An hourly day-ahead Paris Metro Pricing scheme for mobile data networks

摘要

Since static pricing models (such as flat-rate or tiered-rate models) can not improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing (SDP) has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service (QoS) versus the prices that they are willing to pay. Even though PMP can not guarantee the actual QoS during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose a dynamic PMP scheme, so-called DPMP, which determines the prices and capacities of different classes for the next 24 hours. The prices should optimize the revenues and utilities for operators and subscribers, respectively. Our simulation results show that DPMP can better balance those two factors and determine the appropriate log period for operators.