The increase in the volume of services and applications, in addition to the accelerated growth in wireless access demands, represent significant challenges for the fifth generation of mobile networks, the 5G network. This increase in the volume of applications is a reflection of the growing number of devices connecting to the network, consuming data expansively, generating high data load. Another point of great impact is the large-scale daily migration of people in urban centers, causing the so-called Tide Effect. This promotes the space-time fluctuation of traffic throughout the day, making it difficult to control and manage the network (low efficiency in the use of hardware resources, load imbalance, underutilization and idleness of resources). Based on these indications and considering the knowledge of the service operators, data from the movement of users in New York City were extracted through an LBSN (Location-Based Social Network). Considering the expected high traffic demand for 5G and the problems arising from the Tidal Effect, this dissertation proposes a heuristic with two approaches to provisioning hardware resources (one based on the aggregate throughput and the other on the number of connected users). The results show that the network provisioning met the traffic variability of the scenario used, reducing the Probability of Blocking by 3.7%, maximizing the efficiency of the Base Band Unit (BBU) and quantifying the Small Cells (SCs) necessary to meet user demand.
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