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.

中文翻译：

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城市环境中大规模移动网络的时间相关定价：可行性和适应性

由于市区高峰时段网络拥塞严重，一些移动运营商提出动态时间相关定价，将用户的数据使用从高峰时段转移到非高峰时段。我们研究了包含一万个基站的大规模蜂窝网络的时间相关定价的性能。我们的调查揭示了两个重要的观察结果。首先，时间相关的定价在降低网络整体流量的峰均比方面表现良好。但是，当我们查看特定区域（例如办公区域）的基站时，网络使用的单一价格并没有取得良好的性能。其次，我们观察到位置是影响基站流量分布的另一个重要因素。所以，在设计定价策略时也应考虑位置信息。我们提出了一个框架，它结合了数据定价的空间和时间交通模式。我们对一万个基站的模拟表明，我们提出的方案能够实现平均减少 16% 的峰均比。办公区域一半以上基站的峰均比降低了 15% 以上，性能是最先进的时间相关数据定价系统的 2 倍。