Abstract
In recent years, mobile network data has an explosive growth. To adapt this demand and accelerate the development of new applications, the fifth generation of mobile communication networks emerged. At present, the vision and needs of 5G have been gradually clarified. How to integrate existing technologies and various potential new technologies to realize 5G network becomes the next research and development focus. In econometrics field, Granger causality test is a normal analysis tool for time series data based on autoregression, but it is not limited. It is also widely used based on the information theory conditional common information stage generalized Transfer Entropy (TE). In this paper, first Granger causality test is proposed on testing the correlation between two 5G channels, then transfer entropy algorithms is applied to forecast 5G channel coefficient. Then based on the forecasted channel, the energy allocation of the channel is performed by the Inverse Water Filling (IWF) algorithm. Finally, we demonstrate the high energy efficiency of the IWF on channel power allocation. The simulation further validates our theoretical results.
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Acknowledgements
The 5G Channel data used in this paper is provided by New York University Wireless Communication center open source. The work in this paper is funded in part by NSFC under Grants 61771342, 61731006, 61372097, and 61711530132.
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Chen, Z., Liang, Q. (2020). Efficient Energy Power Allocation for Forecasted Channel Based on Transfer Entropy. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_212
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DOI: https://doi.org/10.1007/978-981-13-9409-6_212
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