PROJECT TITLE :
Time Dependent Pricing for Large-scale Mobile Networks of Urban Environment: Feasibility and Adaptability - 2017
ABSTRACT:
As a result of of severe network congestion experienced during peak hours within the urban area, dynamic time-dependent pricing has been proposed by some mobile operators to shift users’ data usage from peak hours to off-peak time slots. We have a tendency to take a look at the performance of your time-dependent pricing on a massive scale cellular network comprising 10 thousand base stations. Our investigation reveals 2 important observations. 1st, time-dependent pricing performs well in reducing the peak-average ratio of the traffic of the network. However, the single value employed by the network does not achieve good performance once we examine base stations in specific regions, like office regions. Second, we have a tendency to observe that location is another important issue that affects the traffic profile of a base station. So, location data ought to be thought of for designing a pricing strategy as well. We have a tendency to propose a framework that mixes each spatial and temporal traffic patterns for data pricing. Our simulation on 10 thousand base stations suggests that our proposed scheme is in a position to attain a mean of 16p.c smaller peak-to-average ratio. With over 15percent smaller peak-to-average ratio of a lot of than 0.5 of base stations in office regions, the performance is two better than that achieved by the state of the art time-dependent knowledge pricing systems.
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