Stochastic-Geometry-Based Performance Analysis of Delayed Mobile Data Offloading With Mobility Prediction in Dense IEEE 802.11 Networks

Mobile data offloading through Wi-Fi is a promising solution to alleviate the explosive data increase in cellular network. While extensive attempts have been made at mobile data offloading, previous studies have rarely paid attention to network characteristics (e.g. Wi-Fi deployment density) and its effect on overall mobile offloading performance. In this paper, we propose a stochastic geometry model for the performance analysis of delay-tolerant offloading with mobility prediction. We aim to understand how the network parameters, such as network size and channel condition, affect the long-term offloading potential in the dense Wi-Fi sitting. The deployment of Wi-Fi is modeled as an independent Poisson point process (PPP) to take the effect of interference and CSMA/CA-based medium access control protocol into account. Then the semi-Markov process is used to model the user's movement taking the sojourn time into account. Based on the PPP deployment and semi-Markov process, we can obtain the potential offloading traffic. Through the above proposed analytical studies, the network providers can easily obtain a rough estimation on the average offloading performance from a given dense network.

Abstract licence: CC BY-NC-ND

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