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UBC Theses and Dissertations

Stochastic geometry aided user mobility analysis in heterogeneous wireless networks Arshad, Rabe


Ultra-dense networks (UDNs) envision the massive deployment of heterogeneous base stations (BSs) and the integration of emerging technologies with the existing wireless networks to meet the desired traffic demands. For instance, in indoor environments that hold around 80% of the overall mobile traffic, integration of visible light communication (VLC) with existing radio frequency (RF) networks has emerged as a new network architecture to meet the rapidly growing traffic demand. In outdoor environments, harnessing unmanned aerial vehicles (UAVs) as flying BSs has helped to achieve a cost-effective and on-the-go wireless network that may be used in several scenarios such as to support disaster response and in temporary hotspots. While network densification offers capacity-per-area improvements, the reduced BS coverage footprints and the heterogeneous BS types result in challenges for user mobility such as frequent handovers. User mobility and the resulting handovers in such highly dense networks may in fact nullify the capacity gains foreseen through BS densification. Thus, there exists a need to quantify the effect of user mobility in UDNs. To this end, we conduct a user mobility analysis in emerging network architectures in indoor and outdoor environments (i.e., RF/VLC hybrid networks and UAVs assisted wireless networks) by deriving the user-to-BS association probabilities, handover rates, and sojourn time. The mathematical analysis makes use of stochastic geometry and modeling BSs' locations via a Poisson point process (PPP). First, we validate our mathematical model via Monte-carlo simulations. Then, we utilize it to quantify and reduce the effect of handover rates on the user rate experience. In addition, we exploit machine learning to make cell dwell time aware handover decisions and thus skip unnecessary handovers.

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