Human-Mobility Modeling for Cellular Networks

University essay from KTH/Kommunikationsnät; KTH/Kommunikationsnät

Author: Hatairatch Charoenkulvanich; Farnaz Fathali; [2013]

Keywords: ;


With the rapid growth of usage of mobile devices and their applications and the ever increasing use of these technologies, optimizing the performance of cellular networks becomes inevitable. Considering the fact that mobile devices are carried by humans, we can conclude that the performance of cellular networks depends on the characteristics of human mobility. Therefore, understanding the basic characteristics of human mobility and designing realistic models based on this understanding can help in optimizing the performance of cellular networks in terms of managing node or base station capacity, handling handoffs, paging, location updating, etc. In this work, we review the most important human mobility characteristics that are extracted from real human mobility traces. We then propose asynthetic model that can produce human traces; we verify the model to examine if it cancapture all the introduced characteristics. The model is designed as a graph in which nodes represent the visit-points and edges are considered as the roads between these visit-points. We focus on keeping the structure of the model close to reality following hierarchical traffic systems. The model is implemented in a simulator to be validated. The results show that our model does not capture all the characteristics as expected. To be precise, the model does not create truncated power-law flight lengths or truncated power-law radius of gyration. Our experiments, verifying our assumptions, show that the algorithms used for defining the area that the user can move within, and also choosing the next destinations, result in a sharing-area among users: the sharing-area is the common set of visit-points that all users usually choose to visit. The existence of this sharing-area is the reason that the results are not as expected. We suggest that for future work, it is interesting to improve the model by changing the way of the user-area selection and the next destination selection with consideration of distance together with visit-point weight.

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