appropriately reserve or manage resources to provide optimum service. The mobility prediction accuracy can be enhanced by using additional context information of user origin, destination. The mobility prediction can then be used in tandem with resource allocation to enable services like streaming media to be sustained even in coverage holes. To enable such mobility context awareness, additional context information, appropriate signaling and interfaces are required. These details along with algorithm are discussed in this technology component There are several works in literature focusing on mobility prediction such as, prediction based on distributed markov chains, hidden markov model based prediction [SWY+10], making use of neural networks and machine learning [LH05] etc. A majority of these works consider regular hexagonal cells and intend to predict the next cell for user transition based on different strategies. Further, they consider either straight line mobility of users or random way point mobility, to evaluate the performance of prediction schemes. Figure 5-12: Diurnal mobility example In real world scenarios, the user mobility is not random but is rather direction oriented, see Figure 5-12. The user direction relies on its origin and destination. Further, there are several users who exhibit similar mobility patterns on daily basis. They tend to regularly traverse a limited set of trajectories, comprising of specific landmarks. For instance, an office goer or commuter in public transport takes similar trajectories on regular basis. Such mobility can be referred to as Diurnal mobility, which constitutes a major portion of mobile users. In this work, users following diurnal mobility are considered and information arising from such mobility (E.g., origin, landmarks, destination) are used to enhance the accuracy of mobility prediction (next cell prediction, future route prediction). In simple Markov based prediction, at each cell statistics of number of times a user transited from celln to celln+1 is considered. Based on mobility statistics of the user over several business days, probability of transition in...