Abstract
Network traffic plays an important role in network management and network activities. It has an important impact on traffic engineering and network performance. However, we have larger difficulties in capturing and estimating them. This paper proposes a new estimating algorithm to forecast and model network traffic in time-frequency synchronization applications. Our approach is based on the normal regression theory. Firstly, normal regression theory is used to characterize and model network traffic. Secondly, the corresponding normal regression model is created to describe network traffic by finding the model parameters using the samples about network traffic. Finally, the estimation algorithm is proposed to predict network traffic in time-frequency synchronization applications. Simulation results indicate that our approach is effective.
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Li, H., Sun, F., Liu, Y., Ren, S., Nan, Y., Chen, C. (2021). A Traffic Prediction Algorithm Based on Converged Networks of LTE and Low Power Wide Area Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_6
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