论文标题
真实的熵还可以预测无线网络用户的每日语音流量
Real Entropy Can Also Predict Daily Voice Traffic for Wireless Network Users
论文作者
论文摘要
语音流量预测对于网络部署优化非常重要，因此可以提高网络效率。基于真实熵的理论结合和相应的预测模型已经证明了它们在移动性预测中的成功。在本文中，将基于熵的可预测性分析和预测模型引入语音流量预测中。对于此采用，提出并讨论了流量量化方法。根据现实世界的语音流量数据，介绍了N阶Markov模型的预测准确性，基于扩散的模型和MF模型，其中25阶Markov模型的性能最佳，并且接近最大可预测性。这项工作表明，真实的熵还可以很好地预测语音流量，从而扩大对基于熵的预测理论的理解。
Voice traffic prediction is significant for network deployment optimization thus to improve the network efficiency. The real entropy based theorectical bound and corresponding prediction models have demonstrated their success in mobility prediction. In this paper, the real entropy based predictability analysis and prediction models are introduced into voice traffic prediction. For this adoption, the traffic quantification methods is proposed and discussed. Based on the real world voice traffic data, the prediction accuracy of N-order Markov models, diffusion based model and MF model are presented, among which, 25-order Markov models performs best and approach close to the maximum predictability. This work demonstrates that, the real entropy can also predict voice traffic well which broaden the understanding on the real entropy based prediction theory.
