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Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers. [ABSTRACT FROM AUTHOR]