Power Allocation in 5G Wireless Communication

Abstract

Granger Causality analysis originated in the field of econometrics is used as a time series analysis tool based on vector auto-regression, and its phased generalized transfer entropy (TE), which is based on conditional co-information in information theory, has been widely used in data analysis in recent years. In this paper, we forecast the Fifth-Generation (5G) channel based on the Granger causality and transfer entropy, then use the water filling algorithm to allocate power for the forecasted 5G channel. In the first part of the paper, we use the Granger causality test to verify the Granger causality correlation of two random 5G channels and ensure that the two channels can be forecasted using the Transfer Entropy method. In the second part, we use transfer entropy to forecast two channels and verify the accuracy of the forecasted channels using Root Mean Square Error (RMSE) and Cramer-Rao Lower Bound (CRLB). Finally, we use the Inverse Water-Filling (IWF) algorithm to perform the power allocation for the forecasted channels and compare it with the Equal Gain (EG) algorithm. The simulations further validate our theoretical results.