Time Dependent Pricing for Large-Scale Mobile Networks of Urban Environment: Feasibility and Adaptability

Because of severe network congestion experienced during peak hours in 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 look at the performance of time-dependent pricing on a large scale cellular network comprising ten thousand base stations. Our investigation reveals two important observations. First, time-dependent pricing performs well in reducing the peak-average ratio of the overall traffic of the network. However, the single price used by the network does not achieve good performance when we look at base stations in specific regions, such as office regions. Second, we observe that location is another important factor that affects the traffic profile of a base station. Therefore, location information should be considered for designing a pricing strategy as well. We propose a framework that combines both spatial and temporal traffic patterns for data pricing. Our simulation on ten thousand base stations suggests that our proposed scheme is able to achieve an average of 16 percent smaller peak-to-average ratio. With over 15 percent smaller peak-to-average ratio of more than half of base stations in office regions, the performance is 2 better than that achieved by the state of the art time-dependent data pricing systems.